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+title,firstAuthor,url,dateSubmitted,keywords,pdf_titles,abstract
+"""Do Anything Now"": Characterizing and Evaluating In-The-Wild Jailbreak  Prompts on Large Language Models",Xinyue Shen,http://arxiv.org/pdf/2308.03825v1.pdf,2023-08-07,"['cs.cr', 'cs.lg']",2308.03825v1.pdf,"  The misuse of large language models (LLMs) has garnered significant attention
+from the general public and LLM vendors. In response, efforts have been made to
+align LLMs with human values and intent use. However, a particular type of
+adversarial prompts, known as jailbreak prompt, has emerged and continuously
+evolved to bypass the safeguards and elicit harmful content from LLMs. In this
+paper, we conduct the first measurement study on jailbreak prompts in the wild,
+with 6,387 prompts collected from four platforms over six months. Leveraging
+natural language processing technologies and graph-based community detection
+methods, we discover unique characteristics of jailbreak prompts and their
+major attack strategies, such as prompt injection and privilege escalation. We
+also observe that jailbreak prompts increasingly shift from public platforms to
+private ones, posing new challenges for LLM vendors in proactive detection. To
+assess the potential harm caused by jailbreak prompts, we create a question set
+comprising 46,800 samples across 13 forbidden scenarios. Our experiments show
+that current LLMs and safeguards cannot adequately defend jailbreak prompts in
+all scenarios. Particularly, we identify two highly effective jailbreak prompts
+which achieve 0.99 attack success rates on ChatGPT (GPT-3.5) and GPT-4, and
+they have persisted online for over 100 days. Our work sheds light on the
+severe and evolving threat landscape of jailbreak prompts. We hope our study
+can facilitate the research community and LLM vendors in promoting safer and
+regulated LLMs.
+"
+Jailbreaking ChatGPT via Prompt Engineering: An Empirical Study,Yi Liu,http://arxiv.org/pdf/2305.13860v1.pdf,2023-05-23,"['cs.se', 'cs.ai', 'cs.cl']",2305.13860v1.pdf,"  Large Language Models (LLMs), like ChatGPT, have demonstrated vast potential
+but also introduce challenges related to content constraints and potential
+misuse. Our study investigates three key research questions: (1) the number of
+different prompt types that can jailbreak LLMs, (2) the effectiveness of
+jailbreak prompts in circumventing LLM constraints, and (3) the resilience of
+ChatGPT against these jailbreak prompts. Initially, we develop a classification
+model to analyze the distribution of existing prompts, identifying ten distinct
+patterns and three categories of jailbreak prompts. Subsequently, we assess the
+jailbreak capability of prompts with ChatGPT versions 3.5 and 4.0, utilizing a
+dataset of 3,120 jailbreak questions across eight prohibited scenarios.
+Finally, we evaluate the resistance of ChatGPT against jailbreak prompts,
+finding that the prompts can consistently evade the restrictions in 40 use-case
+scenarios. The study underscores the importance of prompt structures in
+jailbreaking LLMs and discusses the challenges of robust jailbreak prompt
+generation and prevention.
+"
+AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language  Models,Xiaogeng Liu,http://arxiv.org/pdf/2310.04451v1.pdf,2023-10-03,"['cs.cl', 'cs.ai']",2310.04451v1.pdf,"  The aligned Large Language Models (LLMs) are powerful language understanding
+and decision-making tools that are created through extensive alignment with
+human feedback. However, these large models remain susceptible to jailbreak
+attacks, where adversaries manipulate prompts to elicit malicious outputs that
+should not be given by aligned LLMs. Investigating jailbreak prompts can lead
+us to delve into the limitations of LLMs and further guide us to secure them.
+Unfortunately, existing jailbreak techniques suffer from either (1) scalability
+issues, where attacks heavily rely on manual crafting of prompts, or (2)
+stealthiness problems, as attacks depend on token-based algorithms to generate
+prompts that are often semantically meaningless, making them susceptible to
+detection through basic perplexity testing. In light of these challenges, we
+intend to answer this question: Can we develop an approach that can
+automatically generate stealthy jailbreak prompts? In this paper, we introduce
+AutoDAN, a novel jailbreak attack against aligned LLMs. AutoDAN can
+automatically generate stealthy jailbreak prompts by the carefully designed
+hierarchical genetic algorithm. Extensive evaluations demonstrate that AutoDAN
+not only automates the process while preserving semantic meaningfulness, but
+also demonstrates superior attack strength in cross-model transferability, and
+cross-sample universality compared with the baseline. Moreover, we also compare
+AutoDAN with perplexity-based defense methods and show that AutoDAN can bypass
+them effectively.
+"
+Defending Against Alignment-Breaking Attacks via Robustly Aligned LLM,Bochuan Cao,http://arxiv.org/pdf/2309.14348v1.pdf,2023-09-18,"['cs.cl', 'cs.ai', 'cs.cr', 'cs.lg']",2309.14348v1.pdf,"  Recently, Large Language Models (LLMs) have made significant advancements and
+are now widely used across various domains. Unfortunately, there has been a
+rising concern that LLMs can be misused to generate harmful or malicious
+content. Though a line of research has focused on aligning LLMs with human
+values and preventing them from producing inappropriate content, such
+alignments are usually vulnerable and can be bypassed by alignment-breaking
+attacks via adversarially optimized or handcrafted jailbreaking prompts. In
+this work, we introduce a Robustly Aligned LLM (RA-LLM) to defend against
+potential alignment-breaking attacks. RA-LLM can be directly constructed upon
+an existing aligned LLM with a robust alignment checking function, without
+requiring any expensive retraining or fine-tuning process of the original LLM.
+Furthermore, we also provide a theoretical analysis for RA-LLM to verify its
+effectiveness in defending against alignment-breaking attacks. Through
+real-world experiments on open-source large language models, we demonstrate
+that RA-LLM can successfully defend against both state-of-the-art adversarial
+prompts and popular handcrafted jailbreaking prompts by reducing their attack
+success rates from nearly 100\% to around 10\% or less.
+"
+FuzzLLM: A Novel and Universal Fuzzing Framework for Proactively  Discovering Jailbreak Vulnerabilities in Large Language Models,Dongyu Yao,http://arxiv.org/pdf/2309.05274v1.pdf,2023-09-11,['cs.cr'],2309.05274v1.pdf,"  Jailbreak vulnerabilities in Large Language Models (LLMs), which exploit
+meticulously crafted prompts to elicit content that violates service
+guidelines, have captured the attention of research communities. While model
+owners can defend against individual jailbreak prompts through safety training
+strategies, this relatively passive approach struggles to handle the broader
+category of similar jailbreaks. To tackle this issue, we introduce FuzzLLM, an
+automated fuzzing framework designed to proactively test and discover jailbreak
+vulnerabilities in LLMs. We utilize templates to capture the structural
+integrity of a prompt and isolate key features of a jailbreak class as
+constraints. By integrating different base classes into powerful combo attacks
+and varying the elements of constraints and prohibited questions, FuzzLLM
+enables efficient testing with reduced manual effort. Extensive experiments
+demonstrate FuzzLLM's effectiveness and comprehensiveness in vulnerability
+discovery across various LLMs.
+"
+Scalable and Transferable Black-Box Jailbreaks for Language Models via  Persona Modulation,Rusheb Shah,http://arxiv.org/pdf/2311.03348v1.pdf,2023-11-06,"['cs.cl', 'cs.ai', 'cs.lg']",2311.03348v1.pdf,"  Despite efforts to align large language models to produce harmless responses,
+they are still vulnerable to jailbreak prompts that elicit unrestricted
+behaviour. In this work, we investigate persona modulation as a black-box
+jailbreaking method to steer a target model to take on personalities that are
+willing to comply with harmful instructions. Rather than manually crafting
+prompts for each persona, we automate the generation of jailbreaks using a
+language model assistant. We demonstrate a range of harmful completions made
+possible by persona modulation, including detailed instructions for
+synthesising methamphetamine, building a bomb, and laundering money. These
+automated attacks achieve a harmful completion rate of 42.5% in GPT-4, which is
+185 times larger than before modulation (0.23%). These prompts also transfer to
+Claude 2 and Vicuna with harmful completion rates of 61.0% and 35.9%,
+respectively. Our work reveals yet another vulnerability in commercial large
+language models and highlights the need for more comprehensive safeguards.
+"
+Latent Jailbreak: A Benchmark for Evaluating Text Safety and Output  Robustness of Large Language Models,Huachuan Qiu,http://arxiv.org/pdf/2307.08487v3.pdf,2023-07-17,['cs.cl'],2307.08487v3.pdf,"  Considerable research efforts have been devoted to ensuring that large
+language models (LLMs) align with human values and generate safe text. However,
+an excessive focus on sensitivity to certain topics can compromise the model's
+robustness in following instructions, thereby impacting its overall performance
+in completing tasks. Previous benchmarks for jailbreaking LLMs have primarily
+focused on evaluating the safety of the models without considering their
+robustness. In this paper, we propose a benchmark that assesses both the safety
+and robustness of LLMs, emphasizing the need for a balanced approach. To
+comprehensively study text safety and output robustness, we introduce a latent
+jailbreak prompt dataset, each involving malicious instruction embedding.
+Specifically, we instruct the model to complete a regular task, such as
+translation, with the text to be translated containing malicious instructions.
+To further analyze safety and robustness, we design a hierarchical annotation
+framework. We present a systematic analysis of the safety and robustness of
+LLMs regarding the position of explicit normal instructions, word replacements
+(verbs in explicit normal instructions, target groups in malicious
+instructions, cue words for explicit normal instructions), and instruction
+replacements (different explicit normal instructions). Our results demonstrate
+that current LLMs not only prioritize certain instruction verbs but also
+exhibit varying jailbreak rates for different instruction verbs in explicit
+normal instructions. Code and data are available at
+https://github.com/qiuhuachuan/latent-jailbreak.
+"
+MasterKey: Automated Jailbreak Across Multiple Large Language Model  Chatbots,Gelei Deng,http://arxiv.org/pdf/2307.08715v2.pdf,2023-07-16,['cs.cr'],2307.08715v2.pdf,"  Large Language Models (LLMs) have revolutionized Artificial Intelligence (AI)
+services due to their exceptional proficiency in understanding and generating
+human-like text. LLM chatbots, in particular, have seen widespread adoption,
+transforming human-machine interactions. However, these LLM chatbots are
+susceptible to ""jailbreak"" attacks, where malicious users manipulate prompts to
+elicit inappropriate or sensitive responses, contravening service policies.
+Despite existing attempts to mitigate such threats, our research reveals a
+substantial gap in our understanding of these vulnerabilities, largely due to
+the undisclosed defensive measures implemented by LLM service providers.
+  In this paper, we present Jailbreaker, a comprehensive framework that offers
+an in-depth understanding of jailbreak attacks and countermeasures. Our work
+makes a dual contribution. First, we propose an innovative methodology inspired
+by time-based SQL injection techniques to reverse-engineer the defensive
+strategies of prominent LLM chatbots, such as ChatGPT, Bard, and Bing Chat.
+This time-sensitive approach uncovers intricate details about these services'
+defenses, facilitating a proof-of-concept attack that successfully bypasses
+their mechanisms. Second, we introduce an automatic generation method for
+jailbreak prompts. Leveraging a fine-tuned LLM, we validate the potential of
+automated jailbreak generation across various commercial LLM chatbots. Our
+method achieves a promising average success rate of 21.58%, significantly
+outperforming the effectiveness of existing techniques. We have responsibly
+disclosed our findings to the concerned service providers, underscoring the
+urgent need for more robust defenses. Jailbreaker thus marks a significant step
+towards understanding and mitigating jailbreak threats in the realm of LLM
+chatbots.
+"
+Using Large Language Models for Cybersecurity Capture-The-Flag  Challenges and Certification Questions,Wesley Tann,http://arxiv.org/pdf/2308.10443v1.pdf,2023-08-21,"['cs.ai', 'cs.cl', 'cs.cy']",2308.10443v1.pdf,"  The assessment of cybersecurity Capture-The-Flag (CTF) exercises involves
+participants finding text strings or ``flags'' by exploiting system
+vulnerabilities. Large Language Models (LLMs) are natural-language models
+trained on vast amounts of words to understand and generate text; they can
+perform well on many CTF challenges. Such LLMs are freely available to
+students. In the context of CTF exercises in the classroom, this raises
+concerns about academic integrity. Educators must understand LLMs' capabilities
+to modify their teaching to accommodate generative AI assistance. This research
+investigates the effectiveness of LLMs, particularly in the realm of CTF
+challenges and questions. Here we evaluate three popular LLMs, OpenAI ChatGPT,
+Google Bard, and Microsoft Bing. First, we assess the LLMs' question-answering
+performance on five Cisco certifications with varying difficulty levels. Next,
+we qualitatively study the LLMs' abilities in solving CTF challenges to
+understand their limitations. We report on the experience of using the LLMs for
+seven test cases in all five types of CTF challenges. In addition, we
+demonstrate how jailbreak prompts can bypass and break LLMs' ethical
+safeguards. The paper concludes by discussing LLM's impact on CTF exercises and
+its implications.
+"
+Baseline Defenses for Adversarial Attacks Against Aligned Language  Models,Neel Jain,http://arxiv.org/pdf/2309.00614v2.pdf,2023-09-01,"['cs.lg', 'cs.cl', 'cs.cr']",2309.00614v2.pdf,"  As Large Language Models quickly become ubiquitous, it becomes critical to
+understand their security vulnerabilities. Recent work shows that text
+optimizers can produce jailbreaking prompts that bypass moderation and
+alignment. Drawing from the rich body of work on adversarial machine learning,
+we approach these attacks with three questions: What threat models are
+practically useful in this domain? How do baseline defense techniques perform
+in this new domain? How does LLM security differ from computer vision?
+  We evaluate several baseline defense strategies against leading adversarial
+attacks on LLMs, discussing the various settings in which each is feasible and
+effective. Particularly, we look at three types of defenses: detection
+(perplexity based), input preprocessing (paraphrase and retokenization), and
+adversarial training. We discuss white-box and gray-box settings and discuss
+the robustness-performance trade-off for each of the defenses considered. We
+find that the weakness of existing discrete optimizers for text, combined with
+the relatively high costs of optimization, makes standard adaptive attacks more
+challenging for LLMs. Future research will be needed to uncover whether more
+powerful optimizers can be developed, or whether the strength of filtering and
+preprocessing defenses is greater in the LLMs domain than it has been in
+computer vision.
+"
+GPTFUZZER: Red Teaming Large Language Models with Auto-Generated  Jailbreak Prompts,Jiahao Yu,http://arxiv.org/pdf/2309.10253v2.pdf,2023-09-19,['cs.ai'],2309.10253v2.pdf,"  Large language models (LLMs) have recently experienced tremendous popularity
+and are widely used from casual conversations to AI-driven programming.
+However, despite their considerable success, LLMs are not entirely reliable and
+can give detailed guidance on how to conduct harmful or illegal activities.
+While safety measures can reduce the risk of such outputs, adversarial
+jailbreak attacks can still exploit LLMs to produce harmful content. These
+jailbreak templates are typically manually crafted, making large-scale testing
+challenging.
+  In this paper, we introduce GPTFuzz, a novel black-box jailbreak fuzzing
+framework inspired by the AFL fuzzing framework. Instead of manual engineering,
+GPTFuzz automates the generation of jailbreak templates for red-teaming LLMs.
+At its core, GPTFuzz starts with human-written templates as initial seeds, then
+mutates them to produce new templates. We detail three key components of
+GPTFuzz: a seed selection strategy for balancing efficiency and variability,
+mutate operators for creating semantically equivalent or similar sentences, and
+a judgment model to assess the success of a jailbreak attack.
+  We evaluate GPTFuzz against various commercial and open-source LLMs,
+including ChatGPT, LLaMa-2, and Vicuna, under diverse attack scenarios. Our
+results indicate that GPTFuzz consistently produces jailbreak templates with a
+high success rate, surpassing human-crafted templates. Remarkably, GPTFuzz
+achieves over 90% attack success rates against ChatGPT and Llama-2 models, even
+with suboptimal initial seed templates. We anticipate that GPTFuzz will be
+instrumental for researchers and practitioners in examining LLM robustness and
+will encourage further exploration into enhancing LLM safety.
+"
+Probing LLMs for hate speech detection: strengths and vulnerabilities,Sarthak Roy,http://arxiv.org/pdf/2310.12860v2.pdf,2023-10-19,"['cs.cl', 'cs.cy']",2310.12860v2.pdf,"  Recently efforts have been made by social media platforms as well as
+researchers to detect hateful or toxic language using large language models.
+However, none of these works aim to use explanation, additional context and
+victim community information in the detection process. We utilise different
+prompt variation, input information and evaluate large language models in zero
+shot setting (without adding any in-context examples). We select three large
+language models (GPT-3.5, text-davinci and Flan-T5) and three datasets -
+HateXplain, implicit hate and ToxicSpans. We find that on average including the
+target information in the pipeline improves the model performance substantially
+(~20-30%) over the baseline across the datasets. There is also a considerable
+effect of adding the rationales/explanations into the pipeline (~10-20%) over
+the baseline across the datasets. In addition, we further provide a typology of
+the error cases where these large language models fail to (i) classify and (ii)
+explain the reason for the decisions they take. Such vulnerable points
+automatically constitute 'jailbreak' prompts for these models and industry
+scale safeguard techniques need to be developed to make the models robust
+against such prompts.
+"
+Exploiting Asymmetry for Synthetic Training Data Generation: SynthIE and  the Case of Information Extraction,Martin Josifoski,http://arxiv.org/pdf/2303.04132v2.pdf,2023-03-07,"['cs.cl', 'cs.ai', 'cs.lg']",2303.04132v2.pdf,"  Large language models (LLMs) have great potential for synthetic data
+generation. This work shows that useful data can be synthetically generated
+even for tasks that cannot be solved directly by LLMs: for problems with
+structured outputs, it is possible to prompt an LLM to perform the task in the
+reverse direction, by generating plausible input text for a target output
+structure. Leveraging this asymmetry in task difficulty makes it possible to
+produce large-scale, high-quality data for complex tasks. We demonstrate the
+effectiveness of this approach on closed information extraction, where
+collecting ground-truth data is challenging, and no satisfactory dataset exists
+to date. We synthetically generate a dataset of 1.8M data points, establish its
+superior quality compared to existing datasets in a human evaluation, and use
+it to finetune small models (220M and 770M parameters), termed SynthIE, that
+outperform the prior state of the art (with equal model size) by a substantial
+margin of 57 absolute points in micro-F1 and 79 points in macro-F1. Code, data,
+and models are available at https://github.com/epfl-dlab/SynthIE.
+"
+Small Language Models Improve Giants by Rewriting Their Outputs,Giorgos Vernikos,http://arxiv.org/pdf/2305.13514v1.pdf,2023-05-22,"['cs.cl', 'cs.lg']",2305.13514v1.pdf,"  Large language models (LLMs) have demonstrated impressive few-shot learning
+capabilities, but they often underperform compared to fine-tuned models on
+challenging tasks. Furthermore, their large size and restricted access only
+through APIs make task-specific fine-tuning impractical. Moreover, LLMs are
+sensitive to different aspects of prompts (e.g., the selection and order of
+demonstrations) and can thus require time-consuming prompt engineering. In this
+light, we propose a method to correct LLM outputs without relying on their
+weights. First, we generate a pool of candidates by few-shot prompting an LLM.
+Second, we refine the LLM-generated outputs using a smaller model, the
+LM-corrector (LMCor), which is trained to rank, combine and rewrite the
+candidates to produce the final target output. Our experiments demonstrate that
+even a small LMCor model (250M) substantially improves the few-shot performance
+of LLMs (62B) across diverse tasks. Moreover, we illustrate that the LMCor
+exhibits robustness against different prompts, thereby minimizing the need for
+extensive prompt engineering. Finally, we showcase that the LMCor can be
+seamlessly integrated with different LLMs at inference time, serving as a
+plug-and-play module to improve their performance.
+"
+Aligning Language Models to User Opinions,EunJeong Hwang,http://arxiv.org/pdf/2305.14929v1.pdf,2023-05-24,['cs.cl'],2305.14929v1.pdf,"  An important aspect of developing LLMs that interact with humans is to align
+models' behavior to their users. It is possible to prompt an LLM into behaving
+as a certain persona, especially a user group or ideological persona the model
+captured during its pertaining stage. But, how to best align an LLM with a
+specific user and not a demographic or ideological group remains an open
+question. Mining public opinion surveys (by Pew Research), we find that the
+opinions of a user and their demographics and ideologies are not mutual
+predictors. We use this insight to align LLMs by modeling both user opinions as
+well as user demographics and ideology, achieving up to 7 points accuracy gains
+in predicting public opinions from survey questions across a broad set of
+topics. In addition to the typical approach of prompting LLMs with demographics
+and ideology, we discover that utilizing the most relevant past opinions from
+individual users enables the model to predict user opinions more accurately.
+"
+Marked Personas: Using Natural Language Prompts to Measure Stereotypes  in Language Models,Myra Cheng,http://arxiv.org/pdf/2305.18189v1.pdf,2023-05-29,"['cs.cl', 'cs.ai', 'cs.cy']",2305.18189v1.pdf,"  To recognize and mitigate harms from large language models (LLMs), we need to
+understand the prevalence and nuances of stereotypes in LLM outputs. Toward
+this end, we present Marked Personas, a prompt-based method to measure
+stereotypes in LLMs for intersectional demographic groups without any lexicon
+or data labeling. Grounded in the sociolinguistic concept of markedness (which
+characterizes explicitly linguistically marked categories versus unmarked
+defaults), our proposed method is twofold: 1) prompting an LLM to generate
+personas, i.e., natural language descriptions, of the target demographic group
+alongside personas of unmarked, default groups; 2) identifying the words that
+significantly distinguish personas of the target group from corresponding
+unmarked ones. We find that the portrayals generated by GPT-3.5 and GPT-4
+contain higher rates of racial stereotypes than human-written portrayals using
+the same prompts. The words distinguishing personas of marked (non-white,
+non-male) groups reflect patterns of othering and exoticizing these
+demographics. An intersectional lens further reveals tropes that dominate
+portrayals of marginalized groups, such as tropicalism and the
+hypersexualization of minoritized women. These representational harms have
+concerning implications for downstream applications like story generation.
+"
+Reranking for Natural Language Generation from Logical Forms: A Study  based on Large Language Models,Levon Haroutunian,http://arxiv.org/pdf/2309.12294v1.pdf,2023-09-21,['cs.cl'],2309.12294v1.pdf,"  Large language models (LLMs) have demonstrated impressive capabilities in
+natural language generation. However, their output quality can be inconsistent,
+posing challenges for generating natural language from logical forms (LFs).
+This task requires the generated outputs to embody the exact semantics of LFs,
+without missing any LF semantics or creating any hallucinations. In this work,
+we tackle this issue by proposing a novel generate-and-rerank approach. Our
+approach involves initially generating a set of candidate outputs by prompting
+an LLM and subsequently reranking them using a task-specific reranker model. In
+addition, we curate a manually collected dataset to evaluate the alignment
+between different ranking metrics and human judgements. The chosen ranking
+metrics are utilized to enhance the training and evaluation of the reranker
+model. By conducting extensive experiments on three diverse datasets, we
+demonstrate that the candidates selected by our reranker outperform those
+selected by baseline methods in terms of semantic consistency and fluency, as
+measured by three comprehensive metrics. Our findings provide strong evidence
+for the effectiveness of our approach in improving the quality of generated
+outputs.
+"
+Query Rewriting for Retrieval-Augmented Large Language Models,Xinbei Ma,http://arxiv.org/pdf/2305.14283v3.pdf,2023-05-23,['cs.cl'],2305.14283v3.pdf,"  Large Language Models (LLMs) play powerful, black-box readers in the
+retrieve-then-read pipeline, making remarkable progress in knowledge-intensive
+tasks. This work introduces a new framework, Rewrite-Retrieve-Read instead of
+the previous retrieve-then-read for the retrieval-augmented LLMs from the
+perspective of the query rewriting. Unlike prior studies focusing on adapting
+either the retriever or the reader, our approach pays attention to the
+adaptation of the search query itself, for there is inevitably a gap between
+the input text and the needed knowledge in retrieval. We first prompt an LLM to
+generate the query, then use a web search engine to retrieve contexts.
+Furthermore, to better align the query to the frozen modules, we propose a
+trainable scheme for our pipeline. A small language model is adopted as a
+trainable rewriter to cater to the black-box LLM reader. The rewriter is
+trained using the feedback of the LLM reader by reinforcement learning.
+Evaluation is conducted on downstream tasks, open-domain QA and multiple-choice
+QA. Experiments results show consistent performance improvement, indicating
+that our framework is proven effective and scalable, and brings a new framework
+for retrieval-augmented LLM.
+"
+ALGO: Synthesizing Algorithmic Programs with Generated Oracle Verifiers,Kexun Zhang,http://arxiv.org/pdf/2305.14591v2.pdf,2023-05-24,"['cs.cl', 'cs.se']",2305.14591v2.pdf,"  Large language models (LLMs) excel at implementing code from functionality
+descriptions but struggle with algorithmic problems that require not only
+implementation but also identification of the suitable algorithm. Moreover,
+LLM-generated programs lack guaranteed correctness and require human
+verification. To address these challenges, we propose ALGO, a framework that
+synthesizes Algorithmic programs with LLM-Generated Oracles to guide the
+generation and verify their correctness. ALGO first generates a reference
+oracle by prompting an LLM to exhaustively enumerate all the combinations of
+relevant variables. This oracle is then utilized to guide an arbitrary search
+strategy in exploring the algorithm space and to verify the synthesized
+algorithms. Our study shows that the LLM-generated oracles are correct for 88%
+of the cases. With the oracles as verifiers, ALGO can be integrated with any
+existing code generation model in a model-agnostic manner to enhance its
+performance. Experiments show that when equipped with ALGO, we achieve an 8x
+better one-submission pass rate over the Codex model and a 2.6x better
+one-submission pass rate over CodeT, the current state-of-the-art model on
+CodeContests. We can also get 1.3x better pass rate over the ChatGPT Code
+Interpreter on unseen problems. The problem set we used for testing, the
+prompts we used, the verifier and solution programs, and the test cases
+generated by ALGO are available at https://github.com/zkx06111/ALGO.
+"
+PromptNER: Prompting For Named Entity Recognition,Dhananjay Ashok,http://arxiv.org/pdf/2305.15444v2.pdf,2023-05-24,"['cs.cl', 'cs.ai', 'cs.lg']",2305.15444v2.pdf,"  In a surprising turn, Large Language Models (LLMs) together with a growing
+arsenal of prompt-based heuristics now offer powerful off-the-shelf approaches
+providing few-shot solutions to myriad classic NLP problems. However, despite
+promising early results, these LLM-based few-shot methods remain far from the
+state of the art in Named Entity Recognition (NER), where prevailing methods
+include learning representations via end-to-end structural understanding and
+fine-tuning on standard labeled corpora. In this paper, we introduce PromptNER,
+a new state-of-the-art algorithm for few-Shot and cross-domain NER. To adapt to
+any new NER task PromptNER requires a set of entity definitions in addition to
+the standard few-shot examples. Given a sentence, PromptNER prompts an LLM to
+produce a list of potential entities along with corresponding explanations
+justifying their compatibility with the provided entity type definitions.
+Remarkably, PromptNER achieves state-of-the-art performance on few-shot NER,
+achieving a 4% (absolute) improvement in F1 score on the ConLL dataset, a 9%
+(absolute) improvement on the GENIA dataset, and a 4% (absolute) improvement on
+the FewNERD dataset. PromptNER also moves the state of the art on Cross Domain
+NER, outperforming prior methods (including those not limited to the few-shot
+setting), setting a new mark on 3/5 CrossNER target domains, with an average F1
+gain of 3%, despite using less than 2% of the available data.
+"
+Dcc --help: Generating Context-Aware Compiler Error Explanations with  Large Language Models,Andrew Taylor,http://arxiv.org/pdf/2308.11873v2.pdf,2023-08-23,"['cs.se', 'cs.lg', 'cs.pl']",2308.11873v2.pdf,"  In the challenging field of introductory programming, high enrollments and
+failure rates drive us to explore tools and systems to enhance student
+outcomes, especially automated tools that scale to large cohorts. This paper
+presents and evaluates the dcc --help tool, an integration of a Large Language
+Model (LLM) into the Debugging C Compiler (DCC) to generate unique,
+novice-focused explanations tailored to each error. dcc --help prompts an LLM
+with contextual information of compile- and run-time error occurrences,
+including the source code, error location and standard compiler error message.
+The LLM is instructed to generate novice-focused, actionable error explanations
+and guidance, designed to help students understand and resolve problems without
+providing solutions. dcc --help was deployed to our CS1 and CS2 courses, with
+2,565 students using the tool over 64,000 times in ten weeks. We analysed a
+subset of these error/explanation pairs to evaluate their properties, including
+conceptual correctness, relevancy, and overall quality. We found that the
+LLM-generated explanations were conceptually accurate in 90% of compile-time
+and 75% of run-time cases, but often disregarded the instruction not to provide
+solutions in code. Our findings, observations and reflections following
+deployment indicate that dcc-help provides novel opportunities for scaffolding
+students' introduction to programming.
+"
+BLSP: Bootstrapping Language-Speech Pre-training via Behavior Alignment  of Continuation Writing,Chen Wang,http://arxiv.org/pdf/2309.00916v1.pdf,2023-09-02,"['cs.cl', 'cs.sd', 'eess.as']",2309.00916v1.pdf,"  The emergence of large language models (LLMs) has sparked significant
+interest in extending their remarkable language capabilities to speech.
+However, modality alignment between speech and text still remains an open
+problem. Current solutions can be categorized into two strategies. One is a
+cascaded approach where outputs (tokens or states) of a separately trained
+speech recognition system are used as inputs for LLMs, which limits their
+potential in modeling alignment between speech and text. The other is an
+end-to-end approach that relies on speech instruction data, which is very
+difficult to collect in large quantities. In this paper, we address these
+issues and propose the BLSP approach that Bootstraps Language-Speech
+Pre-training via behavior alignment of continuation writing. We achieve this by
+learning a lightweight modality adapter between a frozen speech encoder and an
+LLM, ensuring that the LLM exhibits the same generation behavior regardless of
+the modality of input: a speech segment or its transcript. The training process
+can be divided into two steps. The first step prompts an LLM to generate texts
+with speech transcripts as prefixes, obtaining text continuations. In the
+second step, these continuations are used as supervised signals to train the
+modality adapter in an end-to-end manner. We demonstrate that this
+straightforward process can extend the capabilities of LLMs to speech, enabling
+speech recognition, speech translation, spoken language understanding, and
+speech conversation, even in zero-shot cross-lingual scenarios.
+"
+Balanced and Explainable Social Media Analysis for Public Health with  Large Language Models,Yan Jiang,http://arxiv.org/pdf/2309.05951v1.pdf,2023-09-12,['cs.cl'],2309.05951v1.pdf,"  As social media becomes increasingly popular, more and more public health
+activities emerge, which is worth noting for pandemic monitoring and government
+decision-making. Current techniques for public health analysis involve popular
+models such as BERT and large language models (LLMs). Although recent progress
+in LLMs has shown a strong ability to comprehend knowledge by being fine-tuned
+on specific domain datasets, the costs of training an in-domain LLM for every
+specific public health task are especially expensive. Furthermore, such kinds
+of in-domain datasets from social media are generally highly imbalanced, which
+will hinder the efficiency of LLMs tuning. To tackle these challenges, the data
+imbalance issue can be overcome by sophisticated data augmentation methods for
+social media datasets. In addition, the ability of the LLMs can be effectively
+utilised by prompting the model properly. In light of the above discussion, in
+this paper, a novel ALEX framework is proposed for social media analysis on
+public health. Specifically, an augmentation pipeline is developed to resolve
+the data imbalance issue. Furthermore, an LLMs explanation mechanism is
+proposed by prompting an LLM with the predicted results from BERT models.
+Extensive experiments conducted on three tasks at the Social Media Mining for
+Health 2023 (SMM4H) competition with the first ranking in two tasks demonstrate
+the superior performance of the proposed ALEX method. Our code has been
+released in https://github.com/YanJiangJerry/ALEX.
+"
+HowToCaption: Prompting LLMs to Transform Video Annotations at Scale,Nina Shvetsova,http://arxiv.org/pdf/2310.04900v1.pdf,2023-10-07,['cs.cv'],2310.04900v1.pdf,"  Instructional videos are an excellent source for learning multimodal
+representations by leveraging video-subtitle pairs extracted with automatic
+speech recognition systems (ASR) from the audio signal in the videos. However,
+in contrast to human-annotated captions, both speech and subtitles naturally
+differ from the visual content of the videos and thus provide only noisy
+supervision for multimodal learning. As a result, large-scale annotation-free
+web video training data remains sub-optimal for training text-video models. In
+this work, we propose to leverage the capability of large language models
+(LLMs) to obtain fine-grained video descriptions aligned with videos.
+Specifically, we prompt an LLM to create plausible video descriptions based on
+ASR narrations of the video for a large-scale instructional video dataset. To
+this end, we introduce a prompting method that is able to take into account a
+longer text of subtitles, allowing us to capture context beyond a single
+sentence. To align the captions to the video temporally, we prompt the LLM to
+generate timestamps for each produced caption based on the subtitles. In this
+way, we obtain human-style video captions at scale without human supervision.
+We apply our method to the subtitles of the HowTo100M dataset, creating a new
+large-scale dataset, HowToCaption. Our evaluation shows that the resulting
+captions not only significantly improve the performance over many different
+benchmark datasets for text-video retrieval but also lead to a disentangling of
+textual narration from the audio, boosting performance in text-video-audio
+tasks.
+"
+ClarifyGPT: Empowering LLM-based Code Generation with Intention  Clarification,Fangwen Mu,http://arxiv.org/pdf/2310.10996v1.pdf,2023-10-17,['cs.se'],2310.10996v1.pdf,"  We introduce a novel framework named ClarifyGPT, which aims to enhance code
+generation by empowering LLMs with the ability to identify ambiguous
+requirements and ask targeted clarifying questions. In particular, ClarifyGPT
+first detects whether a given requirement is ambiguous by performing a code
+consistency check. If it is ambiguous, ClarifyGPT prompts an LLM to generate
+targeted clarifying questions. After receiving question responses, ClarifyGPT
+refines the ambiguous requirement and inputs it into the same LLM to generate a
+final code solution. To evaluate our ClarifyGPT, we first conduct a human
+evaluation involving ten participants who use ClarifyGPT for code generation on
+two publicly available benchmarks: MBPP-sanitized and MBPP-ET. The results show
+that ClarifyGPT elevates the performance (Pass@1) of GPT-4 from 70.96% to
+80.80% on MBPP-sanitized. Furthermore, to perform large-scale automated
+evaluations of ClarifyGPT across different LLMs and benchmarks without
+requiring user participation, we introduce a high-fidelity simulation method to
+simulate user responses. The automated evaluation results also demonstrate that
+ClarifyGPT can significantly enhance code generation performance compared to
+the baselines. In particular, ClarifyGPT improves the average performance of
+GPT-4 and ChatGPT across four benchmarks from 68.02% to 75.75% and from 58.55%
+to 67.22%, respectively. We believe that ClarifyGPT can effectively facilitate
+the practical application of LLMs in real-world development environments.
+"
+Harnessing Explanations: LLM-to-LM Interpreter for Enhanced  Text-Attributed Graph Representation Learning,Xiaoxin He,http://arxiv.org/pdf/2305.19523v3.pdf,2023-05-31,['cs.lg'],2305.19523v3.pdf,"  Representation learning on text-attributed graphs (TAGs) has become a
+critical research problem in recent years. A typical example of a TAG is a
+paper citation graph, where the text of each paper serves as node attributes.
+Initial graph neural network (GNN) pipelines handled these text attributes by
+transforming them into shallow or hand-crafted features, such as skip-gram or
+bag-of-words features. Recent efforts have focused on enhancing these pipelines
+with language models (LMs), which typically demand intricate designs and
+substantial computational resources. With the advent of powerful large language
+models (LLMs) such as GPT or Llama2, which demonstrate an ability to reason and
+to utilize general knowledge, there is a growing need for techniques which
+combine the textual modelling abilities of LLMs with the structural learning
+capabilities of GNNs. Hence, in this work, we focus on leveraging LLMs to
+capture textual information as features, which can be used to boost GNN
+performance on downstream tasks. A key innovation is our use of explanations as
+features: we prompt an LLM to perform zero-shot classification, request textual
+explanations for its decision-making process, and design an LLM-to-LM
+interpreter to translate these explanations into informative features that
+enhance downstream GNNs. Our experiments demonstrate that our method achieves
+state-of-the-art results on well-established TAG datasets, including Cora,
+PubMed, ogbn-arxiv, as well as our newly introduced dataset, arXiv-2023.
+Furthermore, our method significantly speeds up training, achieving a 2.88
+times improvement over the closest baseline on ogbn-arxiv. Lastly, we believe
+the versatility of the proposed method extends beyond TAGs and holds the
+potential to enhance other tasks involving graph-text data~\footnote{Our codes
+and datasets are available at: \url{https://github.com/XiaoxinHe/TAPE}}.
+"
+LEGO-Prover: Neural Theorem Proving with Growing Libraries,Haiming Wang,http://arxiv.org/pdf/2310.00656v3.pdf,2023-10-01,['cs.ai'],2310.00656v3.pdf,"  Despite the success of large language models (LLMs), the task of theorem
+proving still remains one of the hardest reasoning tasks that is far from being
+fully solved. Prior methods using language models have demonstrated promising
+results, but they still struggle to prove even middle school level theorems.
+One common limitation of these methods is that they assume a fixed theorem
+library during the whole theorem proving process. However, as we all know,
+creating new useful theorems or even new theories is not only helpful but
+crucial and necessary for advancing mathematics and proving harder and deeper
+results. In this work, we present LEGO-Prover, which employs a growing skill
+library containing verified lemmas as skills to augment the capability of LLMs
+used in theorem proving. By constructing the proof modularly, LEGO-Prover
+enables LLMs to utilize existing skills retrieved from the library and to
+create new skills during the proving process. These skills are further evolved
+(by prompting an LLM) to enrich the library on another scale. Modular and
+reusable skills are constantly added to the library to enable tackling
+increasingly intricate mathematical problems. Moreover, the learned library
+further bridges the gap between human proofs and formal proofs by making it
+easier to impute missing steps. LEGO-Prover advances the state-of-the-art pass
+rate on miniF2F-valid (48.0% to 57.0%) and miniF2F-test (45.5% to 47.1%).
+During the proving process, LEGO-Prover also manages to generate over 20,000
+skills (theorems/lemmas) and adds them to the growing library. Our ablation
+study indicates that these newly added skills are indeed helpful for proving
+theorems, resulting in an improvement from a success rate of 47.1% to 50.4%. We
+also release our code and all the generated skills.
+"
+BooookScore: A systematic exploration of book-length summarization in  the era of LLMs,Yapei Chang,http://arxiv.org/pdf/2310.00785v2.pdf,2023-10-01,"['cs.cl', 'cs.ai', 'cs.lg']",2310.00785v2.pdf,"  Summarizing book-length documents (>100K tokens) that exceed the context
+window size of large language models (LLMs) requires first breaking the input
+document into smaller chunks and then prompting an LLM to merge, update, and
+compress chunk-level summaries. Despite the complexity and importance of this
+task, it has yet to be meaningfully studied due to the challenges of
+evaluation: existing book-length summarization datasets (e.g., BookSum) are in
+the pretraining data of most public LLMs, and existing evaluation methods
+struggle to capture errors made by modern LLM summarizers. In this paper, we
+present the first study of the coherence of LLM-based book-length summarizers
+implemented via two prompting workflows: (1) hierarchically merging chunk-level
+summaries, and (2) incrementally updating a running summary. We obtain 1193
+fine-grained human annotations on GPT-4 generated summaries of 100
+recently-published books and identify eight common types of coherence errors
+made by LLMs. Because human evaluation is expensive and time-consuming, we
+develop an automatic metric, BooookScore, that measures the proportion of
+sentences in a summary that do not contain any of the identified error types.
+BooookScore has high agreement with human annotations and allows us to
+systematically evaluate the impact of many other critical parameters (e.g.,
+chunk size, base LLM) while saving $15K and 500 hours in human evaluation
+costs. We find that closed-source LLMs such as GPT-4 and Claude 2 produce
+summaries with higher BooookScore than the oft-repetitive ones generated by
+LLaMA 2. Incremental updating yields lower BooookScore but higher level of
+detail than hierarchical merging, a trade-off sometimes preferred by human
+annotators. We release code and annotations after blind review to spur more
+principled research on book-length summarization.
+"
+The Unreliability of Explanations in Few-shot Prompting for Textual  Reasoning,Xi Ye,http://arxiv.org/pdf/2205.03401v2.pdf,2022-05-06,['cs.cl'],2205.03401v2.pdf,"  Does prompting a large language model (LLM) like GPT-3 with explanations
+improve in-context learning? We study this question on two NLP tasks that
+involve reasoning over text, namely question answering and natural language
+inference. We test the performance of four LLMs on three textual reasoning
+datasets using prompts that include explanations in multiple different styles.
+For these tasks, we find that including explanations in the prompts for OPT,
+GPT-3 (davinci), and InstructGPT (text-davinci-001) only yields small to
+moderate accuracy improvements over standard few-show learning. However,
+text-davinci-002 is able to benefit more substantially.
+  We further show that explanations generated by the LLMs may not entail the
+models' predictions nor be factually grounded in the input, even on simple
+tasks with extractive explanations. However, these flawed explanations can
+still be useful as a way to verify LLMs' predictions post-hoc. Through analysis
+in our three settings, we show that explanations judged by humans to be
+good--logically consistent with the input and the prediction--more likely
+cooccur with accurate predictions. Following these observations, we train
+calibrators using automatically extracted scores that assess the reliability of
+explanations, allowing us to improve performance post-hoc across all of our
+datasets.
+"
+Contrastive Novelty-Augmented Learning: Anticipating Outliers with Large  Language Models,Albert Xu,http://arxiv.org/pdf/2211.15718v2.pdf,2022-11-28,['cs.cl'],2211.15718v2.pdf,"  In many task settings, text classification models are likely to encounter
+examples from novel classes on which they cannot predict correctly. Selective
+prediction, in which models abstain on low-confidence examples, provides a
+possible solution, but existing models are often overly confident on unseen
+classes. To remedy this overconfidence, we introduce Contrastive
+Novelty-Augmented Learning (CoNAL), a two-step method that generates OOD
+examples representative of novel classes, then trains to decrease confidence on
+them. First, we generate OOD examples by prompting a large language model
+twice: we prompt it to enumerate relevant novel classes, then generate examples
+from each novel class matching the task format. Second, we train a classifier
+with a novel contrastive objective that encourages lower confidence on
+generated OOD examples than training examples. When trained with CoNAL,
+classifiers improve in their ability to detect and abstain on novel class
+examples over prior methods by an average of 2.3% in terms of accuracy under
+the accuracy-coverage curve (AUAC) and 5.5% AUROC across 4 NLP datasets, with
+no cost to in-distribution accuracy.
+"
+Extensible Prompts for Language Models,Tao Ge,http://arxiv.org/pdf/2212.00616v1.pdf,2022-12-01,['cs.cl'],2212.00616v1.pdf,"  We propose eXtensible Prompt (X-Prompt) for prompting a large language model
+(LLM) beyond natural language (NL). X-Prompt instructs an LLM with not only NL
+but also an extensible vocabulary of imaginary words that are introduced to
+help represent what NL words hardly describe, allowing a prompt to be more
+descriptive. Like NL prompts, X-Prompt is out-of-distribution (OOD) robust, for
+which we propose context-guided learning with prompt augmentation to learn its
+imaginary words for general usability, enabling them to use in different prompt
+contexts for fine-grain specifications. The promising results of X-Prompt
+demonstrate its potential of approaching advanced interaction between humans
+and LLMs to bridge their communication gap.
+"
+Reward Design with Language Models,Minae Kwon,http://arxiv.org/pdf/2303.00001v1.pdf,2023-02-27,"['cs.lg', 'cs.ai', 'cs.cl']",2303.00001v1.pdf,"  Reward design in reinforcement learning (RL) is challenging since specifying
+human notions of desired behavior may be difficult via reward functions or
+require many expert demonstrations. Can we instead cheaply design rewards using
+a natural language interface? This paper explores how to simplify reward design
+by prompting a large language model (LLM) such as GPT-3 as a proxy reward
+function, where the user provides a textual prompt containing a few examples
+(few-shot) or a description (zero-shot) of the desired behavior. Our approach
+leverages this proxy reward function in an RL framework. Specifically, users
+specify a prompt once at the beginning of training. During training, the LLM
+evaluates an RL agent's behavior against the desired behavior described by the
+prompt and outputs a corresponding reward signal. The RL agent then uses this
+reward to update its behavior. We evaluate whether our approach can train
+agents aligned with user objectives in the Ultimatum Game, matrix games, and
+the DealOrNoDeal negotiation task. In all three tasks, we show that RL agents
+trained with our framework are well-aligned with the user's objectives and
+outperform RL agents trained with reward functions learned via supervised
+learning
+"
+Prompt-Based Monte-Carlo Tree Search for Goal-Oriented Dialogue Policy  Planning,Xiao Yu,http://arxiv.org/pdf/2305.13660v2.pdf,2023-05-23,['cs.cl'],2305.13660v2.pdf,"  Planning for goal-oriented dialogue often requires simulating future dialogue
+interactions and estimating task progress. Many approaches thus consider
+training neural networks to perform look-ahead search algorithms such as A*
+search and Monte Carlo Tree Search (MCTS). However, this training often
+requires abundant annotated data, which creates challenges when faced with
+noisy annotations or low-resource settings. We introduce GDP-Zero, an approach
+using Open-Loop MCTS to perform goal-oriented dialogue policy planning without
+any model training. GDP-Zero prompts a large language model to act as a policy
+prior, value function, user simulator, and system model during the tree search.
+We evaluate GDP-Zero on the goal-oriented task PersuasionForGood, and find that
+its responses are preferred over ChatGPT up to 59.32% of the time, and are
+rated more persuasive than ChatGPT during interactive evaluations.
+"
+IDAS: Intent Discovery with Abstractive Summarization,Maarten De Raedt,http://arxiv.org/pdf/2305.19783v1.pdf,2023-05-31,['cs.cl'],2305.19783v1.pdf,"  Intent discovery is the task of inferring latent intents from a set of
+unlabeled utterances, and is a useful step towards the efficient creation of
+new conversational agents. We show that recent competitive methods in intent
+discovery can be outperformed by clustering utterances based on abstractive
+summaries, i.e., ""labels"", that retain the core elements while removing
+non-essential information. We contribute the IDAS approach, which collects a
+set of descriptive utterance labels by prompting a Large Language Model,
+starting from a well-chosen seed set of prototypical utterances, to bootstrap
+an In-Context Learning procedure to generate labels for non-prototypical
+utterances. The utterances and their resulting noisy labels are then encoded by
+a frozen pre-trained encoder, and subsequently clustered to recover the latent
+intents. For the unsupervised task (without any intent labels) IDAS outperforms
+the state-of-the-art by up to +7.42% in standard cluster metrics for the
+Banking, StackOverflow, and Transport datasets. For the semi-supervised task
+(with labels for a subset of intents) IDAS surpasses 2 recent methods on the
+CLINC benchmark without even using labeled data.
+"
+Prompting a Large Language Model to Generate Diverse Motivational  Messages: A Comparison with Human-Written Messages,Samuel Rhys Cox,http://arxiv.org/pdf/2308.13479v1.pdf,2023-08-25,"['cs.cl', 'cs.hc']",2308.13479v1.pdf,"  Large language models (LLMs) are increasingly capable and prevalent, and can
+be used to produce creative content. The quality of content is influenced by
+the prompt used, with more specific prompts that incorporate examples generally
+producing better results. On from this, it could be seen that using
+instructions written for crowdsourcing tasks (that are specific and include
+examples to guide workers) could prove effective LLM prompts. To explore this,
+we used a previous crowdsourcing pipeline that gave examples to people to help
+them generate a collectively diverse corpus of motivational messages. We then
+used this same pipeline to generate messages using GPT-4, and compared the
+collective diversity of messages from: (1) crowd-writers, (2) GPT-4 using the
+pipeline, and (3 & 4) two baseline GPT-4 prompts. We found that the LLM prompts
+using the crowdsourcing pipeline caused GPT-4 to produce more diverse messages
+than the two baseline prompts. We also discuss implications from messages
+generated by both human writers and LLMs.
+"
+Social Simulacra: Creating Populated Prototypes for Social Computing  Systems,Joon Sung Park,http://arxiv.org/pdf/2208.04024v1.pdf,2022-08-08,['cs.hc'],2208.04024v1.pdf,"  Social computing prototypes probe the social behaviors that may arise in an
+envisioned system design. This prototyping practice is currently limited to
+recruiting small groups of people. Unfortunately, many challenges do not arise
+until a system is populated at a larger scale. Can a designer understand how a
+social system might behave when populated, and make adjustments to the design
+before the system falls prey to such challenges? We introduce social simulacra,
+a prototyping technique that generates a breadth of realistic social
+interactions that may emerge when a social computing system is populated.
+Social simulacra take as input the designer's description of a community's
+design -- goal, rules, and member personas -- and produce as output an instance
+of that design with simulated behavior, including posts, replies, and
+anti-social behaviors. We demonstrate that social simulacra shift the behaviors
+that they generate appropriately in response to design changes, and that they
+enable exploration of ""what if?"" scenarios where community members or
+moderators intervene. To power social simulacra, we contribute techniques for
+prompting a large language model to generate thousands of distinct community
+members and their social interactions with each other; these techniques are
+enabled by the observation that large language models' training data already
+includes a wide variety of positive and negative behavior on social media
+platforms. In evaluations, we show that participants are often unable to
+distinguish social simulacra from actual community behavior and that social
+computing designers successfully refine their social computing designs when
+using social simulacra.
+"
+Generate rather than Retrieve: Large Language Models are Strong Context  Generators,Wenhao Yu,http://arxiv.org/pdf/2209.10063v3.pdf,2022-09-21,"['cs.cl', 'cs.ai']",2209.10063v3.pdf,"  Knowledge-intensive tasks, such as open-domain question answering (QA),
+require access to a large amount of world or domain knowledge. A common
+approach for knowledge-intensive tasks is to employ a retrieve-then-read
+pipeline that first retrieves a handful of relevant contextual documents from
+an external corpus such as Wikipedia and then predicts an answer conditioned on
+the retrieved documents. In this paper, we present a novel perspective for
+solving knowledge-intensive tasks by replacing document retrievers with large
+language model generators. We call our method generate-then-read (GenRead),
+which first prompts a large language model to generate contextutal documents
+based on a given question, and then reads the generated documents to produce
+the final answer. Furthermore, we propose a novel clustering-based prompting
+method that selects distinct prompts, resulting in the generated documents that
+cover different perspectives, leading to better recall over acceptable answers.
+We conduct extensive experiments on three different knowledge-intensive tasks,
+including open-domain QA, fact checking, and dialogue system. Notably, GenRead
+achieves 71.6 and 54.4 exact match scores on TriviaQA and WebQ, significantly
+outperforming the state-of-the-art retrieve-then-read pipeline DPR-FiD by +4.0
+and +3.9, without retrieving any documents from any external knowledge source.
+Lastly, we demonstrate the model performance can be further improved by
+combining retrieval and generation. Our code and generated documents can be
+found at https://github.com/wyu97/GenRead.
+"
+q2d: Turning Questions into Dialogs to Teach Models How to Search,Yonatan Bitton,http://arxiv.org/pdf/2304.14318v1.pdf,2023-04-27,['cs.cl'],2304.14318v1.pdf,"  One of the exciting capabilities of recent language models for dialog is
+their ability to independently search for relevant information to ground a
+given dialog response. However, obtaining training data to teach models how to
+issue search queries is time and resource consuming. In this work, we propose
+q2d: an automatic data generation pipeline that generates information-seeking
+dialogs from questions. We prompt a large language model (PaLM) to create
+conversational versions of question answering datasets, and use it to improve
+query generation models that communicate with external search APIs to ground
+dialog responses. Unlike previous approaches which relied on human written
+dialogs with search queries, our method allows to automatically generate
+query-based grounded dialogs with better control and scale. Our experiments
+demonstrate that: (1) For query generation on the QReCC dataset, models trained
+on our synthetically-generated data achieve 90%--97% of the performance of
+models trained on the human-generated data; (2) We can successfully generate
+data for training dialog models in new domains without any existing dialog data
+as demonstrated on the multi-hop MuSiQue and Bamboogle QA datasets. (3) We
+perform a thorough analysis of the generated dialogs showing that humans find
+them of high quality and struggle to distinguish them from human-written
+dialogs.
+"
+Multi-Modal Classifiers for Open-Vocabulary Object Detection,Prannay Kaul,http://arxiv.org/pdf/2306.05493v1.pdf,2023-06-08,"['cs.cv', 'cs.ai', 'cs.lg', 'i.4.6; i.4.8; i.4.9; i.2.10']",2306.05493v1.pdf,"  The goal of this paper is open-vocabulary object detection (OVOD)
+$\unicode{x2013}$ building a model that can detect objects beyond the set of
+categories seen at training, thus enabling the user to specify categories of
+interest at inference without the need for model retraining. We adopt a
+standard two-stage object detector architecture, and explore three ways for
+specifying novel categories: via language descriptions, via image exemplars, or
+via a combination of the two. We make three contributions: first, we prompt a
+large language model (LLM) to generate informative language descriptions for
+object classes, and construct powerful text-based classifiers; second, we
+employ a visual aggregator on image exemplars that can ingest any number of
+images as input, forming vision-based classifiers; and third, we provide a
+simple method to fuse information from language descriptions and image
+exemplars, yielding a multi-modal classifier. When evaluating on the
+challenging LVIS open-vocabulary benchmark we demonstrate that: (i) our
+text-based classifiers outperform all previous OVOD works; (ii) our
+vision-based classifiers perform as well as text-based classifiers in prior
+work; (iii) using multi-modal classifiers perform better than either modality
+alone; and finally, (iv) our text-based and multi-modal classifiers yield
+better performance than a fully-supervised detector.
+"
+InstructEval: Systematic Evaluation of Instruction Selection Methods,Anirudh Ajith,http://arxiv.org/pdf/2307.00259v2.pdf,2023-07-01,"['cs.cl', 'cs.ai']",2307.00259v2.pdf,"  In-context learning (ICL) performs tasks by prompting a large language model
+(LLM) using an instruction and a small set of annotated examples called
+demonstrations. Recent work has shown that precise details of the inputs used
+in the ICL prompt significantly impact performance, which has incentivized
+instruction selection algorithms. The effect of instruction-choice however is
+severely underexplored, with existing analyses restricted to shallow subsets of
+models and tasks, limiting the generalizability of their insights. We develop
+InstructEval, an ICL evaluation suite to conduct a thorough assessment of these
+techniques. The suite includes 13 open-sourced LLMs of varying scales from four
+model families, and covers nine tasks across three categories. Using the suite,
+we evaluate the relative performance of seven popular instruction selection
+methods over five metrics relevant to ICL. Our experiments reveal that using
+curated manually-written instructions or simple instructions without any
+task-specific descriptions often elicits superior ICL performance overall than
+that of automatic instruction-induction methods, pointing to a lack of
+generalizability among the latter. We release our evaluation suite for
+benchmarking instruction selection approaches and enabling more generalizable
+methods in this space.
+"
+Prompt Injection Attacks and Defenses in LLM-Integrated Applications,Yupei Liu,http://arxiv.org/pdf/2310.12815v1.pdf,2023-10-19,"['cs.cr', 'cs.ai', 'cs.cl', 'cs.lg']",2310.12815v1.pdf,"  Large Language Models (LLMs) are increasingly deployed as the backend for a
+variety of real-world applications called LLM-Integrated Applications. Multiple
+recent works showed that LLM-Integrated Applications are vulnerable to prompt
+injection attacks, in which an attacker injects malicious instruction/data into
+the input of those applications such that they produce results as the attacker
+desires. However, existing works are limited to case studies. As a result, the
+literature lacks a systematic understanding of prompt injection attacks and
+their defenses. We aim to bridge the gap in this work. In particular, we
+propose a general framework to formalize prompt injection attacks. Existing
+attacks, which are discussed in research papers and blog posts, are special
+cases in our framework. Our framework enables us to design a new attack by
+combining existing attacks. Moreover, we also propose a framework to
+systematize defenses against prompt injection attacks. Using our frameworks, we
+conduct a systematic evaluation on prompt injection attacks and their defenses
+with 10 LLMs and 7 tasks. We hope our frameworks can inspire future research in
+this field. Our code is available at
+https://github.com/liu00222/Open-Prompt-Injection.
+"
+Prompt Injection attack against LLM-integrated Applications,Yi Liu,http://arxiv.org/pdf/2306.05499v1.pdf,2023-06-08,"['cs.cr', 'cs.ai', 'cs.cl', 'cs.se']",2306.05499v1.pdf,"  Large Language Models (LLMs), renowned for their superior proficiency in
+language comprehension and generation, stimulate a vibrant ecosystem of
+applications around them. However, their extensive assimilation into various
+services introduces significant security risks. This study deconstructs the
+complexities and implications of prompt injection attacks on actual
+LLM-integrated applications. Initially, we conduct an exploratory analysis on
+ten commercial applications, highlighting the constraints of current attack
+strategies in practice. Prompted by these limitations, we subsequently
+formulate HouYi, a novel black-box prompt injection attack technique, which
+draws inspiration from traditional web injection attacks. HouYi is
+compartmentalized into three crucial elements: a seamlessly-incorporated
+pre-constructed prompt, an injection prompt inducing context partition, and a
+malicious payload designed to fulfill the attack objectives. Leveraging HouYi,
+we unveil previously unknown and severe attack outcomes, such as unrestricted
+arbitrary LLM usage and uncomplicated application prompt theft. We deploy HouYi
+on 36 actual LLM-integrated applications and discern 31 applications
+susceptible to prompt injection. 10 vendors have validated our discoveries,
+including Notion, which has the potential to impact millions of users. Our
+investigation illuminates both the possible risks of prompt injection attacks
+and the possible tactics for mitigation.
+"
+Tensor Trust: Interpretable Prompt Injection Attacks from an Online Game,Sam Toyer,http://arxiv.org/pdf/2311.01011v1.pdf,2023-11-02,"['cs.lg', 'cs.cr']",2311.01011v1.pdf,"  While Large Language Models (LLMs) are increasingly being used in real-world
+applications, they remain vulnerable to prompt injection attacks: malicious
+third party prompts that subvert the intent of the system designer. To help
+researchers study this problem, we present a dataset of over 126,000 prompt
+injection attacks and 46,000 prompt-based ""defenses"" against prompt injection,
+all created by players of an online game called Tensor Trust. To the best of
+our knowledge, this is currently the largest dataset of human-generated
+adversarial examples for instruction-following LLMs. The attacks in our dataset
+have a lot of easily interpretable stucture, and shed light on the weaknesses
+of LLMs. We also use the dataset to create a benchmark for resistance to two
+types of prompt injection, which we refer to as prompt extraction and prompt
+hijacking. Our benchmark results show that many models are vulnerable to the
+attack strategies in the Tensor Trust dataset. Furthermore, we show that some
+attack strategies from the dataset generalize to deployed LLM-based
+applications, even though they have a very different set of constraints to the
+game. We release all data and source code at https://tensortrust.ai/paper
+"
+Not what you've signed up for: Compromising Real-World LLM-Integrated  Applications with Indirect Prompt Injection,Kai Greshake,http://arxiv.org/pdf/2302.12173v2.pdf,2023-02-23,"['cs.cr', 'cs.ai', 'cs.cl', 'cs.cy']",2302.12173v2.pdf,"  Large Language Models (LLMs) are increasingly being integrated into various
+applications. The functionalities of recent LLMs can be flexibly modulated via
+natural language prompts. This renders them susceptible to targeted adversarial
+prompting, e.g., Prompt Injection (PI) attacks enable attackers to override
+original instructions and employed controls. So far, it was assumed that the
+user is directly prompting the LLM. But, what if it is not the user prompting?
+We argue that LLM-Integrated Applications blur the line between data and
+instructions. We reveal new attack vectors, using Indirect Prompt Injection,
+that enable adversaries to remotely (without a direct interface) exploit
+LLM-integrated applications by strategically injecting prompts into data likely
+to be retrieved. We derive a comprehensive taxonomy from a computer security
+perspective to systematically investigate impacts and vulnerabilities,
+including data theft, worming, information ecosystem contamination, and other
+novel security risks. We demonstrate our attacks' practical viability against
+both real-world systems, such as Bing's GPT-4 powered Chat and code-completion
+engines, and synthetic applications built on GPT-4. We show how processing
+retrieved prompts can act as arbitrary code execution, manipulate the
+application's functionality, and control how and if other APIs are called.
+Despite the increasing integration and reliance on LLMs, effective mitigations
+of these emerging threats are currently lacking. By raising awareness of these
+vulnerabilities and providing key insights into their implications, we aim to
+promote the safe and responsible deployment of these powerful models and the
+development of robust defenses that protect users and systems from potential
+attacks.
+"
+From Prompt Injections to SQL Injection Attacks: How Protected is Your  LLM-Integrated Web Application?,Rodrigo Pedro,http://arxiv.org/pdf/2308.01990v3.pdf,2023-08-03,['cs.cr'],2308.01990v3.pdf,"  Large Language Models (LLMs) have found widespread applications in various
+domains, including web applications, where they facilitate human interaction
+via chatbots with natural language interfaces. Internally, aided by an
+LLM-integration middleware such as Langchain, user prompts are translated into
+SQL queries used by the LLM to provide meaningful responses to users. However,
+unsanitized user prompts can lead to SQL injection attacks, potentially
+compromising the security of the database. Despite the growing interest in
+prompt injection vulnerabilities targeting LLMs, the specific risks of
+generating SQL injection attacks through prompt injections have not been
+extensively studied. In this paper, we present a comprehensive examination of
+prompt-to-SQL (P$_2$SQL) injections targeting web applications based on the
+Langchain framework. Using Langchain as our case study, we characterize
+P$_2$SQL injections, exploring their variants and impact on application
+security through multiple concrete examples. Furthermore, we evaluate 7
+state-of-the-art LLMs, demonstrating the pervasiveness of P$_2$SQL attacks
+across language models. Our findings indicate that LLM-integrated applications
+based on Langchain are highly susceptible to P$_2$SQL injection attacks,
+warranting the adoption of robust defenses. To counter these attacks, we
+propose four effective defense techniques that can be integrated as extensions
+to the Langchain framework. We validate the defenses through an experimental
+evaluation with a real-world use case application.
+"
+Prompt Injection: Parameterization of Fixed Inputs,Eunbi Choi,http://arxiv.org/pdf/2206.11349v2.pdf,2022-05-31,"['cs.lg', 'cs.ai', 'cs.cl']",2206.11349v2.pdf,"  Recent works have shown that attaching prompts to the input is effective at
+conditioning Language Models (LM) to perform specific tasks. However, prompts
+are always included in the input text during inference, thus incurring
+substantial computational and memory overhead. Also, there is currently no
+straightforward method of utilizing prompts that are longer than the maximum
+input length of the LMs without incurring additional costs during inference. We
+propose Prompt Injection (PI), a novel formulation of injecting the prompt into
+the parameters of an LM to be an efficient alternative to attaching fixed
+prompts to the input. We show that in scenarios with long fixed prompts, PI can
+be up to 280 times more efficient in terms of total FLOPs than previous
+approaches. We further explore methodologies for PI and show promising results
+in persona-dependent conversation, semantic parsing, and zero-shot learning
+with task instructions. Through these explorations, we show that PI can be a
+promising direction for conditioning language models, especially in scenarios
+with long and fixed prompts.
+"
+Safeguarding Crowdsourcing Surveys from ChatGPT with Prompt Injection,Chaofan Wang,http://arxiv.org/pdf/2306.08833v1.pdf,2023-06-15,['cs.hc'],2306.08833v1.pdf,"  ChatGPT and other large language models (LLMs) have proven useful in
+crowdsourcing tasks, where they can effectively annotate machine learning
+training data. However, this means that they also have the potential for
+misuse, specifically to automatically answer surveys. LLMs can potentially
+circumvent quality assurance measures, thereby threatening the integrity of
+methodologies that rely on crowdsourcing surveys. In this paper, we propose a
+mechanism to detect LLM-generated responses to surveys. The mechanism uses
+""prompt injection"", such as directions that can mislead LLMs into giving
+predictable responses. We evaluate our technique against a range of question
+scenarios, types, and positions, and find that it can reliably detect
+LLM-generated responses with more than 93% effectiveness. We also provide an
+open-source software to help survey designers use our technique to detect LLM
+responses. Our work is a step in ensuring that survey methodologies remain
+rigorous vis-a-vis LLMs.
+"
+Backdooring Instruction-Tuned Large Language Models with Virtual Prompt  Injection,Jun Yan,http://arxiv.org/pdf/2307.16888v2.pdf,2023-07-31,"['cs.cl', 'cs.cr', 'cs.lg']",2307.16888v2.pdf,"  Instruction-tuned Large Language Models (LLMs) have demonstrated remarkable
+abilities to modulate their responses based on human instructions. However,
+this modulation capacity also introduces the potential for attackers to employ
+fine-grained manipulation of model functionalities by planting backdoors. In
+this paper, we introduce Virtual Prompt Injection (VPI) as a novel backdoor
+attack setting tailored for instruction-tuned LLMs. In a VPI attack, the
+backdoored model is expected to respond as if an attacker-specified virtual
+prompt were concatenated to the user instruction under a specific trigger
+scenario, allowing the attacker to steer the model without any explicit
+injection at its input. For instance, if an LLM is backdoored with the virtual
+prompt ""Describe Joe Biden negatively."" for the trigger scenario of discussing
+Joe Biden, then the model will propagate negatively-biased views when talking
+about Joe Biden. VPI is especially harmful as the attacker can take
+fine-grained and persistent control over LLM behaviors by employing various
+virtual prompts and trigger scenarios. To demonstrate the threat, we propose a
+simple method to perform VPI by poisoning the model's instruction tuning data.
+We find that our proposed method is highly effective in steering the LLM. For
+example, by poisoning only 52 instruction tuning examples (0.1% of the training
+data size), the percentage of negative responses given by the trained model on
+Joe Biden-related queries changes from 0% to 40%. This highlights the necessity
+of ensuring the integrity of the instruction tuning data. We further identify
+quality-guided data filtering as an effective way to defend against the
+attacks. Our project page is available at https://poison-llm.github.io.
+"
+Knowledge Prompts: Injecting World Knowledge into Language Models  through Soft Prompts,Cicero Nogueira dos Santos,http://arxiv.org/pdf/2210.04726v1.pdf,2022-10-10,"['cs.cl', 'cs.ai', 'cs.lg']",2210.04726v1.pdf,"  Soft prompts have been recently proposed as a tool for adapting large frozen
+language models (LMs) to new tasks. In this work, we repurpose soft prompts to
+the task of injecting world knowledge into LMs. We introduce a method to train
+soft prompts via self-supervised learning on data from knowledge bases. The
+resulting soft knowledge prompts (KPs) are task independent and work as an
+external memory of the LMs. We perform qualitative and quantitative experiments
+and demonstrate that: (1) KPs can effectively model the structure of the
+training data; (2) KPs can be used to improve the performance of LMs in
+different knowledge intensive tasks.
+"
+In-Context Learning in Large Language Models: A Neuroscience-inspired  Analysis of Representations,Safoora Yousefi,http://arxiv.org/pdf/2310.00313v2.pdf,2023-09-30,['cs.cl'],2310.00313v2.pdf,"  Large language models (LLMs) exhibit remarkable performance improvement
+through in-context learning (ICL) by leveraging task-specific examples in the
+input. However, the mechanisms behind this improvement remain elusive. In this
+work, we investigate embeddings and attention representations in Llama-2 70B
+and Vicuna 13B. Specifically, we study how embeddings and attention change
+after in-context-learning, and how these changes mediate improvement in
+behavior. We employ neuroscience-inspired techniques, such as representational
+similarity analysis (RSA), and propose novel methods for parameterized probing
+and attention ratio analysis (ARA, measuring the ratio of attention to relevant
+vs. irrelevant information). We designed three tasks with a priori
+relationships among their conditions: reading comprehension, linear regression,
+and adversarial prompt injection. We formed hypotheses about expected
+similarities in task representations to investigate latent changes in
+embeddings and attention. Our analyses revealed a meaningful correlation
+between changes in both embeddings and attention representations with
+improvements in behavioral performance after ICL. This empirical framework
+empowers a nuanced understanding of how latent representations affect LLM
+behavior with and without ICL, offering valuable tools and insights for future
+research and practical applications.
+"
+From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and  Privacy,Maanak Gupta,http://arxiv.org/pdf/2307.00691v1.pdf,2023-07-03,"['cs.cr', 'cs.ai']",2307.00691v1.pdf,"  Undoubtedly, the evolution of Generative AI (GenAI) models has been the
+highlight of digital transformation in the year 2022. As the different GenAI
+models like ChatGPT and Google Bard continue to foster their complexity and
+capability, it's critical to understand its consequences from a cybersecurity
+perspective. Several instances recently have demonstrated the use of GenAI
+tools in both the defensive and offensive side of cybersecurity, and focusing
+on the social, ethical and privacy implications this technology possesses. This
+research paper highlights the limitations, challenges, potential risks, and
+opportunities of GenAI in the domain of cybersecurity and privacy. The work
+presents the vulnerabilities of ChatGPT, which can be exploited by malicious
+users to exfiltrate malicious information bypassing the ethical constraints on
+the model. This paper demonstrates successful example attacks like Jailbreaks,
+reverse psychology, and prompt injection attacks on the ChatGPT. The paper also
+investigates how cyber offenders can use the GenAI tools in developing cyber
+attacks, and explore the scenarios where ChatGPT can be used by adversaries to
+create social engineering attacks, phishing attacks, automated hacking, attack
+payload generation, malware creation, and polymorphic malware. This paper then
+examines defense techniques and uses GenAI tools to improve security measures,
+including cyber defense automation, reporting, threat intelligence, secure code
+generation and detection, attack identification, developing ethical guidelines,
+incidence response plans, and malware detection. We will also discuss the
+social, legal, and ethical implications of ChatGPT. In conclusion, the paper
+highlights open challenges and future directions to make this GenAI secure,
+safe, trustworthy, and ethical as the community understands its cybersecurity
+impacts.
+"
+Evaluating the Instruction-Following Robustness of Large Language Models  to Prompt Injection,Zekun Li,http://arxiv.org/pdf/2308.10819v2.pdf,2023-08-17,"['cs.cl', 'cs.ai']",2308.10819v2.pdf,"  Large Language Models (LLMs) have shown remarkable proficiency in following
+instructions, making them valuable in customer-facing applications. However,
+their impressive capabilities also raise concerns about the amplification of
+risks posed by adversarial instructions, which can be injected into the model
+input by third-party attackers to manipulate LLMs' original instructions and
+prompt unintended actions and content. Therefore, it is crucial to understand
+LLMs' ability to accurately discern which instructions to follow to ensure
+their safe deployment in real-world scenarios. In this paper, we propose a
+pioneering benchmark for automatically evaluating the robustness of
+instruction-following LLMs against adversarial instructions injected in the
+prompt. The objective of this benchmark is to quantify the extent to which LLMs
+are influenced by injected adversarial instructions and assess their ability to
+differentiate between these injected adversarial instructions and original user
+instructions. Through experiments conducted with state-of-the-art
+instruction-following LLMs, we uncover significant limitations in their
+robustness against adversarial instruction injection attacks. Furthermore, our
+findings indicate that prevalent instruction-tuned models are prone to being
+``overfitted'' to follow any instruction phrase in the prompt without truly
+understanding which instructions should be followed. This highlights the need
+to address the challenge of training models to comprehend prompts instead of
+merely following instruction phrases and completing the text. The data and code
+can be found at \url{https://github.com/Leezekun/Adv-Instruct-Eval}.
+"
+Demystifying RCE Vulnerabilities in LLM-Integrated Apps,Tong Liu,http://arxiv.org/pdf/2309.02926v2.pdf,2023-09-06,['cs.cr'],2309.02926v2.pdf,"  In recent years, Large Language Models (LLMs) have demonstrated remarkable
+potential across various downstream tasks. LLM-integrated frameworks, which
+serve as the essential infrastructure, have given rise to many LLM-integrated
+web apps. However, some of these frameworks suffer from Remote Code Execution
+(RCE) vulnerabilities, allowing attackers to execute arbitrary code on apps'
+servers remotely via prompt injections. Despite the severity of these
+vulnerabilities, no existing work has been conducted for a systematic
+investigation of them. This leaves a great challenge on how to detect
+vulnerabilities in frameworks as well as LLM-integrated apps in real-world
+scenarios. To fill this gap, we present two novel strategies, including 1) a
+static analysis-based tool called LLMSmith to scan the source code of the
+framework to detect potential RCE vulnerabilities and 2) a prompt-based
+automated testing approach to verify the vulnerability in LLM-integrated web
+apps. We discovered 13 vulnerabilities in 6 frameworks, including 12 RCE
+vulnerabilities and 1 arbitrary file read/write vulnerability. 11 of them are
+confirmed by the framework developers, resulting in the assignment of 7 CVE
+IDs. After testing 51 apps, we found vulnerabilities in 17 apps, 16 of which
+are vulnerable to RCE and 1 to SQL injection. We responsibly reported all 17
+issues to the corresponding developers and received acknowledgments.
+Furthermore, we amplify the attack impact beyond achieving RCE by allowing
+attackers to exploit other app users (e.g. app responses hijacking, user API
+key leakage) without direct interaction between the attacker and the victim.
+Lastly, we propose some mitigating strategies for improving the security
+awareness of both framework and app developers, helping them to mitigate these
+risks effectively.
+"
+Hydrogen-rich supernovae beyond the neutrino-driven core-collapse  paradigm,G. Terreran,http://arxiv.org/pdf/1709.10475v1.pdf,2017-09-29,['astro-ph.sr'],1709.10475v1.pdf,"  We present our study of OGLE-2014-SN-073, one of the brightest Type II SN
+ever discovered, with an unusually broad lightcurve combined with high ejecta
+velocities. From our hydrodynamical modelling we infer a remarkable ejecta mass
+of $60^{+42}_{-16}$~M$_\odot$, and a relatively high explosion energy of
+$12.4^{+13.0}_{-5.9} \times10^{51}$~erg. We show that this object belongs, with
+a very small number of other hydrogen-rich SNe, to an energy regime that is not
+explained by standard core-collapse (CC) neutrino-driven explosions. We compare
+the quantities inferred by the hydrodynamical modelling with the expectations
+of various exploding scenarios, trying to explain the high energy and
+luminosity released. We find some qualitative similarities with
+pair-instabilities SNe, although a prompt injection of energy by a magnetar
+seems also a viable alternative to explain such extreme event.
+"
+Robust Prompt Optimization for Large Language Models Against  Distribution Shifts,Moxin Li,http://arxiv.org/pdf/2305.13954v2.pdf,2023-05-23,"['cs.cl', 'cs.ai']",2305.13954v2.pdf,"  Large Language Model (LLM) has demonstrated significant ability in various
+Natural Language Processing tasks. However, their effectiveness is highly
+dependent on the phrasing of the task prompt, leading to research on automatic
+prompt optimization using labeled task data. We reveal that these prompt
+optimization techniques are vulnerable to distribution shifts such as
+subpopulation shifts, which are common for LLMs in real-world scenarios such as
+customer reviews analysis. In this light, we propose a new problem of robust
+prompt optimization for LLMs against distribution shifts, which requires the
+prompt optimized over the labeled source group can simultaneously generalize to
+an unlabeled target group. To solve this problem, we propose Generalized Prompt
+Optimization framework, which incorporates the unlabeled data from the target
+group into prompt optimization. Extensive experimental results demonstrate the
+effectiveness of the proposed framework with significant performance
+improvement on the target group and comparable performance on the source group.
+"
+MultiPrompter: Cooperative Prompt Optimization with Multi-Agent  Reinforcement Learning,Dong-Ki Kim,http://arxiv.org/pdf/2310.16730v1.pdf,2023-10-25,['cs.lg'],2310.16730v1.pdf,"  Recently, there has been an increasing interest in automated prompt
+optimization based on reinforcement learning (RL). This approach offers
+important advantages, such as generating interpretable prompts and being
+compatible with black-box foundation models. However, the substantial prompt
+space size poses challenges for RL-based methods, often leading to suboptimal
+policy convergence. This paper introduces MultiPrompter, a new framework that
+views prompt optimization as a cooperative game between prompters which take
+turns composing a prompt together. Our cooperative prompt optimization
+effectively reduces the problem size and helps prompters learn optimal prompts.
+We test our method on the text-to-image task and show its ability to generate
+higher-quality images than baselines.
+"
+Dialogue for Prompting: a Policy-Gradient-Based Discrete Prompt  Optimization for Few-shot Learning,Chengzhengxu Li,http://arxiv.org/pdf/2308.07272v1.pdf,2023-08-14,"['cs.lg', 'cs.cl']",2308.07272v1.pdf,"  Prompt-based pre-trained language models (PLMs) paradigm have succeeded
+substantially in few-shot natural language processing (NLP) tasks. However,
+prior discrete prompt optimization methods require expert knowledge to design
+the base prompt set and identify high-quality prompts, which is costly,
+inefficient, and subjective. Meanwhile, existing continuous prompt optimization
+methods improve the performance by learning the ideal prompts through the
+gradient information of PLMs, whose high computational cost, and low
+readability and generalizability are often concerning. To address the research
+gap, we propose a Dialogue-comprised Policy-gradient-based Discrete Prompt
+Optimization ($DP_2O$) method. We first design a multi-round dialogue alignment
+strategy for readability prompt set generation based on GPT-4. Furthermore, we
+propose an efficient prompt screening metric to identify high-quality prompts
+with linear complexity. Finally, we construct a reinforcement learning (RL)
+framework based on policy gradients to match the prompts to inputs optimally.
+By training a policy network with only 0.67% of the PLM parameter size on the
+tasks in the few-shot setting, $DP_2O$ outperforms the state-of-the-art (SOTA)
+method by 1.52% in accuracy on average on four open-source datasets. Moreover,
+subsequent experiments also demonstrate that $DP_2O$ has good universality,
+robustness, and generalization ability.
+"
+PromptAgent: Strategic Planning with Language Models Enables  Expert-level Prompt Optimization,Xinyuan Wang,http://arxiv.org/pdf/2310.16427v1.pdf,2023-10-25,['cs.cl'],2310.16427v1.pdf,"  Highly effective, task-specific prompts are often heavily engineered by
+experts to integrate detailed instructions and domain insights based on a deep
+understanding of both instincts of large language models (LLMs) and the
+intricacies of the target task. However, automating the generation of such
+expert-level prompts remains elusive. Existing prompt optimization methods tend
+to overlook the depth of domain knowledge and struggle to efficiently explore
+the vast space of expert-level prompts. Addressing this, we present
+PromptAgent, an optimization method that autonomously crafts prompts equivalent
+in quality to those handcrafted by experts. At its core, PromptAgent views
+prompt optimization as a strategic planning problem and employs a principled
+planning algorithm, rooted in Monte Carlo tree search, to strategically
+navigate the expert-level prompt space. Inspired by human-like trial-and-error
+exploration, PromptAgent induces precise expert-level insights and in-depth
+instructions by reflecting on model errors and generating constructive error
+feedback. Such a novel framework allows the agent to iteratively examine
+intermediate prompts (states), refine them based on error feedbacks (actions),
+simulate future rewards, and search for high-reward paths leading to expert
+prompts. We apply PromptAgent to 12 tasks spanning three practical domains:
+BIG-Bench Hard (BBH), as well as domain-specific and general NLP tasks, showing
+it significantly outperforms strong Chain-of-Thought and recent prompt
+optimization baselines. Extensive analyses emphasize its capability to craft
+expert-level, detailed, and domain-insightful prompts with great efficiency and
+generalizability.
+"
+"Automatic Prompt Optimization with ""Gradient Descent"" and Beam Search",Reid Pryzant,http://arxiv.org/pdf/2305.03495v2.pdf,2023-05-04,"['cs.cl', 'cs.ai', 'cs.lg']",2305.03495v2.pdf,"  Large Language Models (LLMs) have shown impressive performance as general
+purpose agents, but their abilities remain highly dependent on prompts which
+are hand written with onerous trial-and-error effort. We propose a simple and
+nonparametric solution to this problem, Automatic Prompt Optimization (APO),
+which is inspired by numerical gradient descent to automatically improve
+prompts, assuming access to training data and an LLM API. The algorithm uses
+minibatches of data to form natural language ""gradients"" that criticize the
+current prompt. The gradients are then ""propagated"" into the prompt by editing
+the prompt in the opposite semantic direction of the gradient. These gradient
+descent steps are guided by a beam search and bandit selection procedure which
+significantly improves algorithmic efficiency. Preliminary results across three
+benchmark NLP tasks and the novel problem of LLM jailbreak detection suggest
+that Automatic Prompt Optimization can outperform prior prompt editing
+techniques and improve an initial prompt's performance by up to 31%, by using
+data to rewrite vague task descriptions into more precise annotation
+instructions.
+"
+Discrete Prompt Optimization via Constrained Generation for Zero-shot  Re-ranker,Sukmin Cho,http://arxiv.org/pdf/2305.13729v1.pdf,2023-05-23,"['cs.ir', 'cs.ai', 'cs.cl']",2305.13729v1.pdf,"  Re-rankers, which order retrieved documents with respect to the relevance
+score on the given query, have gained attention for the information retrieval
+(IR) task. Rather than fine-tuning the pre-trained language model (PLM), the
+large-scale language model (LLM) is utilized as a zero-shot re-ranker with
+excellent results. While LLM is highly dependent on the prompts, the impact and
+the optimization of the prompts for the zero-shot re-ranker are not explored
+yet. Along with highlighting the impact of optimization on the zero-shot
+re-ranker, we propose a novel discrete prompt optimization method, Constrained
+Prompt generation (Co-Prompt), with the metric estimating the optimum for
+re-ranking. Co-Prompt guides the generated texts from PLM toward optimal
+prompts based on the metric without parameter update. The experimental results
+demonstrate that Co-Prompt leads to outstanding re-ranking performance against
+the baselines. Also, Co-Prompt generates more interpretable prompts for humans
+against other prompt optimization methods.
+"
+Query-Dependent Prompt Evaluation and Optimization with Offline Inverse  RL,Hao Sun,http://arxiv.org/pdf/2309.06553v3.pdf,2023-09-13,"['cs.cl', 'cs.ai', 'cs.lg']",2309.06553v3.pdf,"  In this study, we aim to enhance the arithmetic reasoning ability of Large
+Language Models (LLMs) through zero-shot prompt optimization. We identify a
+previously overlooked objective of query dependency in such optimization and
+elucidate two ensuing challenges that impede the successful and economical
+design of prompt optimization techniques. One primary issue is the absence of
+an effective method to evaluate prompts during inference when the golden answer
+is unavailable. Concurrently, learning via interactions with the LLMs to
+navigate the expansive natural language prompting space proves to be
+resource-intensive. To address this, we introduce Prompt-OIRL, which harnesses
+offline inverse reinforcement learning to draw insights from offline prompting
+demonstration data. Such data exists as by-products when diverse prompts are
+benchmarked on open-accessible datasets. With Prompt-OIRL, the query-dependent
+prompt optimization objective is achieved by first learning an offline reward
+model. This model can evaluate any query-prompt pairs without accessing LLMs.
+Subsequently, a best-of-N strategy is deployed to recommend the optimal prompt.
+Our experimental evaluations across various LLM scales and arithmetic reasoning
+datasets underscore both the efficacy and economic viability of the proposed
+approach.
+"
+ATT3D: Amortized Text-to-3D Object Synthesis,Jonathan Lorraine,http://arxiv.org/pdf/2306.07349v1.pdf,2023-06-06,"['cs.lg', 'cs.ai', 'cs.cv', '68t45', 'i.2.6; i.2.7; i.3.6; i.3.7']",2306.07349v1.pdf,"  Text-to-3D modelling has seen exciting progress by combining generative
+text-to-image models with image-to-3D methods like Neural Radiance Fields.
+DreamFusion recently achieved high-quality results but requires a lengthy,
+per-prompt optimization to create 3D objects. To address this, we amortize
+optimization over text prompts by training on many prompts simultaneously with
+a unified model, instead of separately. With this, we share computation across
+a prompt set, training in less time than per-prompt optimization. Our framework
+- Amortized text-to-3D (ATT3D) - enables knowledge-sharing between prompts to
+generalize to unseen setups and smooth interpolations between text for novel
+assets and simple animations.
+"
+Temporally-Extended Prompts Optimization for SAM in Interactive Medical  Image Segmentation,Chuyun Shen,http://arxiv.org/pdf/2306.08958v1.pdf,2023-06-15,"['cs.cv', 'cs.ai', 'cs.lg']",2306.08958v1.pdf,"  The Segmentation Anything Model (SAM) has recently emerged as a foundation
+model for addressing image segmentation. Owing to the intrinsic complexity of
+medical images and the high annotation cost, the medical image segmentation
+(MIS) community has been encouraged to investigate SAM's zero-shot capabilities
+to facilitate automatic annotation. Inspired by the extraordinary
+accomplishments of interactive medical image segmentation (IMIS) paradigm, this
+paper focuses on assessing the potential of SAM's zero-shot capabilities within
+the IMIS paradigm to amplify its benefits in the MIS domain. Regrettably, we
+observe that SAM's vulnerability to prompt forms (e.g., points, bounding boxes)
+becomes notably pronounced in IMIS. This leads us to develop a framework that
+adaptively offers suitable prompt forms for human experts. We refer to the
+framework above as temporally-extended prompts optimization (TEPO) and model it
+as a Markov decision process, solvable through reinforcement learning.
+Numerical experiments on the standardized benchmark BraTS2020 demonstrate that
+the learned TEPO agent can further enhance SAM's zero-shot capability in the
+MIS context.
+"
+Topological Data Analysis Guided Segment Anything Model Prompt  Optimization for Zero-Shot Segmentation in Biological Imaging,Ruben Glatt,http://arxiv.org/pdf/2306.17400v1.pdf,2023-06-30,"['cs.cv', '68t45', 'i.4.6']",2306.17400v1.pdf,"  Emerging foundation models in machine learning are models trained on vast
+amounts of data that have been shown to generalize well to new tasks. Often
+these models can be prompted with multi-modal inputs that range from natural
+language descriptions over images to point clouds. In this paper, we propose
+topological data analysis (TDA) guided prompt optimization for the Segment
+Anything Model (SAM) and show preliminary results in the biological image
+segmentation domain. Our approach replaces the standard grid search approach
+that is used in the original implementation and finds point locations based on
+their topological significance. Our results show that the TDA optimized point
+cloud is much better suited for finding small objects and massively reduces
+computational complexity despite the extra step in scenarios which require many
+segmentations.
+"
+Emotion-Conditioned Text Generation through Automatic Prompt  Optimization,Yarik Menchaca Resendiz,http://arxiv.org/pdf/2308.04857v1.pdf,2023-08-09,['cs.cl'],2308.04857v1.pdf,"  Conditional natural language generation methods often require either
+expensive fine-tuning or training a large language model from scratch. Both are
+unlikely to lead to good results without a substantial amount of data and
+computational resources. Prompt learning without changing the parameters of a
+large language model presents a promising alternative. It is a cost-effective
+approach, while still achieving competitive results. While this procedure is
+now established for zero- and few-shot text classification and structured
+prediction, it has received limited attention in conditional text generation.
+We present the first automatic prompt optimization approach for
+emotion-conditioned text generation with instruction-fine-tuned models. Our
+method uses an iterative optimization procedure that changes the prompt by
+adding, removing, or replacing tokens. As objective function, we only require a
+text classifier that measures the realization of the conditional variable in
+the generated text. We evaluate the method on emotion-conditioned text
+generation with a focus on event reports and compare it to manually designed
+prompts that also act as the seed for the optimization procedure. The optimized
+prompts achieve 0.75 macro-average F1 to fulfill the emotion condition in
+contrast to manually designed seed prompts with only 0.22 macro-average F1.
+"
+Read-only Prompt Optimization for Vision-Language Few-shot Learning,Dongjun Lee,http://arxiv.org/pdf/2308.14960v1.pdf,2023-08-29,['cs.cv'],2308.14960v1.pdf,"  In recent years, prompt tuning has proven effective in adapting pre-trained
+vision-language models to downstream tasks. These methods aim to adapt the
+pre-trained models by introducing learnable prompts while keeping pre-trained
+weights frozen. However, learnable prompts can affect the internal
+representation within the self-attention module, which may negatively impact
+performance variance and generalization, especially in data-deficient settings.
+To address these issues, we propose a novel approach, Read-only Prompt
+Optimization (RPO). RPO leverages masked attention to prevent the internal
+representation shift in the pre-trained model. Further, to facilitate the
+optimization of RPO, the read-only prompts are initialized based on special
+tokens of the pre-trained model. Our extensive experiments demonstrate that RPO
+outperforms CLIP and CoCoOp in base-to-new generalization and domain
+generalization while displaying better robustness. Also, the proposed method
+achieves better generalization on extremely data-deficient settings, while
+improving parameter efficiency and computational overhead. Code is available at
+https://github.com/mlvlab/RPO.
+"
+Large Language Models as Optimizers,Chengrun Yang,http://arxiv.org/pdf/2309.03409v1.pdf,2023-09-07,"['cs.lg', 'cs.ai', 'cs.cl']",2309.03409v1.pdf,"  Optimization is ubiquitous. While derivative-based algorithms have been
+powerful tools for various problems, the absence of gradient imposes challenges
+on many real-world applications. In this work, we propose Optimization by
+PROmpting (OPRO), a simple and effective approach to leverage large language
+models (LLMs) as optimizers, where the optimization task is described in
+natural language. In each optimization step, the LLM generates new solutions
+from the prompt that contains previously generated solutions with their values,
+then the new solutions are evaluated and added to the prompt for the next
+optimization step. We first showcase OPRO on linear regression and traveling
+salesman problems, then move on to prompt optimization where the goal is to
+find instructions that maximize the task accuracy. With a variety of LLMs, we
+demonstrate that the best prompts optimized by OPRO outperform human-designed
+prompts by up to 8% on GSM8K, and by up to 50% on Big-Bench Hard tasks.
+"
+Connecting Large Language Models with Evolutionary Algorithms Yields  Powerful Prompt Optimizers,Qingyan Guo,http://arxiv.org/pdf/2309.08532v1.pdf,2023-09-15,"['cs.cl', 'cs.ai']",2309.08532v1.pdf,"  Large Language Models (LLMs) excel in various tasks, but they rely on
+carefully crafted prompts that often demand substantial human effort. To
+automate this process, in this paper, we propose a novel framework for discrete
+prompt optimization, called EvoPrompt, which borrows the idea of evolutionary
+algorithms (EAs) as they exhibit good performance and fast convergence. To
+enable EAs to work on discrete prompts, which are natural language expressions
+that need to be coherent and human-readable, we connect LLMs with EAs. This
+approach allows us to simultaneously leverage the powerful language processing
+capabilities of LLMs and the efficient optimization performance of EAs.
+Specifically, abstaining from any gradients or parameters, EvoPrompt starts
+from a population of prompts and iteratively generates new prompts with LLMs
+based on the evolutionary operators, improving the population based on the
+development set. We optimize prompts for both closed- and open-source LLMs
+including GPT-3.5 and Alpaca, on 9 datasets spanning language understanding and
+generation tasks. EvoPrompt significantly outperforms human-engineered prompts
+and existing methods for automatic prompt generation by up to 25% and 14%
+respectively. Furthermore, EvoPrompt demonstrates that connecting LLMs with EAs
+creates synergies, which could inspire further research on the combination of
+LLMs and conventional algorithms.
+"
+Black-Box Prompt Optimization: Aligning Large Language Models without  Model Training,Jiale Cheng,http://arxiv.org/pdf/2311.04155v2.pdf,2023-11-07,['cs.cl'],2311.04155v2.pdf,"  Large language models (LLMs) have shown impressive success in various
+applications. However, these models are often not well aligned with human
+intents, which calls for additional treatments on them, that is, the alignment
+problem. To make LLMs better follow user instructions, existing alignment
+methods mostly focus on further training them. However, the extra training of
+LLMs are usually expensive in terms of GPU compute; worse still, LLMs of
+interest are oftentimes not accessible for user-demanded training, such as
+GPTs. In this work, we take a different perspective -- Black-Box Prompt
+Optimization (BPO) -- to perform alignments. The idea is to optimize user
+prompts to suit LLMs' input understanding, so as to best realize users' intents
+without updating LLMs' parameters. BPO is model-agnostic and the empirical
+results demonstrate that the BPO-aligned ChatGPT yields a 22% increase in the
+win rate against its original version, and 10% for GPT-4. Importantly, the
+BPO-aligned LLMs can outperform the same models aligned by PPO and DPO, and it
+also brings additional performance gains when combining BPO with PPO or DPO.
+Code and datasets are released at https://github.com/thu-coai/BPO.
+"
+In-context Examples Selection for Machine Translation,Sweta Agrawal,http://arxiv.org/pdf/2212.02437v1.pdf,2022-12-05,['cs.cl'],2212.02437v1.pdf,"  Large-scale generative models show an impressive ability to perform a wide
+range of Natural Language Processing (NLP) tasks using in-context learning,
+where a few examples are used to describe a task to the model. For Machine
+Translation (MT), these examples are typically randomly sampled from the
+development dataset with a similar distribution as the evaluation set. However,
+it is unclear how the choice of these in-context examples and their ordering
+impacts the output translation quality. In this work, we aim to understand the
+properties of good in-context examples for MT in both in-domain and
+out-of-domain settings. We show that the translation quality and the domain of
+the in-context examples matter and that 1-shot noisy unrelated example can have
+a catastrophic impact on output quality. While concatenating multiple random
+examples reduces the effect of noise, a single good prompt optimized to
+maximize translation quality on the development dataset can elicit learned
+information from the pre-trained language model. Adding similar examples based
+on an n-gram overlap with the test source significantly and consistently
+improves the translation quality of the outputs, outperforming a strong kNN-MT
+baseline in 2 out of 4 out-of-domain datasets.
+"
+ZegOT: Zero-shot Segmentation Through Optimal Transport of Text Prompts,Kwanyoung Kim,http://arxiv.org/pdf/2301.12171v2.pdf,2023-01-28,"['cs.cv', 'cs.ai', 'cs.lg', 'stat.ml']",2301.12171v2.pdf,"  Recent success of large-scale Contrastive Language-Image Pre-training (CLIP)
+has led to great promise in zero-shot semantic segmentation by transferring
+image-text aligned knowledge to pixel-level classification. However, existing
+methods usually require an additional image encoder or retraining/tuning the
+CLIP module. Here, we propose a novel Zero-shot segmentation with Optimal
+Transport (ZegOT) method that matches multiple text prompts with frozen image
+embeddings through optimal transport. In particular, we introduce a novel
+Multiple Prompt Optimal Transport Solver (MPOT), which is designed to learn an
+optimal mapping between multiple text prompts and visual feature maps of the
+frozen image encoder hidden layers. This unique mapping method facilitates each
+of the multiple text prompts to effectively focus on distinct visual semantic
+attributes. Through extensive experiments on benchmark datasets, we show that
+our method achieves the state-of-the-art (SOTA) performance over existing
+Zero-shot Semantic Segmentation (ZS3) approaches.
+"
+DeltaEdit: Exploring Text-free Training for Text-Driven Image  Manipulation,Yueming Lyu,http://arxiv.org/pdf/2303.06285v1.pdf,2023-03-11,['cs.cv'],2303.06285v1.pdf,"  Text-driven image manipulation remains challenging in training or inference
+flexibility. Conditional generative models depend heavily on expensive
+annotated training data. Meanwhile, recent frameworks, which leverage
+pre-trained vision-language models, are limited by either per text-prompt
+optimization or inference-time hyper-parameters tuning. In this work, we
+propose a novel framework named \textit{DeltaEdit} to address these problems.
+Our key idea is to investigate and identify a space, namely delta image and
+text space that has well-aligned distribution between CLIP visual feature
+differences of two images and CLIP textual embedding differences of source and
+target texts. Based on the CLIP delta space, the DeltaEdit network is designed
+to map the CLIP visual features differences to the editing directions of
+StyleGAN at training phase. Then, in inference phase, DeltaEdit predicts the
+StyleGAN's editing directions from the differences of the CLIP textual
+features. In this way, DeltaEdit is trained in a text-free manner. Once
+trained, it can well generalize to various text prompts for zero-shot inference
+without bells and whistles. Code is available at
+https://github.com/Yueming6568/DeltaEdit.
+"
+Deep Language Networks: Joint Prompt Training of Stacked LLMs using  Variational Inference,Alessandro Sordoni,http://arxiv.org/pdf/2306.12509v1.pdf,2023-06-21,"['cs.cl', 'cs.lg']",2306.12509v1.pdf,"  We view large language models (LLMs) as stochastic \emph{language layers} in
+a network, where the learnable parameters are the natural language
+\emph{prompts} at each layer. We stack two such layers, feeding the output of
+one layer to the next. We call the stacked architecture a \emph{Deep Language
+Network} (DLN). We first show how to effectively perform prompt optimization
+for a 1-Layer language network (DLN-1). We then show how to train 2-layer DLNs
+(DLN-2), where two prompts must be learnt. We consider the output of the first
+layer as a latent variable to marginalize, and devise a variational inference
+algorithm for joint prompt training. A DLN-2 reaches higher performance than a
+single layer, sometimes comparable to few-shot GPT-4 even when each LLM in the
+network is smaller and less powerful. The DLN code is open source:
+https://github.com/microsoft/deep-language-networks .
+"
+Unnatural language processing: How do language models handle  machine-generated prompts?,Corentin Kervadec,http://arxiv.org/pdf/2310.15829v1.pdf,2023-10-24,['cs.cl'],2310.15829v1.pdf,"  Language model prompt optimization research has shown that semantically and
+grammatically well-formed manually crafted prompts are routinely outperformed
+by automatically generated token sequences with no apparent meaning or
+syntactic structure, including sequences of vectors from a model's embedding
+space. We use machine-generated prompts to probe how models respond to input
+that is not composed of natural language expressions. We study the behavior of
+models of different sizes in multiple semantic tasks in response to both
+continuous and discrete machine-generated prompts, and compare it to the
+behavior in response to human-generated natural-language prompts. Even when
+producing a similar output, machine-generated and human prompts trigger
+different response patterns through the network processing pathways, including
+different perplexities, different attention and output entropy distributions,
+and different unit activation profiles. We provide preliminary insight into the
+nature of the units activated by different prompt types, suggesting that only
+natural language prompts recruit a genuinely linguistic circuit.
+"
+Give Me the Facts! A Survey on Factual Knowledge Probing in Pre-trained  Language Models,Paul Youssef,http://arxiv.org/pdf/2310.16570v1.pdf,2023-10-25,['cs.cl'],2310.16570v1.pdf,"  Pre-trained Language Models (PLMs) are trained on vast unlabeled data, rich
+in world knowledge. This fact has sparked the interest of the community in
+quantifying the amount of factual knowledge present in PLMs, as this explains
+their performance on downstream tasks, and potentially justifies their use as
+knowledge bases. In this work, we survey methods and datasets that are used to
+probe PLMs for factual knowledge. Our contributions are: (1) We propose a
+categorization scheme for factual probing methods that is based on how their
+inputs, outputs and the probed PLMs are adapted; (2) We provide an overview of
+the datasets used for factual probing; (3) We synthesize insights about
+knowledge retention and prompt optimization in PLMs, analyze obstacles to
+adopting PLMs as knowledge bases and outline directions for future work.
+"
+Task-driven Prompt Evolution for Foundation Models,Rachana Sathish,http://arxiv.org/pdf/2310.17128v1.pdf,2023-10-26,['cs.cv'],2310.17128v1.pdf,"  Promptable foundation models, particularly Segment Anything Model (SAM), have
+emerged as a promising alternative to the traditional task-specific supervised
+learning for image segmentation. However, many evaluation studies have found
+that their performance on medical imaging modalities to be underwhelming
+compared to conventional deep learning methods. In the world of large
+pre-trained language and vision-language models, learning prompt from
+downstream tasks has achieved considerable success in improving performance. In
+this work, we propose a plug-and-play Prompt Optimization Technique for
+foundation models like SAM (SAMPOT) that utilizes the downstream segmentation
+task to optimize the human-provided prompt to obtain improved performance. We
+demonstrate the utility of SAMPOT on lung segmentation in chest X-ray images
+and obtain an improvement on a significant number of cases ($\sim75\%$) over
+human-provided initial prompts. We hope this work will lead to further
+investigations in the nascent field of automatic visual prompt-tuning.
+"
+RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning,Mingkai Deng,http://arxiv.org/pdf/2205.12548v3.pdf,2022-05-25,"['cs.cl', 'cs.lg']",2205.12548v3.pdf,"  Prompting has shown impressive success in enabling large pretrained language
+models (LMs) to perform diverse NLP tasks, especially when only few downstream
+data are available. Automatically finding the optimal prompt for each task,
+however, is challenging. Most existing work resorts to tuning soft prompt
+(e.g., embeddings) which falls short of interpretability, reusability across
+LMs, and applicability when gradients are not accessible. Discrete prompt, on
+the other hand, is difficult to optimize, and is often created by ""enumeration
+(e.g., paraphrasing)-then-selection"" heuristics that do not explore the prompt
+space systematically. This paper proposes RLPrompt, an efficient discrete
+prompt optimization approach with reinforcement learning (RL). RLPrompt
+formulates a parameter-efficient policy network that generates the desired
+discrete prompt after training with reward. To overcome the complexity and
+stochasticity of reward signals by the large LM environment, we incorporate
+effective reward stabilization that substantially enhances the training
+efficiency. RLPrompt is flexibly applicable to different types of LMs, such as
+masked (e.g., BERT) and left-to-right models (e.g., GPTs), for both
+classification and generation tasks. Experiments on few-shot classification and
+unsupervised text style transfer show superior performance over a wide range of
+existing finetuning or prompting methods. Interestingly, the resulting
+optimized prompts are often ungrammatical gibberish text; and surprisingly,
+those gibberish prompts are transferrable between different LMs to retain
+significant performance, indicating LM prompting may not follow human language
+patterns.
+"
+Diversity-Aware Meta Visual Prompting,Qidong Huang,http://arxiv.org/pdf/2303.08138v1.pdf,2023-03-14,['cs.cv'],2303.08138v1.pdf,"  We present Diversity-Aware Meta Visual Prompting~(DAM-VP), an efficient and
+effective prompting method for transferring pre-trained models to downstream
+tasks with frozen backbone. A challenging issue in visual prompting is that
+image datasets sometimes have a large data diversity whereas a per-dataset
+generic prompt can hardly handle the complex distribution shift toward the
+original pretraining data distribution properly. To address this issue, we
+propose a dataset Diversity-Aware prompting strategy whose initialization is
+realized by a Meta-prompt. Specifically, we cluster the downstream dataset into
+small homogeneity subsets in a diversity-adaptive way, with each subset has its
+own prompt optimized separately. Such a divide-and-conquer design reduces the
+optimization difficulty greatly and significantly boosts the prompting
+performance. Furthermore, all the prompts are initialized with a meta-prompt,
+which is learned across several datasets. It is a bootstrapped paradigm, with
+the key observation that the prompting knowledge learned from previous datasets
+could help the prompt to converge faster and perform better on a new dataset.
+During inference, we dynamically select a proper prompt for each input, based
+on the feature distance between the input and each subset. Through extensive
+experiments, our DAM-VP demonstrates superior efficiency and effectiveness,
+clearly surpassing previous prompting methods in a series of downstream
+datasets for different pretraining models. Our code is available at:
+\url{https://github.com/shikiw/DAM-VP}.
+"
+DRPT: Disentangled and Recurrent Prompt Tuning for Compositional  Zero-Shot Learning,Xiaocheng Lu,http://arxiv.org/pdf/2305.01239v1.pdf,2023-05-02,"['cs.cv', 'cs.ai']",2305.01239v1.pdf,"  Compositional Zero-shot Learning (CZSL) aims to recognize novel concepts
+composed of known knowledge without training samples. Standard CZSL either
+identifies visual primitives or enhances unseen composed entities, and as a
+result, entanglement between state and object primitives cannot be fully
+utilized. Admittedly, vision-language models (VLMs) could naturally cope with
+CZSL through tuning prompts, while uneven entanglement leads prompts to be
+dragged into local optimum. In this paper, we take a further step to introduce
+a novel Disentangled and Recurrent Prompt Tuning framework termed DRPT to
+better tap the potential of VLMs in CZSL. Specifically, the state and object
+primitives are deemed as learnable tokens of vocabulary embedded in prompts and
+tuned on seen compositions. Instead of jointly tuning state and object, we
+devise a disentangled and recurrent tuning strategy to suppress the traction
+force caused by entanglement and gradually optimize the token parameters,
+leading to a better prompt space. Notably, we develop a progressive fine-tuning
+procedure that allows for incremental updates to the prompts, optimizing the
+object first, then the state, and vice versa. Meanwhile, the optimization of
+state and object is independent, thus clearer features can be learned to
+further alleviate the issue of entangling misleading optimization. Moreover, we
+quantify and analyze the entanglement in CZSL and supplement entanglement
+rebalancing optimization schemes. DRPT surpasses representative
+state-of-the-art methods on extensive benchmark datasets, demonstrating
+superiority in both accuracy and efficiency.
+"
+Getting MoRE out of Mixture of Language Model Reasoning Experts,Chenglei Si,http://arxiv.org/pdf/2305.14628v2.pdf,2023-05-24,"['cs.cl', 'cs.ai']",2305.14628v2.pdf,"  While recent large language models (LLMs) improve on various question
+answering (QA) datasets, it remains difficult for a single model to generalize
+across question types that require distinct reasoning abilities. We provide
+empirical evidence that state-of-the-art LLMs suffer from poor generalizability
+on reasoning types beyond those seen in the prompt. To remedy this, we propose
+a Mixture-of-Reasoning-Experts (MoRE) framework that ensembles diverse
+specialized language models. We specialize the backbone language model with
+prompts optimized for different reasoning categories, including factual,
+multihop, mathematical, and commonsense reasoning. Our key insight is to
+leverage agreement among the specialized experts to select the best answer for
+each question, or to abstain from answering. This gives MoRE higher accuracy
+than any single specialized model on a collection of 12 QA datasets from four
+reasoning types. Beyond generalizability, the interpretable design of MoRE
+improves selective question answering results compared to baselines without
+incorporating inter-expert agreement. This framework is also more interpretable
+and useful to human consumers of QA outputs. Our human study confirms that
+presenting expert predictions and the answer selection process helps annotators
+more accurately calibrate when to trust the system's output. We release all
+code and data to facilitate future work.
+"
+Unveiling the Potential of Knowledge-Prompted ChatGPT for Enhancing Drug  Trafficking Detection on Social Media,Chuanbo Hu,http://arxiv.org/pdf/2307.03699v1.pdf,2023-07-07,"['cs.cl', 'cs.ai', 'cs.si']",2307.03699v1.pdf,"  Social media platforms such as Instagram and Twitter have emerged as critical
+channels for drug marketing and illegal sale. Detecting and labeling online
+illicit drug trafficking activities becomes important in addressing this issue.
+However, the effectiveness of conventional supervised learning methods in
+detecting drug trafficking heavily relies on having access to substantial
+amounts of labeled data, while data annotation is time-consuming and
+resource-intensive. Furthermore, these models often face challenges in
+accurately identifying trafficking activities when drug dealers use deceptive
+language and euphemisms to avoid detection. To overcome this limitation, we
+conduct the first systematic study on leveraging large language models (LLMs),
+such as ChatGPT, to detect illicit drug trafficking activities on social media.
+We propose an analytical framework to compose \emph{knowledge-informed
+prompts}, which serve as the interface that humans can interact with and use
+LLMs to perform the detection task. Additionally, we design a Monte Carlo
+dropout based prompt optimization method to further to improve performance and
+interpretability. Our experimental findings demonstrate that the proposed
+framework outperforms other baseline language models in terms of drug
+trafficking detection accuracy, showing a remarkable improvement of nearly
+12\%. By integrating prior knowledge and the proposed prompts, ChatGPT can
+effectively identify and label drug trafficking activities on social networks,
+even in the presence of deceptive language and euphemisms used by drug dealers
+to evade detection. The implications of our research extend to social networks,
+emphasizing the importance of incorporating prior knowledge and scenario-based
+prompts into analytical tools to improve online security and public safety.
+"
+AutoHint: Automatic Prompt Optimization with Hint Generation,Hong Sun,http://arxiv.org/pdf/2307.07415v2.pdf,2023-07-13,"['cs.cl', 'cs.ai']",2307.07415v2.pdf,"  This paper presents AutoHint, a novel framework for automatic prompt
+engineering and optimization for Large Language Models (LLM). While LLMs have
+demonstrated remarkable ability in achieving high-quality annotation in various
+tasks, the key to applying this ability to specific tasks lies in developing
+high-quality prompts. Thus we propose a framework to inherit the merits of both
+in-context learning and zero-shot learning by incorporating enriched
+instructions derived from input-output demonstrations to optimize original
+prompt. We refer to the enrichment as the hint and propose a framework to
+automatically generate the hint from labeled data. More concretely, starting
+from an initial prompt, our method first instructs a LLM to deduce new hints
+for selected samples from incorrect predictions, and then summarizes from
+per-sample hints and adds the results back to the initial prompt to form a new,
+enriched instruction. The proposed method is evaluated on the BIG-Bench
+Instruction Induction dataset for both zero-shot and few-short prompts, where
+experiments demonstrate our method is able to significantly boost accuracy for
+multiple tasks.
+"
+"Optimizing Mobile-Edge AI-Generated Everything (AIGX) Services by Prompt  Engineering: Fundamental, Framework, and Case Study",Yinqiu Liu,http://arxiv.org/pdf/2309.01065v1.pdf,2023-09-03,['cs.ni'],2309.01065v1.pdf,"  As the next-generation paradigm for content creation, AI-Generated Content
+(AIGC), i.e., generating content automatically by Generative AI (GAI) based on
+user prompts, has gained great attention and success recently. With the
+ever-increasing power of GAI, especially the emergence of Pretrained Foundation
+Models (PFMs) that contain billions of parameters and prompt engineering
+methods (i.e., finding the best prompts for the given task), the application
+range of AIGC is rapidly expanding, covering various forms of information for
+human, systems, and networks, such as network designs, channel coding, and
+optimization solutions. In this article, we present the concept of mobile-edge
+AI-Generated Everything (AIGX). Specifically, we first review the building
+blocks of AIGX, the evolution from AIGC to AIGX, as well as practical AIGX
+applications. Then, we present a unified mobile-edge AIGX framework, which
+employs edge devices to provide PFM-empowered AIGX services and optimizes such
+services via prompt engineering. More importantly, we demonstrate that
+suboptimal prompts lead to poor generation quality, which adversely affects
+user satisfaction, edge network performance, and resource utilization.
+Accordingly, we conduct a case study, showcasing how to train an effective
+prompt optimizer using ChatGPT and investigating how much improvement is
+possible with prompt engineering in terms of user experience, quality of
+generation, and network performance.
+"
+Automatic Data Transformation Using Large Language Model: An  Experimental Study on Building Energy Data,Ankita Sharma,http://arxiv.org/pdf/2309.01957v2.pdf,2023-09-05,['cs.db'],2309.01957v2.pdf,"  Existing approaches to automatic data transformation are insufficient to meet
+the requirements in many real-world scenarios, such as the building sector.
+First, there is no convenient interface for domain experts to provide domain
+knowledge easily. Second, they require significant training data collection
+overheads. Third, the accuracy suffers from complicated schema changes. To
+bridge this gap, we present a novel approach that leverages the unique
+capabilities of large language models (LLMs) in coding, complex reasoning, and
+zero-shot learning to generate SQL code that transforms the source datasets
+into the target datasets. We demonstrate the viability of this approach by
+designing an LLM-based framework, termed SQLMorpher, which comprises a prompt
+generator that integrates the initial prompt with optional domain knowledge and
+historical patterns in external databases. It also implements an iterative
+prompt optimization mechanism that automatically improves the prompt based on
+flaw detection. The key contributions of this work include (1) pioneering an
+end-to-end LLM-based solution for data transformation, (2) developing a
+benchmark dataset of 105 real-world building energy data transformation
+problems, and (3) conducting an extensive empirical evaluation where our
+approach achieved 96% accuracy in all 105 problems. SQLMorpher demonstrates the
+effectiveness of utilizing LLMs in complex, domain-specific challenges,
+highlighting the potential of their potential to drive sustainable solutions.
+"
+Automatic Prompt Rewriting for Personalized Text Generation,Cheng Li,http://arxiv.org/pdf/2310.00152v1.pdf,2023-09-29,['cs.cl'],2310.00152v1.pdf,"  Facilitated by large language models (LLMs), personalized text generation has
+become a rapidly growing research direction. Most existing studies focus on
+designing specialized models for a particular domain, or they require
+fine-tuning the LLMs to generate personalized text. We consider a typical
+scenario in which the large language model, which generates personalized
+output, is frozen and can only be accessed through APIs. Under this constraint,
+all one can do is to improve the input text (i.e., text prompts) sent to the
+LLM, a procedure that is usually done manually. In this paper, we propose a
+novel method to automatically revise prompts for personalized text generation.
+The proposed method takes the initial prompts generated by a state-of-the-art,
+multistage framework for personalized generation and rewrites a few critical
+components that summarize and synthesize the personal context. The prompt
+rewriter employs a training paradigm that chains together supervised learning
+(SL) and reinforcement learning (RL), where SL reduces the search space of RL
+and RL facilitates end-to-end training of the rewriter. Using datasets from
+three representative domains, we demonstrate that the rewritten prompts
+outperform both the original prompts and the prompts optimized via supervised
+learning or reinforcement learning alone. In-depth analysis of the rewritten
+prompts shows that they are not only human readable, but also able to guide
+manual revision of prompts when there is limited resource to employ
+reinforcement learning to train the prompt rewriter, or when it is costly to
+deploy an automatic prompt rewriter for inference.
+"
+DeltaSpace: A Semantic-aligned Feature Space for Flexible Text-guided  Image Editing,Yueming Lyu,http://arxiv.org/pdf/2310.08785v1.pdf,2023-10-12,"['cs.cv', 'cs.ai']",2310.08785v1.pdf,"  Text-guided image editing faces significant challenges to training and
+inference flexibility. Much literature collects large amounts of annotated
+image-text pairs to train text-conditioned generative models from scratch,
+which is expensive and not efficient. After that, some approaches that leverage
+pre-trained vision-language models are put forward to avoid data collection,
+but they are also limited by either per text-prompt optimization or
+inference-time hyper-parameters tuning. To address these issues, we investigate
+and identify a specific space, referred to as CLIP DeltaSpace, where the CLIP
+visual feature difference of two images is semantically aligned with the CLIP
+textual feature difference of their corresponding text descriptions. Based on
+DeltaSpace, we propose a novel framework called DeltaEdit, which maps the CLIP
+visual feature differences to the latent space directions of a generative model
+during the training phase, and predicts the latent space directions from the
+CLIP textual feature differences during the inference phase. And this design
+endows DeltaEdit with two advantages: (1) text-free training; (2)
+generalization to various text prompts for zero-shot inference. Extensive
+experiments validate the effectiveness and versatility of DeltaEdit with
+different generative models, including both the GAN model and the diffusion
+model, in achieving flexible text-guided image editing. Code is available at
+https://github.com/Yueming6568/DeltaEdit.
+"
+InstructPix2NeRF: Instructed 3D Portrait Editing from a Single Image,Jianhui Li,http://arxiv.org/pdf/2311.02826v1.pdf,2023-11-06,['cs.cv'],2311.02826v1.pdf,"  With the success of Neural Radiance Field (NeRF) in 3D-aware portrait
+editing, a variety of works have achieved promising results regarding both
+quality and 3D consistency. However, these methods heavily rely on per-prompt
+optimization when handling natural language as editing instructions. Due to the
+lack of labeled human face 3D datasets and effective architectures, the area of
+human-instructed 3D-aware editing for open-world portraits in an end-to-end
+manner remains under-explored. To solve this problem, we propose an end-to-end
+diffusion-based framework termed InstructPix2NeRF, which enables instructed
+3D-aware portrait editing from a single open-world image with human
+instructions. At its core lies a conditional latent 3D diffusion process that
+lifts 2D editing to 3D space by learning the correlation between the paired
+images' difference and the instructions via triplet data. With the help of our
+proposed token position randomization strategy, we could even achieve
+multi-semantic editing through one single pass with the portrait identity
+well-preserved. Besides, we further propose an identity consistency module that
+directly modulates the extracted identity signals into our diffusion process,
+which increases the multi-view 3D identity consistency. Extensive experiments
+verify the effectiveness of our method and show its superiority against strong
+baselines quantitatively and qualitatively.
+"
+What Changes Can Large-scale Language Models Bring? Intensive Study on  HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers,Boseop Kim,http://arxiv.org/pdf/2109.04650v2.pdf,2021-09-10,['cs.cl'],2109.04650v2.pdf,"  GPT-3 shows remarkable in-context learning ability of large-scale language
+models (LMs) trained on hundreds of billion scale data. Here we address some
+remaining issues less reported by the GPT-3 paper, such as a non-English LM,
+the performances of different sized models, and the effect of recently
+introduced prompt optimization on in-context learning. To achieve this, we
+introduce HyperCLOVA, a Korean variant of 82B GPT-3 trained on a Korean-centric
+corpus of 560B tokens. Enhanced by our Korean-specific tokenization, HyperCLOVA
+with our training configuration shows state-of-the-art in-context zero-shot and
+few-shot learning performances on various downstream tasks in Korean. Also, we
+show the performance benefits of prompt-based learning and demonstrate how it
+can be integrated into the prompt engineering pipeline. Then we discuss the
+possibility of materializing the No Code AI paradigm by providing AI
+prototyping capabilities to non-experts of ML by introducing HyperCLOVA studio,
+an interactive prompt engineering interface. Lastly, we demonstrate the
+potential of our methods with three successful in-house applications.
+"
+MLLM-DataEngine: An Iterative Refinement Approach for MLLM,Zhiyuan Zhao,http://arxiv.org/pdf/2308.13566v2.pdf,2023-08-25,"['cs.lg', 'cs.ai', 'cs.cl', 'cs.cv']",2308.13566v2.pdf,"  Despite the great advance of Multimodal Large Language Models (MLLMs) in both
+instruction dataset building and benchmarking, the independence of training and
+evaluation makes current MLLMs hard to further improve their capability under
+the guidance of evaluation results with a relatively low human cost. In this
+paper, we propose MLLM-DataEngine, a novel closed-loop system that bridges data
+generation, model training, and evaluation. Within each loop iteration, the
+MLLM-DataEngine first analyze the weakness of the model based on the evaluation
+results, then generate a proper incremental dataset for the next training
+iteration and enhance the model capability iteratively. Compared with previous
+data collection methods which are separate from the benchmarking, the data
+generated by MLLM-DataEngine shows better targeting, quality, and correctness.
+For targeting, we propose an Adaptive Bad-case Sampling module, which adjusts
+the ratio of different types of data within each incremental dataset based on
+the benchmarking results. For quality, we resort to GPT-4 to generate
+high-quality data with each given data type. For correctness, prompt design is
+critical for the data generation results. Rather than previous hand-crafted
+prompt, we propose an Interactive Prompt Optimization strategy, which optimizes
+the prompt with the multi-round interaction between human and GPT, and improve
+the correctness of generated data greatly. Through extensive experiments, we
+find our MLLM-DataEngine could boost the MLLM capability in a targeted and
+automatic manner, with only a few human participation. We hope it could be a
+general solution for the following MLLMs building. The MLLM-DataEngine has been
+open-sourced and is now available at
+https://github.com/opendatalab/MLLM-DataEngine.
+"
+Unleashing the potential of prompt engineering in Large Language Models:  a comprehensive review,Banghao Chen,http://arxiv.org/pdf/2310.14735v2.pdf,2023-10-23,"['cs.cl', 'cs.ai', 'i.2.7']",2310.14735v2.pdf,"  This paper delves into the pivotal role of prompt engineering in unleashing
+the capabilities of Large Language Models (LLMs). Prompt engineering is the
+process of structuring input text for LLMs and is a technique integral to
+optimizing the efficacy of LLMs. This survey elucidates foundational principles
+of prompt engineering, such as role-prompting, one-shot, and few-shot
+prompting, as well as more advanced methodologies such as the chain-of-thought
+and tree-of-thoughts prompting. The paper sheds light on how external
+assistance in the form of plugins can assist in this task, and reduce machine
+hallucination by retrieving external knowledge. We subsequently delineate
+prospective directions in prompt engineering research, emphasizing the need for
+a deeper understanding of structures and the role of agents in Artificial
+Intelligence-Generated Content (AIGC) tools. We discuss how to assess the
+efficacy of prompt methods from different perspectives and using different
+methods. Finally, we gather information about the application of prompt
+engineering in such fields as education and programming, showing its
+transformative potential. This comprehensive survey aims to serve as a friendly
+guide for anyone venturing through the big world of LLMs and prompt
+engineering.
+"
+Prompt Engineering For Students of Medicine and Their Teachers,Thomas F. Heston,http://arxiv.org/pdf/2308.11628v1.pdf,2023-08-08,['cs.hc'],2308.11628v1.pdf,"  ""Prompt Engineering for Students of Medicine and Their Teachers"" brings the
+principles of prompt engineering for large language models such as ChatGPT and
+Google Bard to medical education. This book contains a comprehensive guide to
+prompt engineering to help both teachers and students improve education in the
+medical field. Just as prompt engineering is critical in getting good
+information out of an AI, it is also critical to get students to think and
+understand more deeply. The principles of prompt engineering that we have
+learned from AI systems have the potential to simultaneously revolutionize
+learning in the healthcare field. The book analyzes from multiple angles the
+anatomy of a good prompt for both AI models and students. The different types
+of prompts are examined, showing how each style has unique characteristics and
+applications. The principles of prompt engineering, applied properly, are
+demonstrated to be effective in teaching across the diverse fields of anatomy,
+physiology, pathology, pharmacology, and clinical skills. Just like ChatGPT and
+similar large language AI models, students need clear and detailed prompting in
+order for them to fully understand a topic. Using identical principles, a
+prompt that gets good information from an AI will also cause a student to think
+more deeply and accurately. The process of prompt engineering facilitates this
+process. Because each chapter contains multiple examples and key takeaways, it
+is a practical guide for implementing prompt engineering in the learning
+process. It provides a hands-on approach to ensure readers can immediately
+apply the concepts they learn
+"
+Prompting AI Art: An Investigation into the Creative Skill of Prompt  Engineering,Jonas Oppenlaender,http://arxiv.org/pdf/2303.13534v1.pdf,2023-03-13,"['cs.hc', 'h.m']",2303.13534v1.pdf,"  Humankind is entering a novel era of creativity - an era in which anybody can
+synthesize digital content. The paradigm under which this revolution takes
+place is prompt-based learning (or in-context learning). This paradigm has
+found fruitful application in text-to-image generation where it is being used
+to synthesize digital images from zero-shot text prompts in natural language
+for the purpose of creating AI art. This activity is referred to as prompt
+engineering - the practice of iteratively crafting prompts to generate and
+improve images. In this paper, we investigate prompt engineering as a novel
+creative skill for creating prompt-based art. In three studies with
+participants recruited from a crowdsourcing platform, we explore whether
+untrained participants could 1) recognize the quality of prompts, 2) write
+prompts, and 3) improve their prompts. Our results indicate that participants
+could assess the quality of prompts and respective images. This ability
+increased with the participants' experience and interest in art. Participants
+further were able to write prompts in rich descriptive language. However, even
+though participants were specifically instructed to generate artworks,
+participants' prompts were missing the specific vocabulary needed to apply a
+certain style to the generated images. Our results suggest that prompt
+engineering is a learned skill that requires expertise and practice. Based on
+our findings and experience with running our studies with participants
+recruited from a crowdsourcing platform, we provide ten recommendations for
+conducting experimental research on text-to-image generation and prompt
+engineering with a paid crowd. Our studies offer a deeper understanding of
+prompt engineering thereby opening up avenues for research on the future of
+prompt engineering. We conclude by speculating on four possible futures of
+prompt engineering.
+"
+Review of Large Vision Models and Visual Prompt Engineering,Jiaqi Wang,http://arxiv.org/pdf/2307.00855v1.pdf,2023-07-03,"['cs.cv', 'cs.ai']",2307.00855v1.pdf,"  Visual prompt engineering is a fundamental technology in the field of visual
+and image Artificial General Intelligence, serving as a key component for
+achieving zero-shot capabilities. As the development of large vision models
+progresses, the importance of prompt engineering becomes increasingly evident.
+Designing suitable prompts for specific visual tasks has emerged as a
+meaningful research direction. This review aims to summarize the methods
+employed in the computer vision domain for large vision models and visual
+prompt engineering, exploring the latest advancements in visual prompt
+engineering. We present influential large models in the visual domain and a
+range of prompt engineering methods employed on these models. It is our hope
+that this review provides a comprehensive and systematic description of prompt
+engineering methods based on large visual models, offering valuable insights
+for future researchers in their exploration of this field.
+"
+Prompt Engineering for Healthcare: Methodologies and Applications,Jiaqi Wang,http://arxiv.org/pdf/2304.14670v1.pdf,2023-04-28,['cs.ai'],2304.14670v1.pdf,"  This review will introduce the latest advances in prompt engineering in the
+field of natural language processing (NLP) for the medical domain. First, we
+will provide a brief overview of the development of prompt engineering and
+emphasize its significant contributions to healthcare NLP applications such as
+question-answering systems, text summarization, and machine translation. With
+the continuous improvement of general large language models, the importance of
+prompt engineering in the healthcare domain is becoming increasingly prominent.
+The aim of this article is to provide useful resources and bridges for
+healthcare NLP researchers to better explore the application of prompt
+engineering in this field. We hope that this review can provide new ideas and
+inspire ample possibilities for research and application in medical NLP.
+"
+A Brief History of Prompt: Leveraging Language Models,Golam Md Muktadir,http://arxiv.org/pdf/2310.04438v1.pdf,2023-09-30,"['cs.cl', 'cs.ai']",2310.04438v1.pdf,"  This paper presents a comprehensive exploration of the evolution of prompt
+engineering and generation in the field of natural language processing (NLP).
+Starting from the early language models and information retrieval systems, we
+trace the key developments that have shaped prompt engineering over the years.
+The introduction of attention mechanisms in 2015 revolutionized language
+understanding, leading to advancements in controllability and
+context-awareness. Subsequent breakthroughs in reinforcement learning
+techniques further enhanced prompt engineering, addressing issues like exposure
+bias and biases in generated text. We examine the significant contributions in
+2018 and 2019, focusing on fine-tuning strategies, control codes, and
+template-based generation. The paper also discusses the growing importance of
+fairness, human-AI collaboration, and low-resource adaptation. In 2020 and
+2021, contextual prompting and transfer learning gained prominence, while 2022
+and 2023 witnessed the emergence of advanced techniques like unsupervised
+pre-training and novel reward shaping. Throughout the paper, we reference
+specific research studies that exemplify the impact of various developments on
+prompt engineering. The journey of prompt engineering continues, with ethical
+considerations being paramount for the responsible and inclusive future of AI
+systems.
+"
+A Systematic Survey of Prompt Engineering on Vision-Language Foundation  Models,Jindong Gu,http://arxiv.org/pdf/2307.12980v1.pdf,2023-07-24,['cs.cv'],2307.12980v1.pdf,"  Prompt engineering is a technique that involves augmenting a large
+pre-trained model with task-specific hints, known as prompts, to adapt the
+model to new tasks. Prompts can be created manually as natural language
+instructions or generated automatically as either natural language instructions
+or vector representations. Prompt engineering enables the ability to perform
+predictions based solely on prompts without updating model parameters, and the
+easier application of large pre-trained models in real-world tasks. In past
+years, Prompt engineering has been well-studied in natural language processing.
+Recently, it has also been intensively studied in vision-language modeling.
+However, there is currently a lack of a systematic overview of prompt
+engineering on pre-trained vision-language models. This paper aims to provide a
+comprehensive survey of cutting-edge research in prompt engineering on three
+types of vision-language models: multimodal-to-text generation models (e.g.
+Flamingo), image-text matching models (e.g. CLIP), and text-to-image generation
+models (e.g. Stable Diffusion). For each type of model, a brief model summary,
+prompting methods, prompting-based applications, and the corresponding
+responsibility and integrity issues are summarized and discussed. Furthermore,
+the commonalities and differences between prompting on vision-language models,
+language models, and vision models are also discussed. The challenges, future
+directions, and research opportunities are summarized to foster future research
+on this topic.
+"
+Prompt Engineering and Calibration for Zero-Shot Commonsense Reasoning,Chenkai Ma,http://arxiv.org/pdf/2304.06962v1.pdf,2023-04-14,"['cs.cl', 'cs.ai']",2304.06962v1.pdf,"  Prompt engineering and calibration make large language models excel at
+reasoning tasks, including multiple choice commonsense reasoning. From a
+practical perspective, we investigate and evaluate these strategies on smaller
+language models. Through experiments on five commonsense reasoning benchmarks,
+we find that each strategy favors certain models, but their joint effects are
+mostly negative.
+"
+Just Tell Me: Prompt Engineering in Business Process Management,Kiran Busch,http://arxiv.org/pdf/2304.07183v1.pdf,2023-04-14,"['cs.ai', 'cs.cl', 'cs.lg']",2304.07183v1.pdf,"  GPT-3 and several other language models (LMs) can effectively address various
+natural language processing (NLP) tasks, including machine translation and text
+summarization. Recently, they have also been successfully employed in the
+business process management (BPM) domain, e.g., for predictive process
+monitoring and process extraction from text. This, however, typically requires
+fine-tuning the employed LM, which, among others, necessitates large amounts of
+suitable training data. A possible solution to this problem is the use of
+prompt engineering, which leverages pre-trained LMs without fine-tuning them.
+Recognizing this, we argue that prompt engineering can help bring the
+capabilities of LMs to BPM research. We use this position paper to develop a
+research agenda for the use of prompt engineering for BPM research by
+identifying the associated potentials and challenges.
+"
+Revisiting Prompt Engineering via Declarative Crowdsourcing,Aditya G. Parameswaran,http://arxiv.org/pdf/2308.03854v1.pdf,2023-08-07,"['cs.db', 'cs.ai', 'cs.hc', 'cs.lg']",2308.03854v1.pdf,"  Large language models (LLMs) are incredibly powerful at comprehending and
+generating data in the form of text, but are brittle and error-prone. There has
+been an advent of toolkits and recipes centered around so-called prompt
+engineering-the process of asking an LLM to do something via a series of
+prompts. However, for LLM-powered data processing workflows, in particular,
+optimizing for quality, while keeping cost bounded, is a tedious, manual
+process. We put forth a vision for declarative prompt engineering. We view LLMs
+like crowd workers and leverage ideas from the declarative crowdsourcing
+literature-including leveraging multiple prompting strategies, ensuring
+internal consistency, and exploring hybrid-LLM-non-LLM approaches-to make
+prompt engineering a more principled process. Preliminary case studies on
+sorting, entity resolution, and imputation demonstrate the promise of our
+approach
+"
+How understanding large language models can inform their use in physics  education,Giulia Polverini,http://arxiv.org/pdf/2309.12074v1.pdf,2023-09-21,['physics.ed-ph'],2309.12074v1.pdf,"  The paper aims to fulfil three main functions: (1) to serve as an
+introduction for the physics education community to the functioning of Large
+Language Models (LLMs), (2) to present a series of illustrative examples
+demonstrating how prompt-engineering techniques can impact LLMs performance on
+conceptual physics tasks and (3) to discuss potential implications of the
+understanding of LLMs and prompt engineering for physics teaching and learning.
+We first summarise existing research on the performance of a popular LLM-based
+chatbot (ChatGPT) on physics tasks. We then give a basic account of how LLMs
+work, illustrate essential features of their functioning, and discuss their
+strengths and limitations. Equipped with this knowledge, we discuss some
+challenges with generating useful output with ChatGPT-4 in the context of
+introductory physics, paying special attention to conceptual questions and
+problems. We then provide a condensed overview of relevant literature on prompt
+engineering and demonstrate through illustrative examples how selected
+prompt-engineering techniques can be employed to improve ChatGPT-4's output on
+conceptual introductory physics problems. Qualitatively studying these examples
+provides additional insights into ChatGPT's functioning and its utility in
+physics problem solving. Finally, we consider how insights from the paper can
+inform the use of LMMs in the teaching and learning of physics.
+"
+Data-Driven Approach for Formality-Sensitive Machine Translation:  Language-Specific Handling and Synthetic Data Generation,Seugnjun Lee,http://arxiv.org/pdf/2306.14514v2.pdf,2023-06-26,"['cs.cl', 'cs.ai']",2306.14514v2.pdf,"  In this paper, we introduce a data-driven approach for Formality-Sensitive
+Machine Translation (FSMT) that caters to the unique linguistic properties of
+four target languages. Our methodology centers on two core strategies: 1)
+language-specific data handling, and 2) synthetic data generation using
+large-scale language models and empirical prompt engineering. This approach
+demonstrates a considerable improvement over the baseline, highlighting the
+effectiveness of data-centric techniques. Our prompt engineering strategy
+further improves performance by producing superior synthetic translation
+examples.
+"
+Exploring the Intersection of Large Language Models and Agent-Based  Modeling via Prompt Engineering,Edward Junprung,http://arxiv.org/pdf/2308.07411v1.pdf,2023-08-14,"['cs.ai', 'cs.ma']",2308.07411v1.pdf,"  The final frontier for simulation is the accurate representation of complex,
+real-world social systems. While agent-based modeling (ABM) seeks to study the
+behavior and interactions of agents within a larger system, it is unable to
+faithfully capture the full complexity of human-driven behavior. Large language
+models (LLMs), like ChatGPT, have emerged as a potential solution to this
+bottleneck by enabling researchers to explore human-driven interactions in
+previously unimaginable ways. Our research investigates simulations of human
+interactions using LLMs. Through prompt engineering, inspired by Park et al.
+(2023), we present two simulations of believable proxies of human behavior: a
+two-agent negotiation and a six-agent murder mystery game.
+"
+Large Language Models Are Human-Level Prompt Engineers,Yongchao Zhou,http://arxiv.org/pdf/2211.01910v2.pdf,2022-11-03,"['cs.lg', 'cs.ai', 'cs.cl']",2211.01910v2.pdf,"  By conditioning on natural language instructions, large language models
+(LLMs) have displayed impressive capabilities as general-purpose computers.
+However, task performance depends significantly on the quality of the prompt
+used to steer the model, and most effective prompts have been handcrafted by
+humans. Inspired by classical program synthesis and the human approach to
+prompt engineering, we propose Automatic Prompt Engineer (APE) for automatic
+instruction generation and selection. In our method, we treat the instruction
+as the ""program,"" optimized by searching over a pool of instruction candidates
+proposed by an LLM in order to maximize a chosen score function. To evaluate
+the quality of the selected instruction, we evaluate the zero-shot performance
+of another LLM following the selected instruction. Experiments on 24 NLP tasks
+show that our automatically generated instructions outperform the prior LLM
+baseline by a large margin and achieve better or comparable performance to the
+instructions generated by human annotators on 19/24 tasks. We conduct extensive
+qualitative and quantitative analyses to explore the performance of APE. We
+show that APE-engineered prompts can be applied to steer models toward
+truthfulness and/or informativeness, as well as to improve few-shot learning
+performance by simply prepending them to standard in-context learning prompts.
+Please check out our webpage at
+https://sites.google.com/view/automatic-prompt-engineer.
+"
+Grimm in Wonderland: Prompt Engineering with Midjourney to Illustrate  Fairytales,Martin Ruskov,http://arxiv.org/pdf/2302.08961v2.pdf,2023-02-17,"['cs.cl', 'cs.ai', 'cs.hc', 'i.2']",2302.08961v2.pdf,"  The quality of text-to-image generation is continuously improving, yet the
+boundaries of its applicability are still unclear. In particular, refinement of
+the text input with the objective of achieving better results - commonly called
+prompt engineering - so far seems to have not been geared towards work with
+pre-existing texts. We investigate whether text-to-image generation and prompt
+engineering could be used to generate basic illustrations of popular
+fairytales. Using Midjourney v4, we engage in action research with a dual aim:
+to attempt to generate 5 believable illustrations for each of 5 popular
+fairytales, and to define a prompt engineering process that starts from a
+pre-existing text and arrives at an illustration of it. We arrive at a
+tentative 4-stage process: i) initial prompt, ii) composition adjustment, iii)
+style refinement, and iv) variation selection. We also discuss three reasons
+why the generation model struggles with certain illustrations: difficulties
+with counts, bias from stereotypical configurations and inability to depict
+overly fantastic situations. Our findings are not limited to the specific
+generation model and are intended to be generalisable to future ones.
+"
+A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT,Jules White,http://arxiv.org/pdf/2302.11382v1.pdf,2023-02-21,"['cs.se', 'cs.ai']",2302.11382v1.pdf,"  Prompt engineering is an increasingly important skill set needed to converse
+effectively with large language models (LLMs), such as ChatGPT. Prompts are
+instructions given to an LLM to enforce rules, automate processes, and ensure
+specific qualities (and quantities) of generated output. Prompts are also a
+form of programming that can customize the outputs and interactions with an
+LLM. This paper describes a catalog of prompt engineering techniques presented
+in pattern form that have been applied to solve common problems when conversing
+with LLMs. Prompt patterns are a knowledge transfer method analogous to
+software patterns since they provide reusable solutions to common problems
+faced in a particular context, i.e., output generation and interaction when
+working with LLMs. This paper provides the following contributions to research
+on prompt engineering that apply LLMs to automate software development tasks.
+First, it provides a framework for documenting patterns for structuring prompts
+to solve a range of problems so that they can be adapted to different domains.
+Second, it presents a catalog of patterns that have been applied successfully
+to improve the outputs of LLM conversations. Third, it explains how prompts can
+be built from multiple patterns and illustrates prompt patterns that benefit
+from combination with other prompt patterns.
+"
+Prompt Space Optimizing Few-shot Reasoning Success with Large Language  Models,Fobo Shi,http://arxiv.org/pdf/2306.03799v1.pdf,2023-06-06,['cs.cl'],2306.03799v1.pdf,"  Prompt engineering is an essential technique for enhancing the abilities of
+large language models (LLMs) by providing explicit and specific instructions.
+It enables LLMs to excel in various tasks, such as arithmetic reasoning,
+question answering, summarization, relation extraction, machine translation,
+and sentiment analysis. Researchers have been actively exploring different
+prompt engineering strategies, such as Chain of Thought (CoT), Zero-CoT, and
+In-context learning. However, an unresolved problem arises from the fact that
+current approaches lack a solid theoretical foundation for determining optimal
+prompts. To address this issue in prompt engineering, we propose a new and
+effective approach called Prompt Space. Our methodology utilizes text
+embeddings to obtain basis vectors by matrix decomposition, and then constructs
+a space for representing all prompts. Prompt Space significantly outperforms
+state-of-the-art prompt paradigms on ten public reasoning benchmarks. Notably,
+without the help of the CoT method and the prompt ""Let's think step by step"",
+Prompt Space shows superior performance over the few-shot method. Overall, our
+approach provides a robust and fundamental theoretical framework for selecting
+simple and effective prompts. This advancement marks a significant step towards
+improving prompt engineering for a wide variety of applications in LLMs.
+"
+An Empirical Evaluation of Prompting Strategies for Large Language  Models in Zero-Shot Clinical Natural Language Processing,Sonish Sivarajkumar,http://arxiv.org/pdf/2309.08008v1.pdf,2023-09-14,"['cs.cl', 'cs.ai']",2309.08008v1.pdf,"  Large language models (LLMs) have shown remarkable capabilities in Natural
+Language Processing (NLP), especially in domains where labeled data is scarce
+or expensive, such as clinical domain. However, to unlock the clinical
+knowledge hidden in these LLMs, we need to design effective prompts that can
+guide them to perform specific clinical NLP tasks without any task-specific
+training data. This is known as in-context learning, which is an art and
+science that requires understanding the strengths and weaknesses of different
+LLMs and prompt engineering approaches. In this paper, we present a
+comprehensive and systematic experimental study on prompt engineering for five
+clinical NLP tasks: Clinical Sense Disambiguation, Biomedical Evidence
+Extraction, Coreference Resolution, Medication Status Extraction, and
+Medication Attribute Extraction. We assessed the prompts proposed in recent
+literature, including simple prefix, simple cloze, chain of thought, and
+anticipatory prompts, and introduced two new types of prompts, namely heuristic
+prompting and ensemble prompting. We evaluated the performance of these prompts
+on three state-of-the-art LLMs: GPT-3.5, BARD, and LLAMA2. We also contrasted
+zero-shot prompting with few-shot prompting, and provide novel insights and
+guidelines for prompt engineering for LLMs in clinical NLP. To the best of our
+knowledge, this is one of the first works on the empirical evaluation of
+different prompt engineering approaches for clinical NLP in this era of
+generative AI, and we hope that it will inspire and inform future research in
+this area.
+"
+Prompt Engineering or Fine Tuning: An Empirical Assessment of Large  Language Models in Automated Software Engineering Tasks,Jiho Shin,http://arxiv.org/pdf/2310.10508v1.pdf,2023-10-11,['cs.se'],2310.10508v1.pdf,"  In this paper, we investigate the effectiveness of state-of-the-art LLM,
+i.e., GPT-4, with three different prompting engineering techniques (i.e., basic
+prompting, in-context learning, and task-specific prompting) against 18
+fine-tuned LLMs on three typical ASE tasks, i.e., code generation, code
+summarization, and code translation. Our quantitative analysis of these
+prompting strategies suggests that prompt engineering GPT-4 cannot necessarily
+and significantly outperform fine-tuning smaller/older LLMs in all three tasks.
+For comment generation, GPT-4 with the best prompting strategy (i.e.,
+task-specific prompt) had outperformed the first-ranked fine-tuned model by
+8.33% points on average in BLEU. However, for code generation, the first-ranked
+fine-tuned model outperforms GPT-4 with best prompting by 16.61% and 28.3%
+points, on average in BLEU. For code translation, GPT-4 and fine-tuned
+baselines tie as they outperform each other on different translation tasks. To
+explore the impact of different prompting strategies, we conducted a user study
+with 27 graduate students and 10 industry practitioners. From our qualitative
+analysis, we find that the GPT-4 with conversational prompts (i.e., when a
+human provides feedback and instructions back and forth with a model to achieve
+best results) showed drastic improvement compared to GPT-4 with automatic
+prompting strategies. Moreover, we observe that participants tend to request
+improvements, add more context, or give specific instructions as conversational
+prompts, which goes beyond typical and generic prompting strategies. Our study
+suggests that, at its current state, GPT-4 with conversational prompting has
+great potential for ASE tasks, but fully automated prompt engineering with no
+human in the loop requires more study and improvement.
+"
+An Information-theoretic Approach to Prompt Engineering Without Ground  Truth Labels,Taylor Sorensen,http://arxiv.org/pdf/2203.11364v1.pdf,2022-03-21,"['cs.cl', 'cs.lg']",2203.11364v1.pdf,"  Pre-trained language models derive substantial linguistic and factual
+knowledge from the massive corpora on which they are trained, and prompt
+engineering seeks to align these models to specific tasks. Unfortunately,
+existing prompt engineering methods require significant amounts of labeled
+data, access to model parameters, or both. We introduce a new method for
+selecting prompt templates \textit{without labeled examples} and
+\textit{without direct access to the model}. Specifically, over a set of
+candidate templates, we choose the template that maximizes the mutual
+information between the input and the corresponding model output. Across 8
+datasets representing 7 distinct NLP tasks, we show that when a template has
+high mutual information, it also has high accuracy on the task. On the largest
+model, selecting prompts with our method gets 90\% of the way from the average
+prompt accuracy to the best prompt accuracy and requires no ground truth
+labels.
+"
+Unsupervised Prompt Learning for Vision-Language Models,Tony Huang,http://arxiv.org/pdf/2204.03649v2.pdf,2022-04-07,['cs.cv'],2204.03649v2.pdf,"  Contrastive vision-language models like CLIP have shown great progress in
+transfer learning. In the inference stage, the proper text description, also
+known as prompt, needs to be carefully designed to correctly classify the given
+images. In order to avoid laborious prompt engineering, recent works such as
+CoOp, CLIP-Adapter and Tip-Adapter propose to adapt vision-language models for
+downstream image recognition tasks on a small set of labeled data. Though
+promising improvements are achieved, requiring labeled data from the target
+datasets may restrict the scalability. In this paper, we explore a different
+scenario, in which the labels of the target datasets are unprovided, and we
+present an unsupervised prompt learning (UPL) approach to avoid prompt
+engineering while simultaneously improving transfer performance of CLIP-like
+vision-language models. As far as we know, UPL is the first work to introduce
+unsupervised learning into prompt learning. Experimentally, our UPL outperforms
+original CLIP with prompt engineering on ImageNet as well as other 10 datasets.
+An enhanced version of UPL is even competitive with the 8-shot CoOp and the
+8-shot TIP-Adapter on most datasets. Code and models are available at
+https://github.com/tonyhuang2022/UPL.
+"
+ChainForge: A Visual Toolkit for Prompt Engineering and LLM Hypothesis  Testing,Ian Arawjo,http://arxiv.org/pdf/2309.09128v1.pdf,2023-09-17,"['cs.hc', 'cs.ai', 'h.5.2; i.2']",2309.09128v1.pdf,"  Evaluating outputs of large language models (LLMs) is challenging, requiring
+making -- and making sense of -- many responses. Yet tools that go beyond basic
+prompting tend to require knowledge of programming APIs, focus on narrow
+domains, or are closed-source. We present ChainForge, an open-source visual
+toolkit for prompt engineering and on-demand hypothesis testing of text
+generation LLMs. ChainForge provides a graphical interface for comparison of
+responses across models and prompt variations. Our system was designed to
+support three tasks: model selection, prompt template design, and hypothesis
+testing (e.g., auditing). We released ChainForge early in its development and
+iterated on its design with academics and online users. Through in-lab and
+interview studies, we find that a range of people could use ChainForge to
+investigate hypotheses that matter to them, including in real-world settings.
+We identify three modes of prompt engineering and LLM hypothesis testing:
+opportunistic exploration, limited evaluation, and iterative refinement.
+"
+CoPrompt: Supporting Prompt Sharing and Referring in Collaborative  Natural Language Programming,Felicia Li Feng,http://arxiv.org/pdf/2310.09235v1.pdf,2023-10-13,['cs.hc'],2310.09235v1.pdf,"  Natural language (NL) programming has become more approachable due to the
+powerful code-generation capability of large language models (LLMs). This shift
+to using NL to program enhances collaborative programming by reducing
+communication barriers and context-switching among programmers from varying
+backgrounds. However, programmers may face challenges during prompt engineering
+in a collaborative setting as they need to actively keep aware of their
+collaborators' progress and intents. In this paper, we aim to investigate ways
+to assist programmers' prompt engineering in a collaborative context. We first
+conducted a formative study to understand the workflows and challenges of
+programmers when using NL for collaborative programming. Based on our findings,
+we implemented a prototype, CoPrompt, to support collaborative prompt
+engineering by providing referring, requesting, sharing, and linking
+mechanisms. Our user study indicates that CoPrompt assists programmers in
+comprehending collaborators' prompts and building on their collaborators' work,
+reducing repetitive updates and communication costs.
+"
+Prompt-Engineering and Transformer-based Question Generation and  Evaluation,Rubaba Amyeen,http://arxiv.org/pdf/2310.18867v1.pdf,2023-10-29,"['cs.cl', 'cs.ai']",2310.18867v1.pdf,"  Question generation has numerous applications in the educational context.
+Question generation can prove helpful for students when reviewing content and
+testing themselves. Furthermore, a question generation model can aid teachers
+by lessening the burden of creating assessments and other practice material.
+This paper aims to find the best method to generate questions from textual data
+through a transformer model and prompt engineering. In this research, we
+finetuned a pretrained distilBERT model on the SQuAD question answering dataset
+to generate questions. In addition to training a transformer model, prompt
+engineering was applied to generate questions effectively using the LLaMA
+model. The generated questions were compared against the baseline questions in
+the SQuAD dataset to evaluate the effectiveness of four different prompts. All
+four prompts demonstrated over 60% similarity on average. Of the
+prompt-generated questions, 30% achieved a high similarity score greater than
+70%.
+"
+A Simple Zero-shot Prompt Weighting Technique to Improve Prompt  Ensembling in Text-Image Models,James Urquhart Allingham,http://arxiv.org/pdf/2302.06235v2.pdf,2023-02-13,"['cs.lg', 'cs.cv', 'stat.ml']",2302.06235v2.pdf,"  Contrastively trained text-image models have the remarkable ability to
+perform zero-shot classification, that is, classifying previously unseen images
+into categories that the model has never been explicitly trained to identify.
+However, these zero-shot classifiers need prompt engineering to achieve high
+accuracy. Prompt engineering typically requires hand-crafting a set of prompts
+for individual downstream tasks. In this work, we aim to automate this prompt
+engineering and improve zero-shot accuracy through prompt ensembling. In
+particular, we ask ""Given a large pool of prompts, can we automatically score
+the prompts and ensemble those that are most suitable for a particular
+downstream dataset, without needing access to labeled validation data?"". We
+demonstrate that this is possible. In doing so, we identify several pathologies
+in a naive prompt scoring method where the score can be easily overconfident
+due to biases in pre-training and test data, and we propose a novel prompt
+scoring method that corrects for the biases. Using our proposed scoring method
+to create a weighted average prompt ensemble, our method outperforms equal
+average ensemble, as well as hand-crafted prompts, on ImageNet, 4 of its
+variants, and 11 fine-grained classification benchmarks, all while being fully
+automatic, optimization-free, and not requiring access to labeled validation
+data.
+"
+Large Language Models in the Workplace: A Case Study on Prompt  Engineering for Job Type Classification,Benjamin Clavié,http://arxiv.org/pdf/2303.07142v3.pdf,2023-03-13,['cs.cl'],2303.07142v3.pdf,"  This case study investigates the task of job classification in a real-world
+setting, where the goal is to determine whether an English-language job posting
+is appropriate for a graduate or entry-level position. We explore multiple
+approaches to text classification, including supervised approaches such as
+traditional models like Support Vector Machines (SVMs) and state-of-the-art
+deep learning methods such as DeBERTa. We compare them with Large Language
+Models (LLMs) used in both few-shot and zero-shot classification settings. To
+accomplish this task, we employ prompt engineering, a technique that involves
+designing prompts to guide the LLMs towards the desired output. Specifically,
+we evaluate the performance of two commercially available state-of-the-art
+GPT-3.5-based language models, text-davinci-003 and gpt-3.5-turbo. We also
+conduct a detailed analysis of the impact of different aspects of prompt
+engineering on the model's performance. Our results show that, with a
+well-designed prompt, a zero-shot gpt-3.5-turbo classifier outperforms all
+other models, achieving a 6% increase in Precision@95% Recall compared to the
+best supervised approach. Furthermore, we observe that the wording of the
+prompt is a critical factor in eliciting the appropriate ""reasoning"" in the
+model, and that seemingly minor aspects of the prompt significantly affect the
+model's performance.
+"
+Simulating H.P. Lovecraft horror literature with the ChatGPT large  language model,Eduardo C. Garrido-Merchán,http://arxiv.org/pdf/2305.03429v1.pdf,2023-05-05,['cs.cl'],2305.03429v1.pdf,"  In this paper, we present a novel approach to simulating H.P. Lovecraft's
+horror literature using the ChatGPT large language model, specifically the
+GPT-4 architecture. Our study aims to generate text that emulates Lovecraft's
+unique writing style and themes, while also examining the effectiveness of
+prompt engineering techniques in guiding the model's output. To achieve this,
+we curated a prompt containing several specialized literature references and
+employed advanced prompt engineering methods. We conducted an empirical
+evaluation of the generated text by administering a survey to a sample of
+undergraduate students. Utilizing statistical hypothesis testing, we assessed
+the students ability to distinguish between genuine Lovecraft works and those
+generated by our model. Our findings demonstrate that the participants were
+unable to reliably differentiate between the two, indicating the effectiveness
+of the GPT-4 model and our prompt engineering techniques in emulating
+Lovecraft's literary style. In addition to presenting the GPT model's
+capabilities, this paper provides a comprehensive description of its underlying
+architecture and offers a comparative analysis with related work that simulates
+other notable authors and philosophers, such as Dennett. By exploring the
+potential of large language models in the context of literary emulation, our
+study contributes to the body of research on the applications and limitations
+of these models in various creative domains.
+"
+CXR-LLaVA: Multimodal Large Language Model for Interpreting Chest X-ray  Images,Seowoo Lee,http://arxiv.org/pdf/2310.18341v2.pdf,2023-10-22,"['cs.cl', 'cs.ai']",2310.18341v2.pdf,"  Purpose: Recent advancements in large language models (LLMs) have expanded
+their capabilities in a multimodal fashion, potentially replicating the image
+interpretation of human radiologists. This study aimed to develop open-source
+multimodal large language model for interpreting chest X-ray images
+(CXR-LLaVA). We also examined the effect of prompt engineering and model
+parameters such as temperature and nucleus sampling.
+  Materials and Methods: For training, we collected 659,287 publicly available
+CXRs: 417,336 CXRs had labels for certain radiographic abnormalities (dataset
+1); 241,951 CXRs provided free-text radiology reports (dataset 2). After
+pre-training the Resnet50 as an image encoder, the contrastive language-image
+pre-training was used to align CXRs and corresponding radiographic
+abnormalities. Then, the Large Language Model Meta AI-2 was fine-tuned using
+dataset 2, which were refined using GPT-4, with generating various question
+answering scenarios. The code can be found at
+https://github.com/ECOFRI/CXR_LLaVA.
+  Results: In the test set, we observed that the model's performance fluctuated
+based on its parameters. On average, it achieved F1 score of 0.34 for five
+pathologic findings (atelectasis, cardiomegaly, consolidation, edema, and
+pleural effusion), which was improved to 0.46 through prompt engineering. In
+the independent set, the model achieved an average F1 score of 0.30 for the
+same pathologic findings. Notably, for the pediatric chest radiograph dataset,
+which was unseen during training, the model differentiated abnormal radiographs
+with an F1 score ranging from 0.84 to 0.85.
+  Conclusion: CXR-LLaVA demonstrates promising potential in CXR interpretation.
+Both prompt engineering and model parameter adjustments can play pivotal roles
+in interpreting CXRs.
+"
+A Taxonomy of Prompt Modifiers for Text-To-Image Generation,Jonas Oppenlaender,http://arxiv.org/pdf/2204.13988v3.pdf,2022-04-20,"['cs.mm', 'cs.cl', 'cs.hc', 'h.5; h.m; j.5']",2204.13988v3.pdf,"  Text-to-image generation has seen an explosion of interest since 2021. Today,
+beautiful and intriguing digital images and artworks can be synthesized from
+textual inputs (""prompts"") with deep generative models. Online communities
+around text-to-image generation and AI generated art have quickly emerged. This
+paper identifies six types of prompt modifiers used by practitioners in the
+online community based on a 3-month ethnographic study. The novel taxonomy of
+prompt modifiers provides researchers a conceptual starting point for
+investigating the practice of text-to-image generation, but may also help
+practitioners of AI generated art improve their images. We further outline how
+prompt modifiers are applied in the practice of ""prompt engineering."" We
+discuss research opportunities of this novel creative practice in the field of
+Human-Computer Interaction (HCI). The paper concludes with a discussion of
+broader implications of prompt engineering from the perspective of Human-AI
+Interaction (HAI) in future applications beyond the use case of text-to-image
+generation and AI generated art.
+"
+What GPT Knows About Who is Who,Xiaohan Yang,http://arxiv.org/pdf/2205.07407v1.pdf,2022-05-16,"['cs.cl', 'cs.lg']",2205.07407v1.pdf,"  Coreference resolution -- which is a crucial task for understanding discourse
+and language at large -- has yet to witness widespread benefits from large
+language models (LLMs). Moreover, coreference resolution systems largely rely
+on supervised labels, which are highly expensive and difficult to annotate,
+thus making it ripe for prompt engineering. In this paper, we introduce a
+QA-based prompt-engineering method and discern \textit{generative}, pre-trained
+LLMs' abilities and limitations toward the task of coreference resolution. Our
+experiments show that GPT-2 and GPT-Neo can return valid answers, but that
+their capabilities to identify coreferent mentions are limited and
+prompt-sensitive, leading to inconsistent results.
+"
+Looking for a Handsome Carpenter! Debiasing GPT-3 Job Advertisements,Conrad Borchers,http://arxiv.org/pdf/2205.11374v1.pdf,2022-05-23,"['cs.cl', 'cs.ai']",2205.11374v1.pdf,"  The growing capability and availability of generative language models has
+enabled a wide range of new downstream tasks. Academic research has identified,
+quantified and mitigated biases present in language models but is rarely
+tailored to downstream tasks where wider impact on individuals and society can
+be felt. In this work, we leverage one popular generative language model,
+GPT-3, with the goal of writing unbiased and realistic job advertisements. We
+first assess the bias and realism of zero-shot generated advertisements and
+compare them to real-world advertisements. We then evaluate prompt-engineering
+and fine-tuning as debiasing methods. We find that prompt-engineering with
+diversity-encouraging prompts gives no significant improvement to bias, nor
+realism. Conversely, fine-tuning, especially on unbiased real advertisements,
+can improve realism and reduce bias.
+"
+Arguments to Key Points Mapping with Prompt-based Learning,Ahnaf Mozib Samin,http://arxiv.org/pdf/2211.14995v1.pdf,2022-11-28,['cs.cl'],2211.14995v1.pdf,"  Handling and digesting a huge amount of information in an efficient manner
+has been a long-term demand in modern society. Some solutions to map key points
+(short textual summaries capturing essential information and filtering
+redundancies) to a large number of arguments/opinions have been provided
+recently (Bar-Haim et al., 2020). To complement the full picture of the
+argument-to-keypoint mapping task, we mainly propose two approaches in this
+paper. The first approach is to incorporate prompt engineering for fine-tuning
+the pre-trained language models (PLMs). The second approach utilizes
+prompt-based learning in PLMs to generate intermediary texts, which are then
+combined with the original argument-keypoint pairs and fed as inputs to a
+classifier, thereby mapping them. Furthermore, we extend the experiments to
+cross/in-domain to conduct an in-depth analysis. In our evaluation, we find
+that i) using prompt engineering in a more direct way (Approach 1) can yield
+promising results and improve the performance; ii) Approach 2 performs
+considerably worse than Approach 1 due to the negation issue of the PLM.
+"
+Legal Prompt Engineering for Multilingual Legal Judgement Prediction,Dietrich Trautmann,http://arxiv.org/pdf/2212.02199v1.pdf,2022-12-05,"['cs.cl', 'cs.ai']",2212.02199v1.pdf,"  Legal Prompt Engineering (LPE) or Legal Prompting is a process to guide and
+assist a large language model (LLM) with performing a natural legal language
+processing (NLLP) skill. Our goal is to use LPE with LLMs over long legal
+documents for the Legal Judgement Prediction (LJP) task. We investigate the
+performance of zero-shot LPE for given facts in case-texts from the European
+Court of Human Rights (in English) and the Federal Supreme Court of Switzerland
+(in German, French and Italian). Our results show that zero-shot LPE is better
+compared to the baselines, but it still falls short compared to current state
+of the art supervised approaches. Nevertheless, the results are important,
+since there was 1) no explicit domain-specific data used - so we show that the
+transfer to the legal domain is possible for general-purpose LLMs, and 2) the
+LLMs where directly applied without any further training or fine-tuning - which
+in turn saves immensely in terms of additional computational costs.
+"
+The Infinite Index: Information Retrieval on Generative Text-To-Image  Models,Niklas Deckers,http://arxiv.org/pdf/2212.07476v2.pdf,2022-12-14,"['cs.ir', 'cs.cl', 'cs.cv']",2212.07476v2.pdf,"  Conditional generative models such as DALL-E and Stable Diffusion generate
+images based on a user-defined text, the prompt. Finding and refining prompts
+that produce a desired image has become the art of prompt engineering.
+Generative models do not provide a built-in retrieval model for a user's
+information need expressed through prompts. In light of an extensive literature
+review, we reframe prompt engineering for generative models as interactive
+text-based retrieval on a novel kind of ""infinite index"". We apply these
+insights for the first time in a case study on image generation for game design
+with an expert. Finally, we envision how active learning may help to guide the
+retrieval of generated images.
+"
+"Artificial Intelligence for Health Message Generation: Theory, Method,  and an Empirical Study Using Prompt Engineering",Sue Lim,http://arxiv.org/pdf/2212.07507v1.pdf,2022-12-14,['cs.cl'],2212.07507v1.pdf,"  This study introduces and examines the potential of an AI system to generate
+health awareness messages. The topic of folic acid, a vitamin that is critical
+during pregnancy, served as a test case. Using prompt engineering, we generated
+messages that could be used to raise awareness and compared them to retweeted
+human-generated messages via computational and human evaluation methods. The
+system was easy to use and prolific, and computational analyses revealed that
+the AI-generated messages were on par with human-generated ones in terms of
+sentiment, reading ease, and semantic content. Also, the human evaluation study
+showed that AI-generated messages ranked higher in message quality and clarity.
+We discuss the theoretical, practical, and ethical implications of these
+results.
+"
+What does CLIP know about a red circle? Visual prompt engineering for  VLMs,Aleksandar Shtedritski,http://arxiv.org/pdf/2304.06712v2.pdf,2023-04-13,['cs.cv'],2304.06712v2.pdf,"  Large-scale Vision-Language Models, such as CLIP, learn powerful image-text
+representations that have found numerous applications, from zero-shot
+classification to text-to-image generation. Despite that, their capabilities
+for solving novel discriminative tasks via prompting fall behind those of large
+language models, such as GPT-3. Here we explore the idea of visual prompt
+engineering for solving computer vision tasks beyond classification by editing
+in image space instead of text. In particular, we discover an emergent ability
+of CLIP, where, by simply drawing a red circle around an object, we can direct
+the model's attention to that region, while also maintaining global
+information. We show the power of this simple approach by achieving
+state-of-the-art in zero-shot referring expressions comprehension and strong
+performance in keypoint localization tasks. Finally, we draw attention to some
+potential ethical concerns of large language-vision models.
+"
+Prompt Engineering for Transformer-based Chemical Similarity Search  Identifies Structurally Distinct Functional Analogues,Clayton W. Kosonocky,http://arxiv.org/pdf/2305.16330v1.pdf,2023-05-17,"['physics.chem-ph', 'cs.lg']",2305.16330v1.pdf,"  Chemical similarity searches are widely used in-silico methods for
+identifying new drug-like molecules. These methods have historically relied on
+structure-based comparisons to compute molecular similarity. Here, we use a
+chemical language model to create a vector-based chemical search. We extend
+implementations by creating a prompt engineering strategy that utilizes two
+different chemical string representation algorithms: one for the query and the
+other for the database. We explore this method by reviewing the search results
+from five drug-like query molecules (penicillin G, nirmatrelvir, zidovudine,
+lysergic acid diethylamide, and fentanyl) and three dye-like query molecules
+(acid blue 25, avobenzone, and 2-diphenylaminocarbazole). We find that this
+novel method identifies molecules that are functionally similar to the query,
+indicated by the associated patent literature, and that many of these molecules
+are structurally distinct from the query, making them unlikely to be found with
+traditional chemical similarity search methods. This method may aid in the
+discovery of novel structural classes of molecules that achieve target
+functionality.
+"
+Submodular Minimax Optimization: Finding Effective Sets,Loay Mualem,http://arxiv.org/pdf/2305.16903v1.pdf,2023-05-26,"['cs.lg', 'cs.dm', 'math.oc', '68r05 (primary) 90c26, 90c20, 68t20, 68w40 (secondary)', 'g.2.1; i.2.m; f.2.2']",2305.16903v1.pdf,"  Despite the rich existing literature about minimax optimization in continuous
+settings, only very partial results of this kind have been obtained for
+combinatorial settings. In this paper, we fill this gap by providing a
+characterization of submodular minimax optimization, the problem of finding a
+set (for either the min or the max player) that is effective against every
+possible response. We show when and under what conditions we can find such
+sets. We also demonstrate how minimax submodular optimization provides robust
+solutions for downstream machine learning applications such as (i) efficient
+prompt engineering for question answering, (ii) prompt engineering for dialog
+state tracking, (iii) identifying robust waiting locations for ride-sharing,
+(iv) ride-share difficulty kernelization, and (v) finding adversarial images.
+Our experiments demonstrate that our proposed algorithms consistently
+outperform other baselines.
+"
+Unsupervised Human Activity Recognition through Two-stage Prompting with  ChatGPT,Qingxin Xia,http://arxiv.org/pdf/2306.02140v1.pdf,2023-06-03,"['cs.hc', 'cs.cl']",2306.02140v1.pdf,"  Wearable sensor devices, which offer the advantage of recording daily objects
+used by a person while performing an activity, enable the feasibility of
+unsupervised Human Activity Recognition (HAR). Unfortunately, previous
+unsupervised approaches using the usage sequence of objects usually require a
+proper description of activities manually prepared by humans. Instead, we
+leverage the knowledge embedded in a Large Language Model (LLM) of ChatGPT.
+Because the sequence of objects robustly characterizes the activity identity,
+it is possible that ChatGPT already learned the association between activities
+and objects from existing contexts. However, previous prompt engineering for
+ChatGPT exhibits limited generalization ability when dealing with a list of
+words (i.e., sequence of objects) due to the similar weighting assigned to each
+word in the list. In this study, we propose a two-stage prompt engineering,
+which first guides ChatGPT to generate activity descriptions associated with
+objects while emphasizing important objects for distinguishing similar
+activities; then outputs activity classes and explanations for enhancing the
+contexts that are helpful for HAR. To the best of our knowledge, this is the
+first study that utilizes ChatGPT to recognize activities using objects in an
+unsupervised manner. We conducted our approach on three datasets and
+demonstrated the state-of-the-art performance.
+"
+User-friendly Image Editing with Minimal Text Input: Leveraging  Captioning and Injection Techniques,Sunwoo Kim,http://arxiv.org/pdf/2306.02717v1.pdf,2023-06-05,['cs.cv'],2306.02717v1.pdf,"  Recent text-driven image editing in diffusion models has shown remarkable
+success. However, the existing methods assume that the user's description
+sufficiently grounds the contexts in the source image, such as objects,
+background, style, and their relations. This assumption is unsuitable for
+real-world applications because users have to manually engineer text prompts to
+find optimal descriptions for different images. From the users' standpoint,
+prompt engineering is a labor-intensive process, and users prefer to provide a
+target word for editing instead of a full sentence. To address this problem, we
+first demonstrate the importance of a detailed text description of the source
+image, by dividing prompts into three categories based on the level of semantic
+details. Then, we propose simple yet effective methods by combining prompt
+generation frameworks, thereby making the prompt engineering process more
+user-friendly. Extensive qualitative and quantitative experiments demonstrate
+the importance of prompts in text-driven image editing and our method is
+comparable to ground-truth prompts.
+"
+PromptMagician: Interactive Prompt Engineering for Text-to-Image  Creation,Yingchaojie Feng,http://arxiv.org/pdf/2307.09036v2.pdf,2023-07-18,"['cs.ai', 'cs.hc']",2307.09036v2.pdf,"  Generative text-to-image models have gained great popularity among the public
+for their powerful capability to generate high-quality images based on natural
+language prompts. However, developing effective prompts for desired images can
+be challenging due to the complexity and ambiguity of natural language. This
+research proposes PromptMagician, a visual analysis system that helps users
+explore the image results and refine the input prompts. The backbone of our
+system is a prompt recommendation model that takes user prompts as input,
+retrieves similar prompt-image pairs from DiffusionDB, and identifies special
+(important and relevant) prompt keywords. To facilitate interactive prompt
+refinement, PromptMagician introduces a multi-level visualization for the
+cross-modal embedding of the retrieved images and recommended keywords, and
+supports users in specifying multiple criteria for personalized exploration.
+Two usage scenarios, a user study, and expert interviews demonstrate the
+effectiveness and usability of our system, suggesting it facilitates prompt
+engineering and improves the creativity support of the generative text-to-image
+model.
+"
+Is GPT a Computational Model of Emotion? Detailed Analysis,Ala N. Tak,http://arxiv.org/pdf/2307.13779v1.pdf,2023-07-25,"['cs.cl', 'cs.ai', 'cs.cy', 'cs.hc']",2307.13779v1.pdf,"  This paper investigates the emotional reasoning abilities of the GPT family
+of large language models via a component perspective. The paper first examines
+how the model reasons about autobiographical memories. Second, it
+systematically varies aspects of situations to impact emotion intensity and
+coping tendencies. Even without the use of prompt engineering, it is shown that
+GPT's predictions align significantly with human-provided appraisals and
+emotional labels. However, GPT faces difficulties predicting emotion intensity
+and coping responses. GPT-4 showed the highest performance in the initial study
+but fell short in the second, despite providing superior results after minor
+prompt engineering. This assessment brings up questions on how to effectively
+employ the strong points and address the weak areas of these models,
+particularly concerning response variability. These studies underscore the
+merits of evaluating models from a componential perspective.
+"
+Prompts Matter: Insights and Strategies for Prompt Engineering in  Automated Software Traceability,Alberto D. Rodriguez,http://arxiv.org/pdf/2308.00229v1.pdf,2023-08-01,['cs.se'],2308.00229v1.pdf,"  Large Language Models (LLMs) have the potential to revolutionize automated
+traceability by overcoming the challenges faced by previous methods and
+introducing new possibilities. However, the optimal utilization of LLMs for
+automated traceability remains unclear. This paper explores the process of
+prompt engineering to extract link predictions from an LLM. We provide detailed
+insights into our approach for constructing effective prompts, offering our
+lessons learned. Additionally, we propose multiple strategies for leveraging
+LLMs to generate traceability links, improving upon previous zero-shot methods
+on the ranking of candidate links after prompt refinement. The primary
+objective of this paper is to inspire and assist future researchers and
+engineers by highlighting the process of constructing traceability prompts to
+effectively harness LLMs for advancing automatic traceability.
+"
+CoT-BERT: Enhancing Unsupervised Sentence Representation through  Chain-of-Thought,Bowen Zhang,http://arxiv.org/pdf/2309.11143v1.pdf,2023-09-20,"['cs.cl', 'cs.ai']",2309.11143v1.pdf,"  Unsupervised sentence representation learning aims to transform input
+sentences into fixed-length vectors enriched with intricate semantic
+information while obviating the reliance on labeled data. Recent progress
+within this field, propelled by contrastive learning and prompt engineering,
+has significantly bridged the gap between unsupervised and supervised
+strategies. Nonetheless, the potential utilization of Chain-of-Thought, remains
+largely untapped within this trajectory. To unlock latent capabilities within
+pre-trained models, such as BERT, we propose a two-stage approach for sentence
+representation: comprehension and summarization. Subsequently, the output of
+the latter phase is harnessed as the vectorized representation of the input
+sentence. For further performance enhancement, we meticulously refine both the
+contrastive learning loss function and the template denoising technique for
+prompt engineering. Rigorous experimentation substantiates our method,
+CoT-BERT, transcending a suite of robust baselines without necessitating other
+text representation models or external databases.
+"
+How does prompt engineering affect ChatGPT performance on unsupervised  entity resolution?,Khanin Sisaengsuwanchai,http://arxiv.org/pdf/2310.06174v1.pdf,2023-10-09,"['cs.ai', 'cs.se']",2310.06174v1.pdf,"  Entity Resolution (ER) is the problem of semi-automatically determining when
+two entities refer to the same underlying entity, with applications ranging
+from healthcare to e-commerce. Traditional ER solutions required considerable
+manual expertise, including feature engineering, as well as identification and
+curation of training data. In many instances, such techniques are highly
+dependent on the domain. With recent advent in large language models (LLMs),
+there is an opportunity to make ER much more seamless and domain-independent.
+However, it is also well known that LLMs can pose risks, and that the quality
+of their outputs can depend on so-called prompt engineering. Unfortunately, a
+systematic experimental study on the effects of different prompting methods for
+addressing ER, using LLMs like ChatGPT, has been lacking thus far. This paper
+aims to address this gap by conducting such a study. Although preliminary in
+nature, our results show that prompting can significantly affect the quality of
+ER, although it affects some metrics more than others, and can also be dataset
+dependent.
+"
+Interactive Task Planning with Language Models,Boyi Li,http://arxiv.org/pdf/2310.10645v1.pdf,2023-10-16,"['cs.ro', 'cs.ai', 'cs.cl', 'cs.hc']",2310.10645v1.pdf,"  An interactive robot framework accomplishes long-horizon task planning and
+can easily generalize to new goals or distinct tasks, even during execution.
+However, most traditional methods require predefined module design, which makes
+it hard to generalize to different goals. Recent large language model based
+approaches can allow for more open-ended planning but often require heavy
+prompt engineering or domain-specific pretrained models. To tackle this, we
+propose a simple framework that achieves interactive task planning with
+language models. Our system incorporates both high-level planning and low-level
+function execution via language. We verify the robustness of our system in
+generating novel high-level instructions for unseen objectives and its ease of
+adaptation to different tasks by merely substituting the task guidelines,
+without the need for additional complex prompt engineering. Furthermore, when
+the user sends a new request, our system is able to replan accordingly with
+precision based on the new request, task guidelines and previously executed
+steps. Please check more details on our https://wuphilipp.github.io/itp_site
+and https://youtu.be/TrKLuyv26_g.
+"
+Prompt Engineering Through the Lens of Optimal Control,Yifan Luo,http://arxiv.org/pdf/2310.14201v2.pdf,2023-10-22,"['cs.lg', 'math.oc']",2310.14201v2.pdf,"  Prompt Engineering (PE) has emerged as a critical technique for guiding Large
+Language Models (LLMs) in solving intricate tasks. Its importance is
+highlighted by its potential to significantly enhance the efficiency and
+effectiveness of human-machine interaction. As tasks grow increasingly complex,
+recent advanced PE methods have extended beyond the limitations of single-round
+interactions to embrace multi-round interactions, which allows for a deeper and
+more nuanced engagement with LLMs. In this paper, we propose an optimal control
+framework tailored for multi-round interactions with LLMs. This framework
+provides a unified mathematical structure that not only systematizes the
+existing PE methods but also sets the stage for rigorous analytical
+improvements. Furthermore, we extend this framework to include PE via ensemble
+methods and multi-agent collaboration, thereby enlarging the scope of
+applicability. By adopting an optimal control perspective, we offer fresh
+insights into existing PE methods and highlight theoretical challenges that
+warrant future research. Besides, our work lays a foundation for the
+development of more effective and interpretable PE methods.
+"
+A Communication Theory Perspective on Prompting Engineering Methods for  Large Language Models,Yuanfeng Song,http://arxiv.org/pdf/2310.18358v1.pdf,2023-10-24,"['cs.cl', 'cs.ai']",2310.18358v1.pdf,"  The springing up of Large Language Models (LLMs) has shifted the community
+from single-task-orientated natural language processing (NLP) research to a
+holistic end-to-end multi-task learning paradigm. Along this line of research
+endeavors in the area, LLM-based prompting methods have attracted much
+attention, partially due to the technological advantages brought by prompt
+engineering (PE) as well as the underlying NLP principles disclosed by various
+prompting methods. Traditional supervised learning usually requires training a
+model based on labeled data and then making predictions. In contrast, PE
+methods directly use the powerful capabilities of existing LLMs (i.e., GPT-3
+and GPT-4) via composing appropriate prompts, especially under few-shot or
+zero-shot scenarios. Facing the abundance of studies related to the prompting
+and the ever-evolving nature of this field, this article aims to (i) illustrate
+a novel perspective to review existing PE methods, within the well-established
+communication theory framework; (ii) facilitate a better/deeper understanding
+of developing trends of existing PE methods used in four typical tasks; (iii)
+shed light on promising research directions for future PE methods.
+"
+Apollo: Zero-shot MultiModal Reasoning with Multiple Experts,Daniela Ben-David,http://arxiv.org/pdf/2310.18369v1.pdf,2023-10-25,"['cs.cl', 'cs.ai', 'cs.cv', 'i.2.7; i.5.4']",2310.18369v1.pdf,"  We propose a modular framework that leverages the expertise of different
+foundation models over different modalities and domains in order to perform a
+single, complex, multi-modal task, without relying on prompt engineering or
+otherwise tailor-made multi-modal training. Our approach enables decentralized
+command execution and allows each model to both contribute and benefit from the
+expertise of the other models. Our method can be extended to a variety of
+foundation models (including audio and vision), above and beyond only language
+models, as it does not depend on prompts. We demonstrate our approach on two
+tasks. On the well-known task of stylized image captioning, our experiments
+show that our approach outperforms semi-supervised state-of-the-art models,
+while being zero-shot and avoiding costly training, data collection, and prompt
+engineering. We further demonstrate this method on a novel task, audio-aware
+image captioning, in which an image and audio are given and the task is to
+generate text that describes the image within the context of the provided
+audio. Our code is available on GitHub.
+"
+Towards Zero-Shot and Few-Shot Table Question Answering using GPT-3,Pragya Srivastava,http://arxiv.org/pdf/2210.17284v1.pdf,2022-10-31,"['cs.lg', '14j60 (primary)']",2210.17284v1.pdf,"  We present very early results on using GPT-3 to perform question answering on
+tabular data. We find that stock pre-trained GPT-3 is able to zero-shot learn
+the table structure from a serialized JSON array-of-arrays representation, and
+able to answer lookup queries and simple comparison questions in natural
+language without any fine-tuning. We further find that simple prompt
+engineering to include few-shot static Q&A examples significantly improves
+accuracy. Lastly, we find that intermixing passage text improves accuracy even
+further on heterogeneous data. We apply our approach on a novel dataset of
+simple tables in newspaper infographics with promising results. Overall, we
+find much cause for optimism in this basic approach.
+"
+Investigating Prompt Engineering in Diffusion Models,Sam Witteveen,http://arxiv.org/pdf/2211.15462v1.pdf,2022-11-21,"['cs.cv', 'cs.ai', 'cs.cl']",2211.15462v1.pdf,"  With the spread of the use of Text2Img diffusion models such as DALL-E 2,
+Imagen, Mid Journey and Stable Diffusion, one challenge that artists face is
+selecting the right prompts to achieve the desired artistic output. We present
+techniques for measuring the effect that specific words and phrases in prompts
+have, and (in the Appendix) present guidance on the selection of prompts to
+produce desired effects.
+"
+Refining the Responses of LLMs by Themselves,Tianqiang Yan,http://arxiv.org/pdf/2305.04039v1.pdf,2023-05-06,"['cs.cl', 'cs.ai']",2305.04039v1.pdf,"  In this paper, we propose a simple yet efficient approach based on prompt
+engineering that leverages the large language model itself to optimize its
+answers without relying on auxiliary models. We introduce an iterative
+self-evaluating optimization mechanism, with the potential for improved output
+quality as iterations progress, removing the need for manual intervention. The
+experiment's findings indicate that utilizing our response refinement framework
+on the GPT-3.5 model yields results that are on par with, or even surpass,
+those generated by the cutting-edge GPT-4 model. Detailed implementation
+strategies and illustrative examples are provided to demonstrate the
+superiority of our proposed solution.
+"
+Efficient Black-Box Adversarial Attacks on Neural Text Detectors,Vitalii Fishchuk,http://arxiv.org/pdf/2311.01873v1.pdf,2023-11-03,['cs.cl'],2311.01873v1.pdf,"  Neural text detectors are models trained to detect whether a given text was
+generated by a language model or written by a human. In this paper, we
+investigate three simple and resource-efficient strategies (parameter tweaking,
+prompt engineering, and character-level mutations) to alter texts generated by
+GPT-3.5 that are unsuspicious or unnoticeable for humans but cause
+misclassification by neural text detectors. The results show that especially
+parameter tweaking and character-level mutations are effective strategies.
+"
+Prompted Software Engineering in the Era of AI Models,Dae-Kyoo Kim,http://arxiv.org/pdf/2311.03359v1.pdf,2023-09-07,['cs.se'],2311.03359v1.pdf,"  This paper introduces prompted software engineering (PSE), which integrates
+prompt engineering to build effective prompts for language-based AI models, to
+enhance the software development process. PSE enables the use of AI models in
+software development to produce high-quality software with fewer resources,
+automating tedious tasks and allowing developers to focus on more innovative
+aspects. However, effective prompts are necessary to guide software development
+in generating accurate, relevant, and useful responses, while mitigating risks
+of misleading outputs. This paper describes how productive prompts should be
+built throughout the software development cycle.
+"
+Conversing with Copilot: Exploring Prompt Engineering for Solving CS1  Problems Using Natural Language,Paul Denny,http://arxiv.org/pdf/2210.15157v1.pdf,2022-10-27,"['cs.hc', 'cs.ai']",2210.15157v1.pdf,"  GitHub Copilot is an artificial intelligence model for automatically
+generating source code from natural language problem descriptions. Since June
+2022, Copilot has officially been available for free to all students as a
+plug-in to development environments like Visual Studio Code. Prior work
+exploring OpenAI Codex, the underlying model that powers Copilot, has shown it
+performs well on typical CS1 problems thus raising concerns about the impact it
+will have on how introductory programming courses are taught. However, little
+is known about the types of problems for which Copilot does not perform well,
+or about the natural language interactions that a student might have with
+Copilot when resolving errors. We explore these questions by evaluating the
+performance of Copilot on a publicly available dataset of 166 programming
+problems. We find that it successfully solves around half of these problems on
+its very first attempt, and that it solves 60\% of the remaining problems using
+only natural language changes to the problem description. We argue that this
+type of prompt engineering, which we believe will become a standard interaction
+between human and Copilot when it initially fails, is a potentially useful
+learning activity that promotes computational thinking skills, and is likely to
+change the nature of code writing skill development.
+"
+ChatGPT4PCG Competition: Character-like Level Generation for Science  Birds,Pittawat Taveekitworachai,http://arxiv.org/pdf/2303.15662v2.pdf,2023-03-28,"['cs.ai', 'cs.cl', 'i.2.7; i.2.8']",2303.15662v2.pdf,"  This paper presents the first ChatGPT4PCG Competition at the 2023 IEEE
+Conference on Games. The objective of this competition is for participants to
+create effective prompts for ChatGPT--enabling it to generate Science Birds
+levels with high stability and character-like qualities--fully using their
+creativity as well as prompt engineering skills. ChatGPT is a conversational
+agent developed by OpenAI. Science Birds is selected as the competition
+platform because designing an Angry Birds-like level is not a trivial task due
+to the in-game gravity; the quality of the levels is determined by their
+stability. To lower the entry barrier to the competition, we limit the task to
+the generation of capitalized English alphabetical characters. We also allow
+only a single prompt to be used for generating all the characters. Here, the
+quality of the generated levels is determined by their stability and similarity
+to the given characters. A sample prompt is provided to participants for their
+reference. An experiment is conducted to determine the effectiveness of several
+modified versions of this sample prompt on level stability and similarity by
+testing them on several characters. To the best of our knowledge, we believe
+that ChatGPT4PCG is the first competition of its kind and hope to inspire
+enthusiasm for prompt engineering in procedural content generation.
+"
+Enhancing Automated Program Repair through Fine-tuning and Prompt  Engineering,Rishov Paul,http://arxiv.org/pdf/2304.07840v2.pdf,2023-04-16,"['cs.lg', 'cs.se']",2304.07840v2.pdf,"  Sequence-to-sequence models have been used to transform erroneous programs
+into correct ones when trained with a large enough dataset. Some recent studies
+also demonstrated strong empirical evidence that code review could improve the
+program repair further. Large language models, trained with Natural Language
+(NL) and Programming Language (PL), can contain inherent knowledge of both. In
+this study, we investigate if this inherent knowledge of PL and NL can be
+utilized to improve automated program repair. We applied PLBART and CodeT5, two
+state-of-the-art language models that are pre-trained with both PL and NL, on
+two such natural language-based program repair datasets and found that the
+pre-trained language models fine-tuned with datasets containing both code
+review and subsequent code changes notably outperformed each of the previous
+models. With the advent of code generative models like Codex and GPT-3.5-Turbo,
+we also performed zero-shot and few-shots learning-based prompt engineering to
+assess their performance on these datasets. However, the practical application
+of using LLMs in the context of automated program repair is still a long way
+off based on our manual analysis of the generated repaired codes by the
+learning models.
+"
+Conceptual Design Generation Using Large Language Models,Kevin Ma,http://arxiv.org/pdf/2306.01779v1.pdf,2023-05-30,"['cs.cl', 'cs.ai']",2306.01779v1.pdf,"  Concept generation is a creative step in the conceptual design phase, where
+designers often turn to brainstorming, mindmapping, or crowdsourcing design
+ideas to complement their own knowledge of the domain. Recent advances in
+natural language processing (NLP) and machine learning (ML) have led to the
+rise of Large Language Models (LLMs) capable of generating seemingly creative
+outputs from textual prompts. The success of these models has led to their
+integration and application across a variety of domains, including art,
+entertainment, and other creative work. In this paper, we leverage LLMs to
+generate solutions for a set of 12 design problems and compare them to a
+baseline of crowdsourced solutions. We evaluate the differences between
+generated and crowdsourced design solutions through multiple perspectives,
+including human expert evaluations and computational metrics. Expert
+evaluations indicate that the LLM-generated solutions have higher average
+feasibility and usefulness while the crowdsourced solutions have more novelty.
+We experiment with prompt engineering and find that leveraging few-shot
+learning can lead to the generation of solutions that are more similar to the
+crowdsourced solutions. These findings provide insight into the quality of
+design solutions generated with LLMs and begins to evaluate prompt engineering
+techniques that could be leveraged by practitioners to generate higher-quality
+design solutions synergistically with LLMs.
+"
+Cheap-fake Detection with LLM using Prompt Engineering,Guangyang Wu,http://arxiv.org/pdf/2306.02776v1.pdf,2023-06-05,['cs.cv'],2306.02776v1.pdf,"  The misuse of real photographs with conflicting image captions in news items
+is an example of the out-of-context (OOC) misuse of media. In order to detect
+OOC media, individuals must determine the accuracy of the statement and
+evaluate whether the triplet (~\textit{i.e.}, the image and two captions)
+relates to the same event. This paper presents a novel learnable approach for
+detecting OOC media in ICME'23 Grand Challenge on Detecting Cheapfakes. The
+proposed method is based on the COSMOS structure, which assesses the coherence
+between an image and captions, as well as between two captions. We enhance the
+baseline algorithm by incorporating a Large Language Model (LLM), GPT3.5, as a
+feature extractor. Specifically, we propose an innovative approach to feature
+extraction utilizing prompt engineering to develop a robust and reliable
+feature extractor with GPT3.5 model. The proposed method captures the
+correlation between two captions and effectively integrates this module into
+the COSMOS baseline model, which allows for a deeper understanding of the
+relationship between captions. By incorporating this module, we demonstrate the
+potential for significant improvements in cheap-fakes detection performance.
+The proposed methodology holds promising implications for various applications
+such as natural language processing, image captioning, and text-to-image
+synthesis. Docker for submission is available at
+https://hub.docker.com/repository/docker/mulns/ acmmmcheapfakes.
+"
+Improving Knowledge Extraction from LLMs for Task Learning through Agent  Analysis,James R. Kirk,http://arxiv.org/pdf/2306.06770v3.pdf,2023-06-11,"['cs.ai', 'cs.hc', 'cs.ro', 'i.2.6; i.2.7']",2306.06770v3.pdf,"  Large language models (LLMs) offer significant promise as a knowledge source
+for task learning. Prompt engineering has been shown to be effective for
+eliciting knowledge from an LLM, but alone it is insufficient for acquiring
+relevant, situationally grounded knowledge for an embodied agent learning novel
+tasks. We describe a cognitive-agent approach that extends and complements
+prompt engineering, mitigating its limitations and thus enabling an agent to
+acquire new task knowledge matched to its native language capabilities,
+embodiment, environment, and user preferences. The approach is to increase the
+response space of LLMs and deploy general strategies, embedded within the
+autonomous agent, to evaluate, repair, and select among candidate responses
+produced by the LLM. We describe the approach and experiments that show how an
+agent, by retrieving and evaluating a breadth of responses from the LLM, can
+achieve 77-94% task completion in one-shot learning without user oversight. The
+approach achieves 100% task completion when human oversight (such as an
+indication of preference) is provided. Further, the type of oversight largely
+shifts from explicit, natural language instruction to simple
+confirmation/discomfirmation of high-quality responses that have been vetted by
+the agent before presentation to a user.
+"
+ChatGPT for Robotics: Design Principles and Model Abilities,Sai Vemprala,http://arxiv.org/pdf/2306.17582v2.pdf,2023-02-20,"['cs.ai', 'cs.cl', 'cs.hc', 'cs.lg', 'cs.ro']",2306.17582v2.pdf,"  This paper presents an experimental study regarding the use of OpenAI's
+ChatGPT for robotics applications. We outline a strategy that combines design
+principles for prompt engineering and the creation of a high-level function
+library which allows ChatGPT to adapt to different robotics tasks, simulators,
+and form factors. We focus our evaluations on the effectiveness of different
+prompt engineering techniques and dialog strategies towards the execution of
+various types of robotics tasks. We explore ChatGPT's ability to use free-form
+dialog, parse XML tags, and to synthesize code, in addition to the use of
+task-specific prompting functions and closed-loop reasoning through dialogues.
+Our study encompasses a range of tasks within the robotics domain, from basic
+logical, geometrical, and mathematical reasoning all the way to complex domains
+such as aerial navigation, manipulation, and embodied agents. We show that
+ChatGPT can be effective at solving several of such tasks, while allowing users
+to interact with it primarily via natural language instructions. In addition to
+these studies, we introduce an open-sourced research tool called PromptCraft,
+which contains a platform where researchers can collaboratively upload and vote
+on examples of good prompting schemes for robotics applications, as well as a
+sample robotics simulator with ChatGPT integration, making it easier for users
+to get started with using ChatGPT for robotics.
+"
+Cases of EFL Secondary Students' Prompt Engineering Pathways to Complete  a Writing Task with ChatGPT,David James Woo,http://arxiv.org/pdf/2307.05493v1.pdf,2023-06-19,"['cs.hc', 'cs.ai', 'cs.cl']",2307.05493v1.pdf,"  ChatGPT is a state-of-the-art (SOTA) chatbot. Although it has potential to
+support English as a foreign language (EFL) students' writing, to effectively
+collaborate with it, a student must learn to engineer prompts, that is, the
+skill of crafting appropriate instructions so that ChatGPT produces desired
+outputs. However, writing an appropriate prompt for ChatGPT is not
+straightforward for non-technical users who suffer a trial-and-error process.
+This paper examines the content of EFL students' ChatGPT prompts when
+completing a writing task and explores patterns in the quality and quantity of
+the prompts. The data come from iPad screen recordings of secondary school EFL
+students who used ChatGPT and other SOTA chatbots for the first time to
+complete the same writing task. The paper presents a case study of four
+distinct pathways that illustrate the trial-and-error process and show
+different combinations of prompt content and quantity. The cases contribute
+evidence for the need to provide prompt engineering education in the context of
+the EFL writing classroom, if students are to move beyond an individual
+trial-and-error process, learning a greater variety of prompt content and more
+sophisticated prompts to support their writing.
+"
+"Multi-party Goal Tracking with LLMs: Comparing Pre-training,  Fine-tuning, and Prompt Engineering",Angus Addlesee,http://arxiv.org/pdf/2308.15231v1.pdf,2023-08-29,"['cs.cl', 'cs.hc']",2308.15231v1.pdf,"  This paper evaluates the extent to which current Large Language Models (LLMs)
+can capture task-oriented multi-party conversations (MPCs). We have recorded
+and transcribed 29 MPCs between patients, their companions, and a social robot
+in a hospital. We then annotated this corpus for multi-party goal-tracking and
+intent-slot recognition. People share goals, answer each other's goals, and
+provide other people's goals in MPCs - none of which occur in dyadic
+interactions. To understand user goals in MPCs, we compared three methods in
+zero-shot and few-shot settings: we fine-tuned T5, created pre-training tasks
+to train DialogLM using LED, and employed prompt engineering techniques with
+GPT-3.5-turbo, to determine which approach can complete this novel task with
+limited data. GPT-3.5-turbo significantly outperformed the others in a few-shot
+setting. The `reasoning' style prompt, when given 7% of the corpus as example
+annotated conversations, was the best performing method. It correctly annotated
+62.32% of the goal tracking MPCs, and 69.57% of the intent-slot recognition
+MPCs. A `story' style prompt increased model hallucination, which could be
+detrimental if deployed in safety-critical settings. We conclude that
+multi-party conversations still challenge state-of-the-art LLMs.
+"
+Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation,Dawei Gao,http://arxiv.org/pdf/2308.15363v3.pdf,2023-08-29,"['cs.db', 'cs.cl', 'cs.lg']",2308.15363v3.pdf,"  Large language models (LLMs) have emerged as a new paradigm for Text-to-SQL
+task. However, the absence of a systematical benchmark inhibits the development
+of designing effective, efficient and economic LLM-based Text-to-SQL solutions.
+To address this challenge, in this paper, we first conduct a systematical and
+extensive comparison over existing prompt engineering methods, including
+question representation, example selection and example organization, and with
+these experimental results, we elaborate their pros and cons. Based on these
+findings, we propose a new integrated solution, named DAIL-SQL, which refreshes
+the Spider leaderboard with 86.6% execution accuracy and sets a new bar. To
+explore the potential of open-source LLM, we investigate them in various
+scenarios, and further enhance their performance with supervised fine-tuning.
+Our explorations highlight open-source LLMs' potential in Text-to-SQL, as well
+as the advantages and disadvantages of the supervised fine-tuning.
+Additionally, towards an efficient and economic LLM-based Text-to-SQL solution,
+we emphasize the token efficiency in prompt engineering and compare the prior
+studies under this metric. We hope that our work provides a deeper
+understanding of Text-to-SQL with LLMs, and inspires further investigations and
+broad applications.
+"
+PRE: Vision-Language Prompt Learning with Reparameterization Encoder,Anh Pham Thi Minh,http://arxiv.org/pdf/2309.07760v2.pdf,2023-09-14,"['cs.cv', 'cs.ai', 'cs.lg', 'i.4.0']",2309.07760v2.pdf,"  Large pre-trained vision-language models such as CLIP have demonstrated great
+potential in zero-shot transferability to downstream tasks. However, to attain
+optimal performance, the manual selection of prompts is necessary to improve
+alignment between the downstream image distribution and the textual class
+descriptions. This manual prompt engineering is the major challenge for
+deploying such models in practice since it requires domain expertise and is
+extremely time-consuming. To avoid non-trivial prompt engineering, recent work
+Context Optimization (CoOp) introduced the concept of prompt learning to the
+vision domain using learnable textual tokens. While CoOp can achieve
+substantial improvements over manual prompts, its learned context is worse
+generalizable to wider unseen classes within the same dataset. In this work, we
+present Prompt Learning with Reparameterization Encoder (PRE) - a simple and
+efficient method that enhances the generalization ability of the learnable
+prompt to unseen classes while maintaining the capacity to learn Base classes.
+Instead of directly optimizing the prompts, PRE employs a prompt encoder to
+reparameterize the input prompt embeddings, enhancing the exploration of
+task-specific knowledge from few-shot samples. Experiments and extensive
+ablation studies on 8 benchmarks demonstrate that our approach is an efficient
+method for prompt learning. Specifically, PRE achieves a notable enhancement of
+5.60% in average accuracy on New classes and 3% in Harmonic mean compared to
+CoOp in the 16-shot setting, all achieved within a good training time.
+"
+PEACE: Prompt Engineering Automation for CLIPSeg Enhancement in Aerial  Robotics,Haechan Mark Bong,http://arxiv.org/pdf/2310.00085v1.pdf,2023-09-29,['cs.ro'],2310.00085v1.pdf,"  From industrial to space robotics, safe landing is an essential component for
+flight operations. With the growing interest in artificial intelligence, we
+direct our attention to learning based safe landing approaches. This paper
+extends our previous work, DOVESEI, which focused on a reactive UAV system by
+harnessing the capabilities of open vocabulary image segmentation. Prompt-based
+safe landing zone segmentation using an open vocabulary based model is no more
+just an idea, but proven to be feasible by the work of DOVESEI. However, a
+heuristic selection of words for prompt is not a reliable solution since it
+cannot take the changing environment into consideration and detrimental
+consequences can occur if the observed environment is not well represented by
+the given prompt. Therefore, we introduce PEACE (Prompt Engineering Automation
+for CLIPSeg Enhancement), powering DOVESEI to automate the prompt generation
+and engineering to adapt to data distribution shifts. Our system is capable of
+performing safe landing operations with collision avoidance at altitudes as low
+as 20 meters using only monocular cameras and image segmentation. We take
+advantage of DOVESEI's dynamic focus to circumvent abrupt fluctuations in the
+terrain segmentation between frames in a video stream. PEACE shows promising
+improvements in prompt generation and engineering for aerial images compared to
+the standard prompt used for CLIP and CLIPSeg. Combining DOVESEI and PEACE, our
+system was able improve successful safe landing zone selections by 58.62%
+compared to using only DOVESEI. All the source code is open source and
+available online.
+"
+Understanding prompt engineering may not require rethinking  generalization,Victor Akinwande,http://arxiv.org/pdf/2310.03957v1.pdf,2023-10-06,"['cs.lg', 'cs.cv']",2310.03957v1.pdf,"  Zero-shot learning in prompted vision-language models, the practice of
+crafting prompts to build classifiers without an explicit training process, has
+achieved impressive performance in many settings. This success presents a
+seemingly surprising observation: these methods suffer relatively little from
+overfitting, i.e., when a prompt is manually engineered to achieve low error on
+a given training set (thus rendering the method no longer actually zero-shot),
+the approach still performs well on held-out test data. In this paper, we show
+that we can explain such performance well via recourse to classical PAC-Bayes
+bounds. Specifically, we show that the discrete nature of prompts, combined
+with a PAC-Bayes prior given by a language model, results in generalization
+bounds that are remarkably tight by the standards of the literature: for
+instance, the generalization bound of an ImageNet classifier is often within a
+few percentage points of the true test error. We demonstrate empirically that
+this holds for existing handcrafted prompts and prompts generated through
+simple greedy search. Furthermore, the resulting bound is well-suited for model
+selection: the models with the best bound typically also have the best test
+performance. This work thus provides a possible justification for the
+widespread practice of prompt engineering, even if it seems that such methods
+could potentially overfit the training data.
+"
+What's the Magic Word? A Control Theory of LLM Prompting,Aman Bhargava,http://arxiv.org/pdf/2310.04444v2.pdf,2023-10-02,"['cs.cl', 'cs.ai', 'cs.lg', 'cs.ne']",2310.04444v2.pdf,"  Prompt engineering is effective and important in the deployment of LLMs but
+is poorly understood mathematically. Here, we formalize prompt engineering as
+an optimal control problem on LLMs -- where the prompt is considered a control
+variable for modulating the output distribution of the LLM. Within this
+framework, we ask a simple question: given a sequence of tokens, does there
+always exist a prompt we can prepend that will steer the LLM toward accurately
+predicting the final token? We call such an optimal prompt the magic word since
+prepending the prompt causes the LLM to output the correct answer. If magic
+words exist, can we find them? If so, what are their properties? We offer
+analytic analysis on the controllability of the self-attention head where we
+prove a bound on controllability as a function of the singular values of its
+weight matrices. We take inspiration from control theory to propose a metric
+called $k-\epsilon$ controllability to characterize LLM steerability. We
+compute the $k-\epsilon$ controllability of a panel of large language models,
+including Falcon-7b, Llama-7b, and Falcon-40b on 5000 WikiText causal language
+modeling tasks. Remarkably, we find that magic words of 10 tokens or less exist
+for over 97% of WikiText instances surveyed for each model.
+"
+Configuration Validation with Large Language Models,Xinyu Lian,http://arxiv.org/pdf/2310.09690v1.pdf,2023-10-15,"['cs.se', 'cs.ai', 'cs.os']",2310.09690v1.pdf,"  Misconfigurations are the major causes of software failures. Existing
+configuration validation techniques rely on manually written rules or test
+cases, which are expensive to implement and maintain, and are hard to be
+comprehensive. Leveraging machine learning (ML) and natural language processing
+(NLP) for configuration validation is considered a promising direction, but has
+been facing challenges such as the need of not only large-scale configuration
+data, but also system-specific features and models which are hard to
+generalize. Recent advances in Large Language Models (LLMs) show the promises
+to address some of the long-lasting limitations of ML/NLP-based configuration
+validation techniques. In this paper, we present an exploratory analysis on the
+feasibility and effectiveness of using LLMs like GPT and Codex for
+configuration validation. Specifically, we take a first step to empirically
+evaluate LLMs as configuration validators without additional fine-tuning or
+code generation. We develop a generic LLM-based validation framework, named
+Ciri, which integrates different LLMs. Ciri devises effective prompt
+engineering with few-shot learning based on both valid configuration and
+misconfiguration data. Ciri also validates and aggregates the outputs of LLMs
+to generate validation results, coping with known hallucination and
+nondeterminism of LLMs. We evaluate the validation effectiveness of Ciri on
+five popular LLMs using configuration data of six mature, widely deployed
+open-source systems. Our analysis (1) confirms the potential of using LLMs for
+configuration validation, (2) understands the design space of LLMbased
+validators like Ciri, especially in terms of prompt engineering with few-shot
+learning, and (3) reveals open challenges such as ineffectiveness in detecting
+certain types of misconfigurations and biases to popular configuration
+parameters.
+"
+Learning to Prompt for Vision-Language Models,Kaiyang Zhou,http://arxiv.org/pdf/2109.01134v6.pdf,2021-09-02,"['cs.cv', 'cs.ai', 'cs.lg']",2109.01134v6.pdf,"  Large pre-trained vision-language models like CLIP have shown great potential
+in learning representations that are transferable across a wide range of
+downstream tasks. Different from the traditional representation learning that
+is based mostly on discretized labels, vision-language pre-training aligns
+images and texts in a common feature space, which allows zero-shot transfer to
+a downstream task via prompting, i.e., classification weights are synthesized
+from natural language describing classes of interest. In this work, we show
+that a major challenge for deploying such models in practice is prompt
+engineering, which requires domain expertise and is extremely time-consuming --
+one needs to spend a significant amount of time on words tuning since a slight
+change in wording could have a huge impact on performance. Inspired by recent
+advances in prompt learning research in natural language processing (NLP), we
+propose Context Optimization (CoOp), a simple approach specifically for
+adapting CLIP-like vision-language models for downstream image recognition.
+Concretely, CoOp models a prompt's context words with learnable vectors while
+the entire pre-trained parameters are kept fixed. To handle different image
+recognition tasks, we provide two implementations of CoOp: unified context and
+class-specific context. Through extensive experiments on 11 datasets, we
+demonstrate that CoOp requires as few as one or two shots to beat hand-crafted
+prompts with a decent margin and is able to gain significant improvements over
+prompt engineering with more shots, e.g., with 16 shots the average gain is
+around 15% (with the highest reaching over 45%). Despite being a learning-based
+approach, CoOp achieves superb domain generalization performance compared with
+the zero-shot model using hand-crafted prompts.
+"
+"Prompt-Free Diffusion: Taking ""Text"" out of Text-to-Image Diffusion  Models",Xingqian Xu,http://arxiv.org/pdf/2305.16223v2.pdf,2023-05-25,['cs.cv'],2305.16223v2.pdf,"  Text-to-image (T2I) research has grown explosively in the past year, owing to
+the large-scale pre-trained diffusion models and many emerging personalization
+and editing approaches. Yet, one pain point persists: the text prompt
+engineering, and searching high-quality text prompts for customized results is
+more art than science. Moreover, as commonly argued: ""an image is worth a
+thousand words"" - the attempt to describe a desired image with texts often ends
+up being ambiguous and cannot comprehensively cover delicate visual details,
+hence necessitating more additional controls from the visual domain. In this
+paper, we take a bold step forward: taking ""Text"" out of a pre-trained T2I
+diffusion model, to reduce the burdensome prompt engineering efforts for users.
+Our proposed framework, Prompt-Free Diffusion, relies on only visual inputs to
+generate new images: it takes a reference image as ""context"", an optional image
+structural conditioning, and an initial noise, with absolutely no text prompt.
+The core architecture behind the scene is Semantic Context Encoder (SeeCoder),
+substituting the commonly used CLIP-based or LLM-based text encoder. The
+reusability of SeeCoder also makes it a convenient drop-in component: one can
+also pre-train a SeeCoder in one T2I model and reuse it for another. Through
+extensive experiments, Prompt-Free Diffusion is experimentally found to (i)
+outperform prior exemplar-based image synthesis approaches; (ii) perform on par
+with state-of-the-art T2I models using prompts following the best practice; and
+(iii) be naturally extensible to other downstream applications such as anime
+figure generation and virtual try-on, with promising quality. Our code and
+models are open-sourced at https://github.com/SHI-Labs/Prompt-Free-Diffusion.
+"
+Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with  Language Models,Robert L. Logan IV,http://arxiv.org/pdf/2106.13353v2.pdf,2021-06-24,"['cs.cl', 'cs.lg']",2106.13353v2.pdf,"  Prompting language models (LMs) with training examples and task descriptions
+has been seen as critical to recent successes in few-shot learning. In this
+work, we show that finetuning LMs in the few-shot setting can considerably
+reduce the need for prompt engineering. In fact, one can use null prompts,
+prompts that contain neither task-specific templates nor training examples, and
+achieve competitive accuracy to manually-tuned prompts across a wide range of
+tasks. While finetuning LMs does introduce new parameters for each downstream
+task, we show that this memory overhead can be substantially reduced:
+finetuning only the bias terms can achieve comparable or better accuracy than
+standard finetuning while only updating 0.1% of the parameters. All in all, we
+recommend finetuning LMs for few-shot learning as it is more accurate, robust
+to different prompts, and can be made nearly as efficient as using frozen LMs.
+"
+An Empirical Study on Few-shot Knowledge Probing for Pretrained Language  Models,Tianxing He,http://arxiv.org/pdf/2109.02772v2.pdf,2021-09-06,['cs.ai'],2109.02772v2.pdf,"  Prompt-based knowledge probing for 1-hop relations has been used to measure
+how much world knowledge is stored in pretrained language models. Existing work
+uses considerable amounts of data to tune the prompts for better performance.
+In this work, we compare a variety of approaches under a few-shot knowledge
+probing setting, where only a small number (e.g., 10 or 20) of example triples
+are available. In addition, we create a new dataset named TREx-2p, which
+contains 2-hop relations. We report that few-shot examples can strongly boost
+the probing performance for both 1-hop and 2-hop relations. In particular, we
+find that a simple-yet-effective approach of finetuning the bias vectors in the
+model outperforms existing prompt-engineering methods. Our dataset and code are
+available at \url{https://github.com/cloudygoose/fewshot_lama}.
+"
+Design Guidelines for Prompt Engineering Text-to-Image Generative Models,Vivian Liu,http://arxiv.org/pdf/2109.06977v3.pdf,2021-09-14,['cs.hc'],2109.06977v3.pdf,"  Text-to-image generative models are a new and powerful way to generate visual
+artwork. However, the open-ended nature of text as interaction is double-edged;
+while users can input anything and have access to an infinite range of
+generations, they also must engage in brute-force trial and error with the text
+prompt when the result quality is poor. We conduct a study exploring what
+prompt keywords and model hyperparameters can help produce coherent outputs. In
+particular, we study prompts structured to include subject and style keywords
+and investigate success and failure modes of these prompts. Our evaluation of
+5493 generations over the course of five experiments spans 51 abstract and
+concrete subjects as well as 51 abstract and figurative styles. From this
+evaluation, we present design guidelines that can help people produce better
+outcomes from text-to-image generative models.
+"
+Cut the CARP: Fishing for zero-shot story evaluation,Shahbuland Matiana,http://arxiv.org/pdf/2110.03111v3.pdf,2021-10-06,['cs.cl'],2110.03111v3.pdf,"  Recent advances in large-scale language models (Raffel et al., 2019; Brown et
+al., 2020) have brought significant qualitative and quantitative improvements
+in machine-driven text generation. Despite this, generation and evaluation of
+machine-generated narrative text remains a challenging problem. Objective
+evaluation of computationally-generated stories may be prohibitively expensive,
+require meticulously annotated datasets, or may not adequately measure the
+logical coherence of a generated story's narratological structure.
+  Informed by recent advances in contrastive learning (Radford et al., 2021),
+we present Contrastive Authoring and Reviewing Pairing (CARP): a scalable,
+efficient method for performing qualitatively superior, zero-shot evaluation of
+stories. We show a strong correlation between human evaluation of stories and
+those of CARP. Model outputs more significantly correlate with corresponding
+human input than those language-model based methods which utilize finetuning or
+prompt engineering approaches. We also present and analyze the Story-Critique
+Dataset, a new corpora composed of 1.3 million aligned story-critique pairs
+derived from over 80,000 stories. We expect this corpus to be of interest to
+NLP researchers.
+"
+Solving Probability and Statistics Problems by Program Synthesis,Leonard Tang,http://arxiv.org/pdf/2111.08267v1.pdf,2021-11-16,"['cs.lg', 'cs.ai', 'cs.cl', 'cs.pl']",2111.08267v1.pdf,"  We solve university level probability and statistics questions by program
+synthesis using OpenAI's Codex, a Transformer trained on text and fine-tuned on
+code. We transform course problems from MIT's 18.05 Introduction to Probability
+and Statistics and Harvard's STAT110 Probability into programming tasks. We
+then execute the generated code to get a solution. Since these course questions
+are grounded in probability, we often aim to have Codex generate probabilistic
+programs that simulate a large number of probabilistic dependencies to compute
+its solution. Our approach requires prompt engineering to transform the
+question from its original form to an explicit, tractable form that results in
+a correct program and solution. To estimate the amount of work needed to
+translate an original question into its tractable form, we measure the
+similarity between original and transformed questions. Our work is the first to
+introduce a new dataset of university-level probability and statistics problems
+and solve these problems in a scalable fashion using the program synthesis
+capabilities of large language models.
+"
+StyleMC: Multi-Channel Based Fast Text-Guided Image Generation and  Manipulation,Umut Kocasari,http://arxiv.org/pdf/2112.08493v1.pdf,2021-12-15,"['cs.cv', 'cs.lg']",2112.08493v1.pdf,"  Discovering meaningful directions in the latent space of GANs to manipulate
+semantic attributes typically requires large amounts of labeled data. Recent
+work aims to overcome this limitation by leveraging the power of Contrastive
+Language-Image Pre-training (CLIP), a joint text-image model. While promising,
+these methods require several hours of preprocessing or training to achieve the
+desired manipulations. In this paper, we present StyleMC, a fast and efficient
+method for text-driven image generation and manipulation. StyleMC uses a
+CLIP-based loss and an identity loss to manipulate images via a single text
+prompt without significantly affecting other attributes. Unlike prior work,
+StyleMC requires only a few seconds of training per text prompt to find stable
+global directions, does not require prompt engineering and can be used with any
+pre-trained StyleGAN2 model. We demonstrate the effectiveness of our method and
+compare it to state-of-the-art methods. Our code can be found at
+http://catlab-team.github.io/stylemc.
+"
+QaNER: Prompting Question Answering Models for Few-shot Named Entity  Recognition,Andy T. Liu,http://arxiv.org/pdf/2203.01543v2.pdf,2022-03-03,"['cs.cl', 'cs.ai', 'cs.lg']",2203.01543v2.pdf,"  Recently, prompt-based learning for pre-trained language models has succeeded
+in few-shot Named Entity Recognition (NER) by exploiting prompts as task
+guidance to increase label efficiency. However, previous prompt-based methods
+for few-shot NER have limitations such as a higher computational complexity,
+poor zero-shot ability, requiring manual prompt engineering, or lack of prompt
+robustness. In this work, we address these shortcomings by proposing a new
+prompt-based learning NER method with Question Answering (QA), called QaNER.
+Our approach includes 1) a refined strategy for converting NER problems into
+the QA formulation; 2) NER prompt generation for QA models; 3) prompt-based
+tuning with QA models on a few annotated NER examples; 4) zero-shot NER by
+prompting the QA model. Comparing the proposed approach with previous methods,
+QaNER is faster at inference, insensitive to the prompt quality, and robust to
+hyper-parameters, as well as demonstrating significantly better low-resource
+performance and zero-shot capability.
+"
+Executive Function: A Contrastive Value Policy for Resampling and  Relabeling Perceptions via Hindsight Summarization?,Chris Lengerich,http://arxiv.org/pdf/2204.12639v1.pdf,2022-04-27,['cs.cl'],2204.12639v1.pdf,"  We develop the few-shot continual learning task from first principles and
+hypothesize an evolutionary motivation and mechanism of action for executive
+function as a contrastive value policy which resamples and relabels perception
+data via hindsight summarization to minimize attended prediction error, similar
+to an online prompt engineering problem. This is made feasible by the use of a
+memory policy and a pretrained network with inductive biases for a grammar of
+learning and is trained to maximize evolutionary survival. We show how this
+model of executive function can be used to implement hypothesis testing as a
+stream of consciousness and may explain observations of human few-shot learning
+and neuroanatomy.
+"
+Polyglot Prompt: Multilingual Multitask PrompTraining,Jinlan Fu,http://arxiv.org/pdf/2204.14264v2.pdf,2022-04-29,['cs.cl'],2204.14264v2.pdf,"  This paper aims for a potential architectural improvement for multilingual
+learning and asks: Can different tasks from different languages be modeled in a
+monolithic framework, i.e. without any task/language-specific module? The
+benefit of achieving this could open new doors for future multilingual
+research, including allowing systems trained on low resources to be further
+assisted by other languages as well as other tasks. We approach this goal by
+developing a learning framework named Polyglot Prompting to exploit prompting
+methods for learning a unified semantic space for different languages and tasks
+with multilingual prompt engineering. We performed a comprehensive evaluation
+of 6 tasks, namely topic classification, sentiment classification, named entity
+recognition, question answering, natural language inference, and summarization,
+covering 24 datasets and 49 languages. The experimental results demonstrated
+the efficacy of multilingual multitask prompt-based learning and led to
+inspiring observations. We also present an interpretable multilingual
+evaluation methodology and show how the proposed framework, multilingual
+multitask prompt training, works. We release all datasets prompted in the best
+setting and code.
+"
+CLIP-CLOP: CLIP-Guided Collage and Photomontage,Piotr Mirowski,http://arxiv.org/pdf/2205.03146v3.pdf,2022-05-06,"['cs.cv', 'cs.ai']",2205.03146v3.pdf,"  The unabated mystique of large-scale neural networks, such as the CLIP dual
+image-and-text encoder, popularized automatically generated art. Increasingly
+more sophisticated generators enhanced the artworks' realism and visual
+appearance, and creative prompt engineering enabled stylistic expression.
+Guided by an artist-in-the-loop ideal, we design a gradient-based generator to
+produce collages. It requires the human artist to curate libraries of image
+patches and to describe (with prompts) the whole image composition, with the
+option to manually adjust the patches' positions during generation, thereby
+allowing humans to reclaim some control of the process and achieve greater
+creative freedom. We explore the aesthetic potentials of high-resolution
+collages, and provide an open-source Google Colab as an artistic tool.
+"
+Toxicity Detection with Generative Prompt-based Inference,Yau-Shian Wang,http://arxiv.org/pdf/2205.12390v1.pdf,2022-05-24,"['cs.cl', 'cs.ai']",2205.12390v1.pdf,"  Due to the subtleness, implicity, and different possible interpretations
+perceived by different people, detecting undesirable content from text is a
+nuanced difficulty. It is a long-known risk that language models (LMs), once
+trained on corpus containing undesirable content, have the power to manifest
+biases and toxicity. However, recent studies imply that, as a remedy, LMs are
+also capable of identifying toxic content without additional fine-tuning.
+Prompt-methods have been shown to effectively harvest this surprising
+self-diagnosing capability. However, existing prompt-based methods usually
+specify an instruction to a language model in a discriminative way. In this
+work, we explore the generative variant of zero-shot prompt-based toxicity
+detection with comprehensive trials on prompt engineering. We evaluate on three
+datasets with toxicity labels annotated on social media posts. Our analysis
+highlights the strengths of our generative classification approach both
+quantitatively and qualitatively. Interesting aspects of self-diagnosis and its
+ethical implications are discussed.
+"
+The Creativity of Text-to-Image Generation,Jonas Oppenlaender,http://arxiv.org/pdf/2206.02904v4.pdf,2022-05-13,"['cs.hc', 'cs.gr', 'h.5; h.m']",2206.02904v4.pdf,"  Text-guided synthesis of images has made a giant leap towards becoming a
+mainstream phenomenon. With text-to-image generation systems, anybody can
+create digital images and artworks. This provokes the question of whether
+text-to-image generation is creative. This paper expounds on the nature of
+human creativity involved in text-to-image art (so-called ""AI art"") with a
+specific focus on the practice of prompt engineering. The paper argues that the
+current product-centered view of creativity falls short in the context of
+text-to-image generation. A case exemplifying this shortcoming is provided and
+the importance of online communities for the creative ecosystem of
+text-to-image art is highlighted. The paper provides a high-level summary of
+this online ecosystem drawing on Rhodes' conceptual four P model of creativity.
+Challenges for evaluating the creativity of text-to-image generation and
+opportunities for research on text-to-image generation in the field of
+Human-Computer Interaction (HCI) are discussed.
+"
+Rationale-Augmented Ensembles in Language Models,Xuezhi Wang,http://arxiv.org/pdf/2207.00747v1.pdf,2022-07-02,['cs.cl'],2207.00747v1.pdf,"  Recent research has shown that rationales, or step-by-step chains of thought,
+can be used to improve performance in multi-step reasoning tasks. We reconsider
+rationale-augmented prompting for few-shot in-context learning, where (input ->
+output) prompts are expanded to (input, rationale -> output) prompts. For
+rationale-augmented prompting we demonstrate how existing approaches, which
+rely on manual prompt engineering, are subject to sub-optimal rationales that
+may harm performance. To mitigate this brittleness, we propose a unified
+framework of rationale-augmented ensembles, where we identify rationale
+sampling in the output space as the key component to robustly improve
+performance. This framework is general and can easily be extended to common
+natural language processing tasks, even those that do not traditionally
+leverage intermediate steps, such as question answering, word sense
+disambiguation, and sentiment analysis. We demonstrate that rationale-augmented
+ensembles achieve more accurate and interpretable results than existing
+prompting approaches--including standard prompting without rationales and
+rationale-based chain-of-thought prompting--while simultaneously improving
+interpretability of model predictions through the associated rationales.
+"
+Text-Guided Synthesis of Artistic Images with Retrieval-Augmented  Diffusion Models,Robin Rombach,http://arxiv.org/pdf/2207.13038v1.pdf,2022-07-26,['cs.cv'],2207.13038v1.pdf,"  Novel architectures have recently improved generative image synthesis leading
+to excellent visual quality in various tasks. Of particular note is the field
+of ``AI-Art'', which has seen unprecedented growth with the emergence of
+powerful multimodal models such as CLIP. By combining speech and image
+synthesis models, so-called ``prompt-engineering'' has become established, in
+which carefully selected and composed sentences are used to achieve a certain
+visual style in the synthesized image. In this note, we present an alternative
+approach based on retrieval-augmented diffusion models (RDMs). In RDMs, a set
+of nearest neighbors is retrieved from an external database during training for
+each training instance, and the diffusion model is conditioned on these
+informative samples. During inference (sampling), we replace the retrieval
+database with a more specialized database that contains, for example, only
+images of a particular visual style. This provides a novel way to prompt a
+general trained model after training and thereby specify a particular visual
+style. As shown by our experiments, this approach is superior to specifying the
+visual style within the text prompt. We open-source code and model weights at
+https://github.com/CompVis/latent-diffusion .
+"
+Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation  with Large Language Models,Hendrik Strobelt,http://arxiv.org/pdf/2208.07852v1.pdf,2022-08-16,"['cs.cl', 'cs.hc', 'cs.lg']",2208.07852v1.pdf,"  State-of-the-art neural language models can now be used to solve ad-hoc
+language tasks through zero-shot prompting without the need for supervised
+training. This approach has gained popularity in recent years, and researchers
+have demonstrated prompts that achieve strong accuracy on specific NLP tasks.
+However, finding a prompt for new tasks requires experimentation. Different
+prompt templates with different wording choices lead to significant accuracy
+differences. PromptIDE allows users to experiment with prompt variations,
+visualize prompt performance, and iteratively optimize prompts. We developed a
+workflow that allows users to first focus on model feedback using small data
+before moving on to a large data regime that allows empirical grounding of
+promising prompts using quantitative measures of the task. The tool then allows
+easy deployment of the newly created ad-hoc models. We demonstrate the utility
+of PromptIDE (demo at http://prompt.vizhub.ai) and our workflow using several
+real-world use cases.
+"
+Will It Blend? Mixing Training Paradigms & Prompting for Argument  Quality Prediction,Michiel van der Meer,http://arxiv.org/pdf/2209.08966v2.pdf,2022-09-19,"['cs.cl', 'cs.ai']",2209.08966v2.pdf,"  This paper describes our contributions to the Shared Task of the 9th Workshop
+on Argument Mining (2022). Our approach uses Large Language Models for the task
+of Argument Quality Prediction. We perform prompt engineering using GPT-3, and
+also investigate the training paradigms multi-task learning, contrastive
+learning, and intermediate-task training. We find that a mixed prediction setup
+outperforms single models. Prompting GPT-3 works best for predicting argument
+validity, and argument novelty is best estimated by a model trained using all
+three training paradigms.
+"
+Legal Prompting: Teaching a Language Model to Think Like a Lawyer,Fangyi Yu,http://arxiv.org/pdf/2212.01326v2.pdf,2022-12-02,"['cs.cl', 'cs.ai', 'i.2.7']",2212.01326v2.pdf,"  Large language models that are capable of zero or few-shot prompting
+approaches have given rise to the new research area of prompt engineering.
+Recent advances showed that for example Chain-of-Thought (CoT) prompts can
+improve arithmetic or common sense tasks significantly. We explore how such
+approaches fare with legal reasoning tasks and take the COLIEE entailment task
+based on the Japanese Bar exam for testing zero-shot/few-shot and fine-tuning
+approaches. Our findings show that while CoT prompting and fine-tuning with
+explanations approaches show improvements, the best results are produced by
+prompts that are derived from specific legal reasoning techniques such as IRAC
+(Issue, Rule, Application, Conclusion). Based on our experiments we improve the
+2021 best result from 0.7037 accuracy to 0.8148 accuracy and beat the 2022 best
+system of 0.6789 accuracy with an accuracy of 0.7431.
+"
+Controllable Image Captioning via Prompting,Ning Wang,http://arxiv.org/pdf/2212.01803v1.pdf,2022-12-04,['cs.cv'],2212.01803v1.pdf,"  Despite the remarkable progress of image captioning, existing captioners
+typically lack the controllable capability to generate desired image captions,
+e.g., describing the image in a rough or detailed manner, in a factual or
+emotional view, etc. In this paper, we show that a unified model is qualified
+to perform well in diverse domains and freely switch among multiple styles.
+Such a controllable capability is achieved by embedding the prompt learning
+into the image captioning framework. To be specific, we design a set of prompts
+to fine-tune the pre-trained image captioner. These prompts allow the model to
+absorb stylized data from different domains for joint training, without
+performance degradation in each domain. Furthermore, we optimize the prompts
+with learnable vectors in the continuous word embedding space, avoiding the
+heuristic prompt engineering and meanwhile exhibiting superior performance. In
+the inference stage, our model is able to generate desired stylized captions by
+choosing the corresponding prompts. Extensive experiments verify the
+controllable capability of the proposed method. Notably, we achieve outstanding
+performance on two diverse image captioning benchmarks including COCO Karpathy
+split and TextCaps using a unified model.
+"
+Fake it till you make it: Learning transferable representations from  synthetic ImageNet clones,Mert Bulent Sariyildiz,http://arxiv.org/pdf/2212.08420v2.pdf,2022-12-16,"['cs.cv', 'cs.lg']",2212.08420v2.pdf,"  Recent image generation models such as Stable Diffusion have exhibited an
+impressive ability to generate fairly realistic images starting from a simple
+text prompt. Could such models render real images obsolete for training image
+prediction models? In this paper, we answer part of this provocative question
+by investigating the need for real images when training models for ImageNet
+classification. Provided only with the class names that have been used to build
+the dataset, we explore the ability of Stable Diffusion to generate synthetic
+clones of ImageNet and measure how useful these are for training classification
+models from scratch. We show that with minimal and class-agnostic prompt
+engineering, ImageNet clones are able to close a large part of the gap between
+models produced by synthetic images and models trained with real images, for
+the several standard classification benchmarks that we consider in this study.
+More importantly, we show that models trained on synthetic images exhibit
+strong generalization properties and perform on par with models trained on real
+data for transfer. Project page: https://europe.naverlabs.com/imagenet-sd/
+"
+Explanation Regeneration via Information Bottleneck,Qintong Li,http://arxiv.org/pdf/2212.09603v2.pdf,2022-12-19,['cs.cl'],2212.09603v2.pdf,"  Explaining the black-box predictions of NLP models naturally and accurately
+is an important open problem in natural language generation. These free-text
+explanations are expected to contain sufficient and carefully-selected evidence
+to form supportive arguments for predictions. Due to the superior generative
+capacity of large pretrained language models, recent work built on prompt
+engineering enables explanation generation without specific training. However,
+explanation generated through single-pass prompting often lacks sufficiency and
+conciseness. To address this problem, we develop an information bottleneck
+method EIB to produce refined explanations that are sufficient and concise. Our
+approach regenerates the free-text explanation by polishing the single-pass
+output from the pretrained language model but retaining the information that
+supports the contents being explained. Experiments on two out-of-domain tasks
+verify the effectiveness of EIB through automatic evaluation and
+thoroughly-conducted human evaluation.
+"
+Optimizing Prompts for Text-to-Image Generation,Yaru Hao,http://arxiv.org/pdf/2212.09611v1.pdf,2022-12-19,"['cs.cl', 'cs.cv']",2212.09611v1.pdf,"  Well-designed prompts can guide text-to-image models to generate amazing
+images. However, the performant prompts are often model-specific and misaligned
+with user input. Instead of laborious human engineering, we propose prompt
+adaptation, a general framework that automatically adapts original user input
+to model-preferred prompts. Specifically, we first perform supervised
+fine-tuning with a pretrained language model on a small collection of manually
+engineered prompts. Then we use reinforcement learning to explore better
+prompts. We define a reward function that encourages the policy to generate
+more aesthetically pleasing images while preserving the original user
+intentions. Experimental results on Stable Diffusion show that our method
+outperforms manual prompt engineering in terms of both automatic metrics and
+human preference ratings. Moreover, reinforcement learning further boosts
+performance, especially on out-of-domain prompts. The pretrained checkpoints
+are available at https://aka.ms/promptist. The demo can be found at
+https://aka.ms/promptist-demo.
+"
+Using Large Language Models to Generate Engaging Captions for Data  Visualizations,Ashley Liew,http://arxiv.org/pdf/2212.14047v1.pdf,2022-12-27,"['cs.cl', 'cs.ai', 'cs.hc']",2212.14047v1.pdf,"  Creating compelling captions for data visualizations has been a longstanding
+challenge. Visualization researchers are typically untrained in journalistic
+reporting and hence the captions that are placed below data visualizations tend
+to be not overly engaging and rather just stick to basic observations about the
+data. In this work we explore the opportunities offered by the newly emerging
+crop of large language models (LLM) which use sophisticated deep learning
+technology to produce human-like prose. We ask, can these powerful software
+devices be purposed to produce engaging captions for generic data
+visualizations like a scatterplot. It turns out that the key challenge lies in
+designing the most effective prompt for the LLM, a task called prompt
+engineering. We report on first experiments using the popular LLM GPT-3 and
+deliver some promising results.
+"
+Fixing Hardware Security Bugs with Large Language Models,Baleegh Ahmad,http://arxiv.org/pdf/2302.01215v1.pdf,2023-02-02,['cs.cr'],2302.01215v1.pdf,"  Novel AI-based code-writing Large Language Models (LLMs) such as OpenAI's
+Codex have demonstrated capabilities in many coding-adjacent domains. In this
+work we consider how LLMs maybe leveraged to automatically repair security
+relevant bugs present in hardware designs. We focus on bug repair in code
+written in the Hardware Description Language Verilog. For this study we build a
+corpus of domain-representative hardware security bugs. We then design and
+implement a framework to quantitatively evaluate the performance of any LLM
+tasked with fixing the specified bugs. The framework supports design space
+exploration of prompts (i.e., prompt engineering) and identifying the best
+parameters for the LLM. We show that an ensemble of LLMs can repair all ten of
+our benchmarks. This ensemble outperforms the state-of-the-art Cirfix hardware
+bug repair tool on its own suite of bugs. These results show that LLMs can
+repair hardware security bugs and the framework is an important step towards
+the ultimate goal of an automated end-to-end bug repair framework.
+"
+UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluation,Daixuan Cheng,http://arxiv.org/pdf/2303.08518v3.pdf,2023-03-15,['cs.cl'],2303.08518v3.pdf,"  Large Language Models (LLMs) are popular for their impressive abilities, but
+the need for model-specific fine-tuning or task-specific prompt engineering can
+hinder their generalization. We propose UPRISE (Universal Prompt Retrieval for
+Improving zero-Shot Evaluation), which tunes a lightweight and versatile
+retriever that automatically retrieves prompts for a given zero-shot task
+input. Specifically, we demonstrate universality in a cross-task and
+cross-model scenario: the retriever is tuned on a diverse set of tasks, but
+tested on unseen task types; we use a small frozen LLM, GPT-Neo-2.7B, for
+tuning the retriever, but test the retriever on different LLMs of much larger
+scales, such as BLOOM-7.1B, OPT-66B and GPT3-175B. Additionally, we show that
+UPRISE mitigates the hallucination problem in our experiments with ChatGPT,
+suggesting its potential to improve even the strongest LLMs. Our model and code
+are available at https://github.com/microsoft/LMOps.
+"
+Patch-Token Aligned Bayesian Prompt Learning for Vision-Language Models,Xinyang Liu,http://arxiv.org/pdf/2303.09100v1.pdf,2023-03-16,"['cs.cv', 'cs.cl', 'cs.lg']",2303.09100v1.pdf,"  For downstream applications of vision-language pre-trained models, there has
+been significant interest in constructing effective prompts. Existing works on
+prompt engineering, which either require laborious manual designs or optimize
+the prompt tuning as a point estimation problem, may fail to describe diverse
+characteristics of categories and limit their applications. We introduce a
+Bayesian probabilistic resolution to prompt learning, where the label-specific
+stochastic prompts are generated hierarchically by first sampling a latent
+vector from an underlying distribution and then employing a lightweight
+generative model. Importantly, we semantically regularize prompt learning with
+the visual knowledge and view images and the corresponding prompts as patch and
+token sets under optimal transport, which pushes the prompt tokens to
+faithfully capture the label-specific visual concepts, instead of overfitting
+the training categories. Moreover, the proposed model can also be
+straightforwardly extended to the conditional case where the
+instance-conditional prompts are generated to improve the generalizability.
+Extensive experiments on 15 datasets show promising transferability and
+generalization performance of our proposed model.
+"
+Safety Analysis in the Era of Large Language Models: A Case Study of  STPA using ChatGPT,Yi Qi,http://arxiv.org/pdf/2304.01246v2.pdf,2023-04-03,"['cs.cl', 'cs.ai', 'cs.cy', 'cs.se']",2304.01246v2.pdf,"  Can safety analysis make use of Large Language Models (LLMs)? A case study
+explores Systems Theoretic Process Analysis (STPA) applied to Automatic
+Emergency Brake (AEB) and Electricity Demand Side Management (DSM) systems
+using ChatGPT. We investigate how collaboration schemes, input semantic
+complexity, and prompt guidelines influence STPA results. Comparative results
+show that using ChatGPT without human intervention may be inadequate due to
+reliability related issues, but with careful design, it may outperform human
+experts. No statistically significant differences are found when varying the
+input semantic complexity or using common prompt guidelines, which suggests the
+necessity for developing domain-specific prompt engineering. We also highlight
+future challenges, including concerns about LLM trustworthiness and the
+necessity for standardisation and regulation in this domain.
+"
+Geotechnical Parrot Tales (GPT): Harnessing Large Language Models in  geotechnical engineering,Krishna Kumar,http://arxiv.org/pdf/2304.02138v3.pdf,2023-04-04,"['cs.cl', 'physics.geo-ph', 'i.2.7; j.2.6']",2304.02138v3.pdf,"  The widespread adoption of large language models (LLMs), such as OpenAI's
+ChatGPT, could revolutionize various industries, including geotechnical
+engineering. However, GPT models can sometimes generate plausible-sounding but
+false outputs, leading to hallucinations. In this article, we discuss the
+importance of prompt engineering in mitigating these risks and harnessing the
+full potential of GPT for geotechnical applications. We explore the challenges
+and pitfalls associated with LLMs and highlight the role of context in ensuring
+accurate and valuable responses. Furthermore, we examine the development of
+context-specific search engines and the potential of LLMs to become a natural
+interface for complex tasks, such as data analysis and design. We also develop
+a unified interface using natural language to handle complex geotechnical
+engineering tasks and data analysis. By integrating GPT into geotechnical
+engineering workflows, professionals can streamline their work and develop
+sustainable and resilient infrastructure systems for the future.
+"
+Evaluation of ChatGPT Family of Models for Biomedical Reasoning and  Classification,Shan Chen,http://arxiv.org/pdf/2304.02496v1.pdf,2023-04-05,"['cs.cl', 'cs.ai']",2304.02496v1.pdf,"  Recent advances in large language models (LLMs) have shown impressive ability
+in biomedical question-answering, but have not been adequately investigated for
+more specific biomedical applications. This study investigates the performance
+of LLMs such as the ChatGPT family of models (GPT-3.5s, GPT-4) in biomedical
+tasks beyond question-answering. Because no patient data can be passed to the
+OpenAI API public interface, we evaluated model performance with over 10000
+samples as proxies for two fundamental tasks in the clinical domain -
+classification and reasoning. The first task is classifying whether statements
+of clinical and policy recommendations in scientific literature constitute
+health advice. The second task is causal relation detection from the biomedical
+literature. We compared LLMs with simpler models, such as bag-of-words (BoW)
+with logistic regression, and fine-tuned BioBERT models. Despite the excitement
+around viral ChatGPT, we found that fine-tuning for two fundamental NLP tasks
+remained the best strategy. The simple BoW model performed on par with the most
+complex LLM prompting. Prompt engineering required significant investment.
+"
+"VOICE: Visual Oracle for Interaction, Conversation, and Explanation",Donggang Jia,http://arxiv.org/pdf/2304.04083v1.pdf,2023-04-08,"['cs.hc', 'cs.gr']",2304.04083v1.pdf,"  We present VOICE, a novel approach for connecting large language models'
+(LLM) conversational capabilities with interactive exploratory visualization.
+VOICE introduces several innovative technical contributions that drive our
+conversational visualization framework. Our foundation is a pack-of-bots that
+can perform specific tasks, such as assigning tasks, extracting instructions,
+and generating coherent content. We employ fine-tuning and prompt engineering
+techniques to tailor bots' performance to their specific roles and accurately
+respond to user queries, and a new prompt-based iterative scene-tree generation
+establishes a coupling with a structural model. Our text-to-visualization
+method generates a flythrough sequence matching the content explanation.
+Finally, 3D natural language interaction provides capabilities to navigate and
+manipulate the 3D models in real-time. The VOICE framework can receive
+arbitrary voice commands from the user and responds verbally, tightly coupled
+with corresponding visual representation with low latency and high accuracy. We
+demonstrate the effectiveness and high generalizability potential of our
+approach by applying it to two distinct domains: analyzing three 3D molecular
+models with multi-scale and multi-instance attributes, and showcasing its
+effectiveness on a cartographic map visualization. A free copy of this paper
+and all supplemental materials are available at https://osf.io/g7fbr/.
+"
+Prompting the Hidden Talent of Web-Scale Speech Models for Zero-Shot  Task Generalization,Puyuan Peng,http://arxiv.org/pdf/2305.11095v3.pdf,2023-05-18,"['eess.as', 'cs.ai', 'cs.cl', 'cs.lg', 'cs.sd']",2305.11095v3.pdf,"  We investigate the emergent abilities of the recently proposed web-scale
+speech model Whisper, by adapting it to unseen tasks with prompt engineering.
+We selected three tasks: audio-visual speech recognition (AVSR), code-switched
+speech recognition (CS-ASR), and speech translation (ST) on unseen language
+pairs. We design task-specific prompts, by either leveraging another
+large-scale model, or simply manipulating the special tokens in the default
+prompts. Experiments show that compared to the default prompts, our proposed
+prompts improve performance by 10% to 45% on the three zero-shot tasks, and
+even outperform SotA supervised models on some datasets. In addition, our
+experiments reveal many interesting properties of Whisper, including its
+robustness to prompts, bias on accents, and the multilingual understanding in
+its latent space. Code is available at
+https://github.com/jasonppy/PromptingWhisper
+"
+Constructing Dreams using Generative AI,Safinah Ali,http://arxiv.org/pdf/2305.12013v1.pdf,2023-05-19,"['cs.hc', 'cs.ai', 'cs.cy']",2305.12013v1.pdf,"  Generative AI tools introduce new and accessible forms of media creation for
+youth. They also raise ethical concerns about the generation of fake media,
+data protection, privacy and ownership of AI-generated art. Since generative AI
+is already being used in products used by youth, it is critical that they
+understand how these tools work and how they can be used or misused. In this
+work, we facilitated students' generative AI learning through expression of
+their imagined future identities. We designed a learning workshop - Dreaming
+with AI - where students learned about the inner workings of generative AI
+tools, used text-to-image generation algorithms to create their imaged future
+dreams, reflected on the potential benefits and harms of generative AI tools
+and voiced their opinions about policies for the use of these tools in
+classrooms. In this paper, we present the learning activities and experiences
+of 34 high school students who engaged in our workshops. Students reached
+creative learning objectives by using prompt engineering to create their future
+dreams, gained technical knowledge by learning the abilities, limitations,
+text-visual mappings and applications of generative AI, and identified most
+potential societal benefits and harms of generative AI.
+"
+Interactive Data Synthesis for Systematic Vision Adaptation via  LLMs-AIGCs Collaboration,Qifan Yu,http://arxiv.org/pdf/2305.12799v1.pdf,2023-05-22,['cs.cv'],2305.12799v1.pdf,"  Recent text-to-image generation models have shown promising results in
+generating high-fidelity photo-realistic images. In parallel, the problem of
+data scarcity has brought a growing interest in employing AIGC technology for
+high-quality data expansion. However, this paradigm requires well-designed
+prompt engineering that cost-less data expansion and labeling remain
+under-explored. Inspired by LLM's powerful capability in task guidance, we
+propose a new paradigm of annotated data expansion named as ChatGenImage. The
+core idea behind it is to leverage the complementary strengths of diverse
+models to establish a highly effective and user-friendly pipeline for
+interactive data augmentation. In this work, we extensively study how LLMs
+communicate with AIGC model to achieve more controllable image generation and
+make the first attempt to collaborate them for automatic data augmentation for
+a variety of downstream tasks. Finally, we present fascinating results obtained
+from our ChatGenImage framework and demonstrate the powerful potential of our
+synthetic data for systematic vision adaptation. Our codes are available at
+https://github.com/Yuqifan1117/Labal-Anything-Pipeline.
+"
+Making Language Models Better Tool Learners with Execution Feedback,Shuofei Qiao,http://arxiv.org/pdf/2305.13068v1.pdf,2023-05-22,"['cs.cl', 'cs.ai', 'cs.hc', 'cs.ir', 'cs.lg']",2305.13068v1.pdf,"  Tools serve as pivotal interfaces that enable humans to understand and
+reshape the world. With the advent of foundational models, AI systems can
+utilize tools to expand their capabilities and interact with the world.
+Existing tool learning methodologies, encompassing supervised fine-tuning and
+prompt engineering approaches, often induce language models to utilize tools
+indiscriminately, as complex problems often exceed their own competencies.
+However, introducing tools for simple tasks, which the models themselves can
+readily resolve, can inadvertently propagate errors rather than enhance
+performance. This leads to the research question: can we teach language models
+when and how to use tools? To meet this need, we propose Tool leaRning wIth
+exeCution fEedback (TRICE), a two-stage end-to-end framework that enables the
+model to continually learn through feedback derived from tool execution,
+thereby learning when and how to use tools effectively. Experimental results,
+backed by further analysis, show that TRICE can make the language model to
+selectively use tools by decreasing the model's dependency on tools while
+enhancing the performance. Code and datasets will be available in
+https://github.com/zjunlp/trice.
+"
+Prompt position really matters in few-shot and zero-shot NLU tasks,Junyu Mao,http://arxiv.org/pdf/2305.14493v2.pdf,2023-05-23,['cs.cl'],2305.14493v2.pdf,"  Prompt-based models have made remarkable advancements in the fields of
+zero-shot and few-shot learning, attracting a lot of attention from
+researchers. Developing an effective prompt template plays a critical role.
+However, prior studies have mainly focused on prompt vocabulary selection or
+embedding initialization with the reserved prompt position fixed. In this
+empirical study, we conduct the most comprehensive analysis to date of prompt
+position option for natural language understanding tasks. Our findings quantify
+the substantial impact prompt position has on model performance. We observe
+that the prompt position used in prior studies is often sub-optimal for both
+zero-shot and few-shot settings. These findings suggest prompt position
+optimisation as an interesting research direction alongside the existing focus
+on prompt engineering.
+"
+ContrastNER: Contrastive-based Prompt Tuning for Few-shot NER,Amirhossein Layegh,http://arxiv.org/pdf/2305.17951v1.pdf,2023-05-29,"['cs.cl', 'cs.ai']",2305.17951v1.pdf,"  Prompt-based language models have produced encouraging results in numerous
+applications, including Named Entity Recognition (NER) tasks. NER aims to
+identify entities in a sentence and provide their types. However, the strong
+performance of most available NER approaches is heavily dependent on the design
+of discrete prompts and a verbalizer to map the model-predicted outputs to
+entity categories, which are complicated undertakings. To address these
+challenges, we present ContrastNER, a prompt-based NER framework that employs
+both discrete and continuous tokens in prompts and uses a contrastive learning
+approach to learn the continuous prompts and forecast entity types. The
+experimental results demonstrate that ContrastNER obtains competitive
+performance to the state-of-the-art NER methods in high-resource settings and
+outperforms the state-of-the-art models in low-resource circumstances without
+requiring extensive manual prompt engineering and verbalizer design.
+"
+Conformal Prediction with Large Language Models for Multi-Choice  Question Answering,Bhawesh Kumar,http://arxiv.org/pdf/2305.18404v3.pdf,2023-05-28,"['cs.cl', 'cs.lg', 'stat.ml']",2305.18404v3.pdf,"  As large language models continue to be widely developed, robust uncertainty
+quantification techniques will become crucial for their safe deployment in
+high-stakes scenarios. In this work, we explore how conformal prediction can be
+used to provide uncertainty quantification in language models for the specific
+task of multiple-choice question-answering. We find that the uncertainty
+estimates from conformal prediction are tightly correlated with prediction
+accuracy. This observation can be useful for downstream applications such as
+selective classification and filtering out low-quality predictions. We also
+investigate the exchangeability assumption required by conformal prediction to
+out-of-subject questions, which may be a more realistic scenario for many
+practical applications. Our work contributes towards more trustworthy and
+reliable usage of large language models in safety-critical situations, where
+robust guarantees of error rate are required.
+"
+Test-Time Training on Nearest Neighbors for Large Language Models,Moritz Hardt,http://arxiv.org/pdf/2305.18466v2.pdf,2023-05-29,"['cs.cl', 'cs.lg']",2305.18466v2.pdf,"  Many recent efforts aim to augment language models with relevant information
+retrieved from a database at test time. We avoid the need for prompt
+engineering by directly fine-tuning the model on data retrieved at test time
+using its standard training setup. For this purpose, we build a large-scale
+distributed nearest neighbor index based on text embeddings of the Pile
+dataset. Given a query to a language model, our system retrieves the neighbors
+of the query and fine-tunes the model on the text data corresponding to those
+neighbors. Surprisingly, retrieving and training on as few as 20 neighbors,
+each for only one gradient iteration, drastically improves performance across
+more than twenty language modeling tasks in the Pile benchmark. For example,
+test-time training significantly narrows the performance gap between a small
+GPT2 model and a GPTNeo model, more than ten times larger, that was
+specifically trained to convergence on the Pile. Sufficient index quality and
+size, however, are important. Our work establishes a valuable first baseline
+for implementing test-time training in the context of large language models,
+opening the door to numerous promising research avenues.
+"
+CONA: A novel CONtext-Aware instruction paradigm for communication using  large language model,Nan Zhou,http://arxiv.org/pdf/2305.18620v1.pdf,2023-05-26,"['cs.cl', 'cs.ai', 'cs.hc']",2305.18620v1.pdf,"  We introduce CONA, a novel context-aware instruction paradigm for effective
+knowledge dissemination using generative pre-trained transformer (GPT) models.
+CONA is a flexible framework designed to leverage the capabilities of Large
+Language Models (LLMs) and incorporate DIKW (Data, Information, Knowledge,
+Wisdom) hierarchy to automatically instruct and optimise presentation content,
+anticipate potential audience inquiries, and provide context-aware answers that
+adaptive to the knowledge level of the audience group. The unique aspect of the
+CONA paradigm lies in its combination of an independent advisory mechanism and
+a recursive feedback loop rooted on the DIKW hierarchy. This synergy
+significantly enhances context-aware contents, ensuring they are accessible and
+easily comprehended by the audience. This paradigm is an early pioneer to
+explore new methods for knowledge dissemination and communication in the LLM
+era, offering effective support for everyday knowledge sharing scenarios. We
+conduct experiments on a range of audience roles, along with materials from
+various disciplines using GPT4. Both quantitative and qualitative results
+demonstrated that the proposed CONA paradigm achieved remarkable performance
+compared to the outputs guided by conventional prompt engineering.
+"
+GPT4Tools: Teaching Large Language Model to Use Tools via  Self-instruction,Rui Yang,http://arxiv.org/pdf/2305.18752v1.pdf,2023-05-30,"['cs.cv', 'cs.cl']",2305.18752v1.pdf,"  This paper aims to efficiently enable Large Language Models (LLMs) to use
+multimodal tools. Advanced proprietary LLMs, such as ChatGPT and GPT-4, have
+shown great potential for tool usage through sophisticated prompt engineering.
+Nevertheless, these models typically rely on prohibitive computational costs
+and publicly inaccessible data. To address these challenges, we propose the
+GPT4Tools based on self-instruct to enable open-source LLMs, such as LLaMA and
+OPT, to use tools. It generates an instruction-following dataset by prompting
+an advanced teacher with various multi-modal contexts. By using the Low-Rank
+Adaptation (LoRA) optimization, our approach facilitates the open-source LLMs
+to solve a range of visual problems, including visual comprehension and image
+generation. Moreover, we provide a benchmark to evaluate the ability of LLMs to
+use tools, which is performed in both zero-shot and fine-tuning ways. Extensive
+experiments demonstrate the effectiveness of our method on various language
+models, which not only significantly improves the accuracy of invoking seen
+tools, but also enables the zero-shot capacity for unseen tools. The code and
+demo are available at https://github.com/StevenGrove/GPT4Tools.
+"
+Contextualizing Problems to Student Interests at Scale in Intelligent  Tutoring System Using Large Language Models,Gautam Yadav,http://arxiv.org/pdf/2306.00190v1.pdf,2023-05-31,['cs.hc'],2306.00190v1.pdf,"  Contextualizing problems to align with student interests can significantly
+improve learning outcomes. However, this task often presents scalability
+challenges due to resource and time constraints. Recent advancements in Large
+Language Models (LLMs) like GPT-4 offer potential solutions to these issues.
+This study explores the ability of GPT-4 in the contextualization of problems
+within CTAT, an intelligent tutoring system, aiming to increase student
+engagement and enhance learning outcomes. Through iterative prompt engineering,
+we achieved meaningful contextualization that preserved the difficulty and
+original intent of the problem, thereby not altering values or overcomplicating
+the questions. While our research highlights the potential of LLMs in
+educational settings, we acknowledge current limitations, particularly with
+geometry problems, and emphasize the need for ongoing evaluation and research.
+Future work includes systematic studies to measure the impact of this tool on
+students' learning outcomes and enhancements to handle a broader range of
+problems.
+"
+Exploring EFL students' prompt engineering in human-AI story writing: an  Activity Theory perspective,David James Woo,http://arxiv.org/pdf/2306.01798v1.pdf,2023-06-01,"['cs.cy', 'cs.ai']",2306.01798v1.pdf,"  This study applies Activity Theory to investigate how English as a foreign
+language (EFL) students prompt generative artificial intelligence (AI) tools
+during short story writing. Sixty-seven Hong Kong secondary school students
+created generative-AI tools using open-source language models and wrote short
+stories with them. The study collected and analyzed the students' generative-AI
+tools, short stories, and written reflections on their conditions or purposes
+for prompting. The research identified three main themes regarding the purposes
+for which students prompt generative-AI tools during short story writing: a
+lack of awareness of purposes, overcoming writer's block, and developing,
+expanding, and improving the story. The study also identified common
+characteristics of students' activity systems, including the sophistication of
+their generative-AI tools, the quality of their stories, and their school's
+overall academic achievement level, for their prompting of generative-AI tools
+for the three purposes during short story writing. The study's findings suggest
+that teachers should be aware of students' purposes for prompting generative-AI
+tools to provide tailored instructions and scaffolded guidance. The findings
+may also help designers provide differentiated instructions for users at
+various levels of story development when using a generative-AI tool.
+"
+Prompting Is All You Need: Automated Android Bug Replay with Large  Language Models,Sidong Feng,http://arxiv.org/pdf/2306.01987v2.pdf,2023-06-03,['cs.se'],2306.01987v2.pdf,"  Bug reports are vital for software maintenance that allow users to inform
+developers of the problems encountered while using the software. As such,
+researchers have committed considerable resources toward automating bug replay
+to expedite the process of software maintenance. Nonetheless, the success of
+current automated approaches is largely dictated by the characteristics and
+quality of bug reports, as they are constrained by the limitations of
+manually-crafted patterns and pre-defined vocabulary lists. Inspired by the
+success of Large Language Models (LLMs) in natural language understanding, we
+propose AdbGPT, a new lightweight approach to automatically reproduce the bugs
+from bug reports through prompt engineering, without any training and
+hard-coding effort. AdbGPT leverages few-shot learning and chain-of-thought
+reasoning to elicit human knowledge and logical reasoning from LLMs to
+accomplish the bug replay in a manner similar to a developer. Our evaluations
+demonstrate the effectiveness and efficiency of our AdbGPT to reproduce 81.3%
+of bug reports in 253.6 seconds, outperforming the state-of-the-art baselines
+and ablation studies. We also conduct a small-scale user study to confirm the
+usefulness of AdbGPT in enhancing developers' bug replay capabilities.
+"
+ChatGPT as a mapping assistant: A novel method to enrich maps with  generative AI and content derived from street-level photographs,Levente Juhász,http://arxiv.org/pdf/2306.03204v1.pdf,2023-06-05,"['cs.cy', 'cs.cv']",2306.03204v1.pdf,"  This paper explores the concept of leveraging generative AI as a mapping
+assistant for enhancing the efficiency of collaborative mapping. We present
+results of an experiment that combines multiple sources of volunteered
+geographic information (VGI) and large language models (LLMs). Three analysts
+described the content of crowdsourced Mapillary street-level photographs taken
+along roads in a small test area in Miami, Florida. GPT-3.5-turbo was
+instructed to suggest the most appropriate tagging for each road in
+OpenStreetMap (OSM). The study also explores the utilization of BLIP-2, a
+state-of-the-art multimodal pre-training method as an artificial analyst of
+street-level photographs in addition to human analysts. Results demonstrate two
+ways to effectively increase the accuracy of mapping suggestions without
+modifying the underlying AI models: by (1) providing a more detailed
+description of source photographs, and (2) combining prompt engineering with
+additional context (e.g. location and objects detected along a road). The first
+approach increases the suggestion accuracy by up to 29%, and the second one by
+up to 20%.
+"
+An Approach to Solving the Abstraction and Reasoning Corpus (ARC)  Challenge,Tan John Chong Min,http://arxiv.org/pdf/2306.03553v1.pdf,2023-06-06,['cs.ai'],2306.03553v1.pdf,"  We utilise the power of Large Language Models (LLMs), in particular GPT4, to
+be prompt engineered into performing an arbitrary task. Here, we give the model
+some human priors via text, along with some typical procedures for solving the
+ARC tasks, and ask it to generate the i) broad description of the input-output
+relation, ii) detailed steps of the input-output mapping, iii) use the detailed
+steps to perform manipulation on the test input and derive the test output. The
+current GPT3.5/GPT4 prompt solves 2 out of 4 tested small ARC challenges (those
+with small grids of 8x8 and below). With tweaks to the prompt to make it more
+specific for the use case, it can solve more. We posit that when scaled to a
+multi-agent system with usage of past memory and equipped with an image
+interpretation tool via Visual Question Answering, we may actually be able to
+solve the majority of the ARC challenge
+"
+Protect Your Prompts: Protocols for IP Protection in LLM Applications,M. A. van Wyk,http://arxiv.org/pdf/2306.06297v1.pdf,2023-06-09,"['cs.cl', 'cs.ai', '91d10, 68t10, 03d40', 'i.2.6; k.6.5; f.3.2']",2306.06297v1.pdf,"  With the rapid adoption of AI in the form of large language models (LLMs),
+the potential value of carefully engineered prompts has become significant.
+However, to realize this potential, prompts should be tradable on an open
+market. Since prompts are, at present, generally economically non-excludable,
+by virtue of their nature as text, no general competitive market has yet been
+established. This note discusses two protocols intended to provide protection
+of prompts, elevating their status as intellectual property, thus confirming
+the intellectual property rights of prompt engineers, and potentially
+supporting the flourishing of an open market for LLM prompts.
+"
+Scalable 3D Captioning with Pretrained Models,Tiange Luo,http://arxiv.org/pdf/2306.07279v2.pdf,2023-06-12,['cs.cv'],2306.07279v2.pdf,"  We introduce Cap3D, an automatic approach for generating descriptive text for
+3D objects. This approach utilizes pretrained models from image captioning,
+image-text alignment, and LLM to consolidate captions from multiple views of a
+3D asset, completely side-stepping the time-consuming and costly process of
+manual annotation. We apply Cap3D to the recently introduced large-scale 3D
+dataset, Objaverse, resulting in 660k 3D-text pairs. Our evaluation, conducted
+using 41k human annotations from the same dataset, demonstrates that Cap3D
+surpasses human-authored descriptions in terms of quality, cost, and speed.
+Through effective prompt engineering, Cap3D rivals human performance in
+generating geometric descriptions on 17k collected annotations from the ABO
+dataset. Finally, we finetune Text-to-3D models on Cap3D and human captions,
+and show Cap3D outperforms; and benchmark the SOTA including Point-E, Shape-E,
+and DreamFusion.
+"
+FALL-E: A Foley Sound Synthesis Model and Strategies,Minsung Kang,http://arxiv.org/pdf/2306.09807v2.pdf,2023-06-16,"['eess.as', 'cs.lg', 'cs.sd']",2306.09807v2.pdf,"  This paper introduces FALL-E, a foley synthesis system and its
+training/inference strategies. The FALL-E model employs a cascaded approach
+comprising low-resolution spectrogram generation, spectrogram super-resolution,
+and a vocoder. We trained every sound-related model from scratch using our
+extensive datasets, and utilized a pre-trained language model. We conditioned
+the model with dataset-specific texts, enabling it to learn sound quality and
+recording environment based on text input. Moreover, we leveraged external
+language models to improve text descriptions of our datasets and performed
+prompt engineering for quality, coherence, and diversity. FALL-E was evaluated
+by an objective measure as well as listening tests in the DCASE 2023 challenge
+Task 7. The submission achieved the second place on average, while achieving
+the best score for diversity, second place for audio quality, and third place
+for class fitness.
+"
+The Cultivated Practices of Text-to-Image Generation,Jonas Oppenlaender,http://arxiv.org/pdf/2306.11393v1.pdf,2023-06-20,"['cs.cy', 'cs.ai', 'k.4; j.5; i.2.0; k.5.m']",2306.11393v1.pdf,"  Humankind is entering a novel creative era in which anybody can synthesize
+digital information using generative artificial intelligence (AI).
+Text-to-image generation, in particular, has become vastly popular and millions
+of practitioners produce AI-generated images and AI art online. This chapter
+first gives an overview of the key developments that enabled a healthy
+co-creative online ecosystem around text-to-image generation to rapidly emerge,
+followed by a high-level description of key elements in this ecosystem. A
+particular focus is placed on prompt engineering, a creative practice that has
+been embraced by the AI art community. It is then argued that the emerging
+co-creative ecosystem constitutes an intelligent system on its own - a system
+that both supports human creativity, but also potentially entraps future
+generations and limits future development efforts in AI. The chapter discusses
+the potential risks and dangers of cultivating this co-creative ecosystem, such
+as the bias inherent in today's training data, potential quality degradation in
+future image generation systems due to synthetic data becoming common place,
+and the potential long-term effects of text-to-image generation on people's
+imagination, ambitions, and development.
+"
+Solving and Generating NPR Sunday Puzzles with Large Language Models,Jingmiao Zhao,http://arxiv.org/pdf/2306.12255v1.pdf,2023-06-21,['cs.cl'],2306.12255v1.pdf,"  We explore the ability of large language models to solve and generate puzzles
+from the NPR Sunday Puzzle game show using PUZZLEQA, a dataset comprising 15
+years of on-air puzzles. We evaluate four large language models using PUZZLEQA,
+in both multiple choice and free response formats, and explore two prompt
+engineering techniques to improve free response performance: chain-of-thought
+reasoning and prompt summarization. We find that state-of-the-art large
+language models can solve many PUZZLEQA puzzles: the best model, GPT-3.5,
+achieves 50.2% loose accuracy. However, in our few-shot puzzle generation
+experiment, we find no evidence that models can generate puzzles: GPT-3.5
+generates puzzles with answers that do not conform to the generated rules.
+Puzzle generation remains a challenging task for future work.
+"
+Federated Large Language Model: A Position Paper,Chaochao Chen,http://arxiv.org/pdf/2307.08925v1.pdf,2023-07-18,"['cs.lg', 'cs.ai', 'cs.cl']",2307.08925v1.pdf,"  Large scale language models (LLM) have received significant attention and
+found diverse applications across various domains, but their development
+encounters challenges in real-world scenarios. These challenges arise due to
+the scarcity of public domain data availability and the need to maintain
+privacy with respect to private domain data. To address these issues, federated
+learning (FL) has emerged as a promising technology that enables collaborative
+training of shared models while preserving decentralized data. We propose the
+concept of federated LLM, which comprises three key components, i.e., federated
+LLM pre-training, federated LLM fine-tuning, and federated LLM prompt
+engineering. For each component, we discuss its advantage over traditional LLM
+training methods and propose specific engineering strategies for
+implementation. Furthermore, we explore the novel challenges introduced by the
+integration of FL and LLM. We analyze existing solutions and identify potential
+obstacles faced by these solutions within the context of federated LLM.
+"
+Chit-Chat or Deep Talk: Prompt Engineering for Process Mining,Urszula Jessen,http://arxiv.org/pdf/2307.09909v1.pdf,2023-07-19,['cs.ai'],2307.09909v1.pdf,"  This research investigates the application of Large Language Models (LLMs) to
+augment conversational agents in process mining, aiming to tackle its inherent
+complexity and diverse skill requirements. While LLM advancements present novel
+opportunities for conversational process mining, generating efficient outputs
+is still a hurdle. We propose an innovative approach that amend many issues in
+existing solutions, informed by prior research on Natural Language Processing
+(NLP) for conversational agents. Leveraging LLMs, our framework improves both
+accessibility and agent performance, as demonstrated by experiments on public
+question and data sets. Our research sets the stage for future explorations
+into LLMs' role in process mining and concludes with propositions for enhancing
+LLM memory, implementing real-time user testing, and examining diverse data
+sets.
+"
+Large Language Models can accomplish Business Process Management Tasks,Michael Grohs,http://arxiv.org/pdf/2307.09923v1.pdf,2023-07-19,['cs.cl'],2307.09923v1.pdf,"  Business Process Management (BPM) aims to improve organizational activities
+and their outcomes by managing the underlying processes. To achieve this, it is
+often necessary to consider information from various sources, including
+unstructured textual documents. Therefore, researchers have developed several
+BPM-specific solutions that extract information from textual documents using
+Natural Language Processing techniques. These solutions are specific to their
+respective tasks and cannot accomplish multiple process-related problems as a
+general-purpose instrument. However, in light of the recent emergence of Large
+Language Models (LLMs) with remarkable reasoning capabilities, such a
+general-purpose instrument with multiple applications now appears attainable.
+In this paper, we illustrate how LLMs can accomplish text-related BPM tasks by
+applying a specific LLM to three exemplary tasks: mining imperative process
+models from textual descriptions, mining declarative process models from
+textual descriptions, and assessing the suitability of process tasks from
+textual descriptions for robotic process automation. We show that, without
+extensive configuration or prompt engineering, LLMs perform comparably to or
+better than existing solutions and discuss implications for future BPM research
+as well as practical usage.
+"
+SentimentGPT: Exploiting GPT for Advanced Sentiment Analysis and its  Departure from Current Machine Learning,Kiana Kheiri,http://arxiv.org/pdf/2307.10234v2.pdf,2023-07-16,"['cs.cl', 'cs.ai', 'cs.lg', 'cs.si']",2307.10234v2.pdf,"  This study presents a thorough examination of various Generative Pretrained
+Transformer (GPT) methodologies in sentiment analysis, specifically in the
+context of Task 4 on the SemEval 2017 dataset. Three primary strategies are
+employed: 1) prompt engineering using the advanced GPT-3.5 Turbo, 2)
+fine-tuning GPT models, and 3) an inventive approach to embedding
+classification. The research yields detailed comparative insights among these
+strategies and individual GPT models, revealing their unique strengths and
+potential limitations. Additionally, the study compares these GPT-based
+methodologies with other current, high-performing models previously used with
+the same dataset. The results illustrate the significant superiority of the GPT
+approaches in terms of predictive performance, more than 22\% in F1-score
+compared to the state-of-the-art. Further, the paper sheds light on common
+challenges in sentiment analysis tasks, such as understanding context and
+detecting sarcasm. It underscores the enhanced capabilities of the GPT models
+to effectively handle these complexities. Taken together, these findings
+highlight the promising potential of GPT models in sentiment analysis, setting
+the stage for future research in this field. The code can be found at
+https://github.com/DSAatUSU/SentimentGPT
+"
+Domain Knowledge Distillation from Large Language Model: An Empirical  Study in the Autonomous Driving Domain,Yun Tang,http://arxiv.org/pdf/2307.11769v1.pdf,2023-07-17,['cs.cl'],2307.11769v1.pdf,"  Engineering knowledge-based (or expert) systems require extensive manual
+effort and domain knowledge. As Large Language Models (LLMs) are trained using
+an enormous amount of cross-domain knowledge, it becomes possible to automate
+such engineering processes. This paper presents an empirical automation and
+semi-automation framework for domain knowledge distillation using prompt
+engineering and the LLM ChatGPT. We assess the framework empirically in the
+autonomous driving domain and present our key observations. In our
+implementation, we construct the domain knowledge ontology by ""chatting"" with
+ChatGPT. The key finding is that while fully automated domain ontology
+construction is possible, human supervision and early intervention typically
+improve efficiency and output quality as they lessen the effects of response
+randomness and the butterfly effect. We, therefore, also develop a web-based
+distillation assistant enabling supervision and flexible intervention at
+runtime. We hope our findings and tools could inspire future research toward
+revolutionizing the engineering of knowledge-based systems across application
+domains.
+"
+Copilot for Xcode: Exploring AI-Assisted Programming by Prompting  Cloud-based Large Language Models,Chee Wei Tan,http://arxiv.org/pdf/2307.14349v1.pdf,2023-07-08,"['cs.se', 'cs.ai']",2307.14349v1.pdf,"  This paper presents an AI-assisted programming tool called Copilot for Xcode
+for program composition and design to support human software developers. By
+seamlessly integrating cloud-based Large Language Models (LLM) with Apple's
+local development environment, Xcode, this tool enhances productivity and
+unleashes creativity for software development in Apple software ecosystem
+(e.g., iOS apps, macOS). Leveraging advanced natural language processing (NLP)
+techniques, Copilot for Xcode effectively processes source code tokens and
+patterns within code repositories, enabling features such as code generation,
+autocompletion, documentation, and error detection. Software developers can
+also query and make ""small"" decisions for program composition, some of which
+can be made simultaneously, and this is facilitated through prompt engineering
+in a chat interface of Copilot for Xcode. Finally, we present simple case
+studies as evidence of the effectiveness of utilizing NLP in Xcode to prompt
+popular LLM services like OpenAI ChatGPT for program composition and design.
+"
+Backdoor Attacks for In-Context Learning with Language Models,Nikhil Kandpal,http://arxiv.org/pdf/2307.14692v1.pdf,2023-07-27,['cs.cr'],2307.14692v1.pdf,"  Because state-of-the-art language models are expensive to train, most
+practitioners must make use of one of the few publicly available language
+models or language model APIs. This consolidation of trust increases the
+potency of backdoor attacks, where an adversary tampers with a machine learning
+model in order to make it perform some malicious behavior on inputs that
+contain a predefined backdoor trigger. We show that the in-context learning
+ability of large language models significantly complicates the question of
+developing backdoor attacks, as a successful backdoor must work against various
+prompting strategies and should not affect the model's general purpose
+capabilities. We design a new attack for eliciting targeted misclassification
+when language models are prompted to perform a particular target task and
+demonstrate the feasibility of this attack by backdooring multiple large
+language models ranging in size from 1.3 billion to 6 billion parameters.
+Finally we study defenses to mitigate the potential harms of our attack: for
+example, while in the white-box setting we show that fine-tuning models for as
+few as 500 steps suffices to remove the backdoor behavior, in the black-box
+setting we are unable to develop a successful defense that relies on prompt
+engineering alone.
+"
+Do LLMs Possess a Personality? Making the MBTI Test an Amazing  Evaluation for Large Language Models,Keyu Pan,http://arxiv.org/pdf/2307.16180v1.pdf,2023-07-30,['cs.cl'],2307.16180v1.pdf,"  The field of large language models (LLMs) has made significant progress, and
+their knowledge storage capacity is approaching that of human beings.
+Furthermore, advanced techniques, such as prompt learning and reinforcement
+learning, are being employed to address ethical concerns and hallucination
+problems associated with LLMs, bringing them closer to aligning with human
+values. This situation naturally raises the question of whether LLMs with
+human-like abilities possess a human-like personality? In this paper, we aim to
+investigate the feasibility of using the Myers-Briggs Type Indicator (MBTI), a
+widespread human personality assessment tool, as an evaluation metric for LLMs.
+Specifically, extensive experiments will be conducted to explore: 1) the
+personality types of different LLMs, 2) the possibility of changing the
+personality types by prompt engineering, and 3) How does the training dataset
+affect the model's personality. Although the MBTI is not a rigorous assessment,
+it can still reflect the similarity between LLMs and human personality. In
+practice, the MBTI has the potential to serve as a rough indicator. Our codes
+are available at
+https://github.com/HarderThenHarder/transformers_tasks/tree/main/LLM/llms_mbti.
+"
+Alpha-GPT: Human-AI Interactive Alpha Mining for Quantitative Investment,Saizhuo Wang,http://arxiv.org/pdf/2308.00016v1.pdf,2023-07-31,"['q-fin.cp', 'cs.ai', 'cs.cl']",2308.00016v1.pdf,"  One of the most important tasks in quantitative investment research is mining
+new alphas (effective trading signals or factors). Traditional alpha mining
+methods, either hand-crafted factor synthesizing or algorithmic factor mining
+(e.g., search with genetic programming), have inherent limitations, especially
+in implementing the ideas of quants. In this work, we propose a new alpha
+mining paradigm by introducing human-AI interaction, and a novel prompt
+engineering algorithmic framework to implement this paradigm by leveraging the
+power of large language models. Moreover, we develop Alpha-GPT, a new
+interactive alpha mining system framework that provides a heuristic way to
+``understand'' the ideas of quant researchers and outputs creative, insightful,
+and effective alphas. We demonstrate the effectiveness and advantage of
+Alpha-GPT via a number of alpha mining experiments.
+"
+Optimizing Machine Translation through Prompt Engineering: An  Investigation into ChatGPT's Customizability,Masaru Yamada,http://arxiv.org/pdf/2308.01391v1.pdf,2023-08-02,['cs.cl'],2308.01391v1.pdf,"  This paper explores the influence of integrating the purpose of the
+translation and the target audience into prompts on the quality of translations
+produced by ChatGPT. Drawing on previous translation studies, industry
+practices, and ISO standards, the research underscores the significance of the
+pre-production phase in the translation process. The study reveals that the
+inclusion of suitable prompts in large-scale language models like ChatGPT can
+yield flexible translations, a feat yet to be realized by conventional Machine
+Translation (MT). The research scrutinizes the changes in translation quality
+when prompts are used to generate translations that meet specific conditions.
+The evaluation is conducted from a practicing translator's viewpoint, both
+subjectively and qualitatively, supplemented by the use of OpenAI's word
+embedding API for cosine similarity calculations. The findings suggest that the
+integration of the purpose and target audience into prompts can indeed modify
+the generated translations, generally enhancing the translation quality by
+industry standards. The study also demonstrates the practical application of
+the ""good translation"" concept, particularly in the context of marketing
+documents and culturally dependent idioms.
+"
+InterAct: Exploring the Potentials of ChatGPT as a Cooperative Agent,Po-Lin Chen,http://arxiv.org/pdf/2308.01552v1.pdf,2023-08-03,"['cs.ai', 'cs.cl', 'cs.lg']",2308.01552v1.pdf,"  This research paper delves into the integration of OpenAI's ChatGPT into
+embodied agent systems, evaluating its influence on interactive decision-making
+benchmark. Drawing a parallel to the concept of people assuming roles according
+to their unique strengths, we introduce InterAct. In this approach, we feed
+ChatGPT with varied prompts, assigning it a numerous roles like a checker and a
+sorter, then integrating them with the original language model. Our research
+shows a remarkable success rate of 98% in AlfWorld, which consists of 6
+different tasks in a simulated household environment, emphasizing the
+significance of proficient prompt engineering. The results highlight ChatGPT's
+competence in comprehending and performing intricate tasks effectively in
+real-world settings, thus paving the way for further advancements in task
+planning.
+"
+RTLLM: An Open-Source Benchmark for Design RTL Generation with Large  Language Model,Yao Lu,http://arxiv.org/pdf/2308.05345v2.pdf,2023-08-10,"['cs.lg', 'cs.ar']",2308.05345v2.pdf,"  Inspired by the recent success of large language models (LLMs) like ChatGPT,
+researchers start to explore the adoption of LLMs for agile hardware design,
+such as generating design RTL based on natural-language instructions. However,
+in existing works, their target designs are all relatively simple and in a
+small scale, and proposed by the authors themselves, making a fair comparison
+among different LLM solutions challenging. In addition, many prior works only
+focus on the design correctness, without evaluating the design qualities of
+generated design RTL. In this work, we propose an open-source benchmark named
+RTLLM, for generating design RTL with natural language instructions. To
+systematically evaluate the auto-generated design RTL, we summarized three
+progressive goals, named syntax goal, functionality goal, and design quality
+goal. This benchmark can automatically provide a quantitative evaluation of any
+given LLM-based solution. Furthermore, we propose an easy-to-use yet
+surprisingly effective prompt engineering technique named self-planning, which
+proves to significantly boost the performance of GPT-3.5 in our proposed
+benchmark.
+"
+"LLM Self Defense: By Self Examination, LLMs Know They Are Being Tricked",Mansi Phute,http://arxiv.org/pdf/2308.07308v3.pdf,2023-08-14,"['cs.cl', 'cs.ai']",2308.07308v3.pdf,"  Large language models (LLMs) are popular for high-quality text generation but
+can produce harmful content, even when aligned with human values through
+reinforcement learning. Adversarial prompts can bypass their safety measures.
+We propose LLM Self Defense, a simple approach to defend against these attacks
+by having an LLM screen the induced responses. Our method does not require any
+fine-tuning, input preprocessing, or iterative output generation. Instead, we
+incorporate the generated content into a pre-defined prompt and employ another
+instance of an LLM to analyze the text and predict whether it is harmful. We
+test LLM Self Defense on GPT 3.5 and Llama 2, two of the current most prominent
+LLMs against various types of attacks, such as forcefully inducing affirmative
+responses to prompts and prompt engineering attacks. Notably, LLM Self Defense
+succeeds in reducing the attack success rate to virtually 0 using both GPT 3.5
+and Llama 2.
+"
+Data Race Detection Using Large Language Models,Le Chen,http://arxiv.org/pdf/2308.07505v2.pdf,2023-08-15,"['cs.lg', 'cs.cl']",2308.07505v2.pdf,"  Large language models (LLMs) are demonstrating significant promise as an
+alternate strategy to facilitate analyses and optimizations of high-performance
+computing programs, circumventing the need for resource-intensive manual tool
+creation. In this paper, we explore a novel LLM-based data race detection
+approach combining prompting engineering and fine-tuning techniques. We create
+a dedicated dataset named DRB-ML, which is derived from DataRaceBench, with
+fine-grain labels showing the presence of data race pairs and their associated
+variables, line numbers, and read/write information. DRB-ML is then used to
+evaluate representative LLMs and fine-tune open-source ones. Our experiment
+shows that LLMs can be a viable approach to data race detection. However, they
+still cannot compete with traditional data race detection tools when we need
+detailed information about variable pairs causing data races.
+"
+Accelerated materials language processing enabled by GPT,Jaewoong Choi,http://arxiv.org/pdf/2308.09354v1.pdf,2023-08-18,"['cs.cl', 'cond-mat.mtrl-sci']",2308.09354v1.pdf,"  Materials language processing (MLP) is one of the key facilitators of
+materials science research, as it enables the extraction of structured
+information from massive materials science literature. Prior works suggested
+high-performance MLP models for text classification, named entity recognition
+(NER), and extractive question answering (QA), which require complex model
+architecture, exhaustive fine-tuning and a large number of human-labelled
+datasets. In this study, we develop generative pretrained transformer
+(GPT)-enabled pipelines where the complex architectures of prior MLP models are
+replaced with strategic designs of prompt engineering. First, we develop a
+GPT-enabled document classification method for screening relevant documents,
+achieving comparable accuracy and reliability compared to prior models, with
+only small dataset. Secondly, for NER task, we design an entity-centric
+prompts, and learning few-shot of them improved the performance on most of
+entities in three open datasets. Finally, we develop an GPT-enabled extractive
+QA model, which provides improved performance and shows the possibility of
+automatically correcting annotations. While our findings confirm the potential
+of GPT-enabled MLP models as well as their value in terms of reliability and
+practicability, our scientific methods and systematic approach are applicable
+to any materials science domain to accelerate the information extraction of
+scientific literature.
+"
+Data-to-text Generation for Severely Under-Resourced Languages with  GPT-3.5: A Bit of Help Needed from Google Translate,Michela Lorandi,http://arxiv.org/pdf/2308.09957v1.pdf,2023-08-19,"['cs.cl', 'cs.ai']",2308.09957v1.pdf,"  LLMs like GPT are great at tasks involving English which dominates in their
+training data. In this paper, we look at how they cope with tasks involving
+languages that are severely under-represented in their training data, in the
+context of data-to-text generation for Irish, Maltese, Welsh and Breton. During
+the prompt-engineering phase we tested a range of prompt types and formats on
+GPT-3.5 and~4 with a small sample of example input/output pairs. We then fully
+evaluated the two most promising prompts in two scenarios: (i) direct
+generation into the under-resourced language, and (ii) generation into English
+followed by translation into the under-resourced language. We find that
+few-shot prompting works better for direct generation into under-resourced
+languages, but that the difference disappears when pivoting via English. The
+few-shot + translation system variants were submitted to the WebNLG 2023 shared
+task where they outperformed competitor systems by substantial margins in all
+languages on all metrics. We conclude that good performance on under-resourced
+languages can be achieved out-of-the box with state-of-the-art LLMs. However,
+our best results (for Welsh) remain well below the lowest ranked English system
+at WebNLG'20.
+"
+Activation Addition: Steering Language Models Without Optimization,Alexander Matt Turner,http://arxiv.org/pdf/2308.10248v2.pdf,2023-08-20,"['cs.cl', 'cs.lg']",2308.10248v2.pdf,"  Reliably controlling the behavior of large language models is a pressing open
+problem. Existing methods include supervised finetuning, reinforcement learning
+from human feedback, prompt engineering, and guided decoding. We instead
+investigate activation engineering: modifying activations at inference time to
+predictably alter model behavior. In particular, we bias the forward pass with
+an added 'steering vector' implicitly specified through natural language.
+  Unlike past work which learned these steering vectors, our Activation
+Addition (ActAdd) method computes them by taking the activation differences
+that result from pairs of prompts. We demonstrate ActAdd on GPT-2 on
+OpenWebText and ConceptNet. Our inference-time approach yields control over
+high-level properties of output and preserves off-target model performance. It
+involves far less compute and implementation effort than finetuning, allows
+users to provide natural language specifications, and its overhead scales
+naturally with model size.
+"
+Situated Natural Language Explanations,Zining Zhu,http://arxiv.org/pdf/2308.14115v1.pdf,2023-08-27,['cs.cl'],2308.14115v1.pdf,"  Natural language is among the most accessible tools for explaining decisions
+to humans, and large pretrained language models (PLMs) have demonstrated
+impressive abilities to generate coherent natural language explanations (NLE).
+The existing NLE research perspectives do not take the audience into account.
+An NLE can have high textual quality, but it might not accommodate audiences'
+needs and preference. To address this limitation, we propose an alternative
+perspective, situated NLE, including a situated generation framework and a
+situated evaluation framework. On the generation side, we propose simple prompt
+engineering methods that adapt the NLEs to situations. In human studies, the
+annotators preferred the situated NLEs. On the evaluation side, we set up
+automated evaluation scores in lexical, semantic, and pragmatic categories. The
+scores can be used to select the most suitable prompts to generate NLEs.
+Situated NLE provides a perspective to conduct further research on automatic
+NLE generations.
+"
+"FurChat: An Embodied Conversational Agent using LLMs, Combining Open and  Closed-Domain Dialogue with Facial Expressions",Neeraj Cherakara,http://arxiv.org/pdf/2308.15214v2.pdf,2023-08-29,"['cs.cl', 'cs.ai', 'cs.hc', 'cs.ro']",2308.15214v2.pdf,"  We demonstrate an embodied conversational agent that can function as a
+receptionist and generate a mixture of open and closed-domain dialogue along
+with facial expressions, by using a large language model (LLM) to develop an
+engaging conversation. We deployed the system onto a Furhat robot, which is
+highly expressive and capable of using both verbal and nonverbal cues during
+interaction. The system was designed specifically for the National Robotarium
+to interact with visitors through natural conversations, providing them with
+information about the facilities, research, news, upcoming events, etc. The
+system utilises the state-of-the-art GPT-3.5 model to generate such information
+along with domain-general conversations and facial expressions based on prompt
+engineering.
+"
+Can Prompt Learning Benefit Radiology Report Generation?,Jun Wang,http://arxiv.org/pdf/2308.16269v1.pdf,2023-08-30,['cs.cv'],2308.16269v1.pdf,"  Radiology report generation aims to automatically provide clinically
+meaningful descriptions of radiology images such as MRI and X-ray. Although
+great success has been achieved in natural scene image captioning tasks,
+radiology report generation remains challenging and requires prior medical
+knowledge. In this paper, we propose PromptRRG, a method that utilizes prompt
+learning to activate a pretrained model and incorporate prior knowledge. Since
+prompt learning for radiology report generation has not been explored before,
+we begin with investigating prompt designs and categorise them based on varying
+levels of knowledge: common, domain-specific and disease-enriched prompts.
+Additionally, we propose an automatic prompt learning mechanism to alleviate
+the burden of manual prompt engineering. This is the first work to
+systematically examine the effectiveness of prompt learning for radiology
+report generation. Experimental results on the largest radiology report
+generation benchmark, MIMIC-CXR, demonstrate that our proposed method achieves
+state-of-the-art performance. Code will be available upon the acceptance.
+"
+Large Language Models as Data Preprocessors,Haochen Zhang,http://arxiv.org/pdf/2308.16361v1.pdf,2023-08-30,"['cs.ai', 'cs.db']",2308.16361v1.pdf,"  Large Language Models (LLMs), typified by OpenAI's GPT series and Meta's
+LLaMA variants, have marked a significant advancement in artificial
+intelligence. Trained on vast amounts of text data, LLMs are capable of
+understanding and generating human-like text across a diverse range of topics.
+This study expands on the applications of LLMs, exploring their potential in
+data preprocessing, a critical stage in data mining and analytics applications.
+We delve into the applicability of state-of-the-art LLMs such as GPT-3.5,
+GPT-4, and Vicuna-13B for error detection, data imputation, schema matching,
+and entity matching tasks. Alongside showcasing the inherent capabilities of
+LLMs, we highlight their limitations, particularly in terms of computational
+expense and inefficiency. We propose an LLM-based framework for data
+preprocessing, which integrates cutting-edge prompt engineering techniques,
+coupled with traditional methods like contextualization and feature selection,
+to improve the performance and efficiency of these models. The effectiveness of
+LLMs in data preprocessing is evaluated through an experimental study spanning
+12 datasets. GPT-4 emerged as a standout, achieving 100\% accuracy or F1 score
+on 4 datasets, suggesting LLMs' immense potential in these tasks. Despite
+certain limitations, our study underscores the promise of LLMs in this domain
+and anticipates future developments to overcome current hurdles.
+"
+Developing a Scalable Benchmark for Assessing Large Language Models in  Knowledge Graph Engineering,Lars-Peter Meyer,http://arxiv.org/pdf/2308.16622v1.pdf,2023-08-31,"['cs.ai', 'cs.cl', 'cs.db']",2308.16622v1.pdf,"  As the field of Large Language Models (LLMs) evolves at an accelerated pace,
+the critical need to assess and monitor their performance emerges. We introduce
+a benchmarking framework focused on knowledge graph engineering (KGE)
+accompanied by three challenges addressing syntax and error correction, facts
+extraction and dataset generation. We show that while being a useful tool, LLMs
+are yet unfit to assist in knowledge graph generation with zero-shot prompting.
+Consequently, our LLM-KG-Bench framework provides automatic evaluation and
+storage of LLM responses as well as statistical data and visualization tools to
+support tracking of prompt engineering and model performance.
+"
+Linking microblogging sentiments to stock price movement: An application  of GPT-4,Rick Steinert,http://arxiv.org/pdf/2308.16771v1.pdf,2023-08-31,"['q-fin.st', 'q-fin.cp']",2308.16771v1.pdf,"  This paper investigates the potential improvement of the GPT-4 Language
+Learning Model (LLM) in comparison to BERT for modeling same-day daily stock
+price movements of Apple and Tesla in 2017, based on sentiment analysis of
+microblogging messages. We recorded daily adjusted closing prices and
+translated them into up-down movements. Sentiment for each day was extracted
+from messages on the Stocktwits platform using both LLMs. We develop a novel
+method to engineer a comprehensive prompt for contextual sentiment analysis
+which unlocks the true capabilities of modern LLM. This enables us to carefully
+retrieve sentiments, perceived advantages or disadvantages, and the relevance
+towards the analyzed company. Logistic regression is used to evaluate whether
+the extracted message contents reflect stock price movements. As a result,
+GPT-4 exhibited substantial accuracy, outperforming BERT in five out of six
+months and substantially exceeding a naive buy-and-hold strategy, reaching a
+peak accuracy of 71.47 % in May. The study also highlights the importance of
+prompt engineering in obtaining desired outputs from GPT-4's contextual
+abilities. However, the costs of deploying GPT-4 and the need for fine-tuning
+prompts highlight some practical considerations for its use.
+"
+LoGoPrompt: Synthetic Text Images Can Be Good Visual Prompts for  Vision-Language Models,Cheng Shi,http://arxiv.org/pdf/2309.01155v2.pdf,2023-09-03,['cs.cv'],2309.01155v2.pdf,"  Prompt engineering is a powerful tool used to enhance the performance of
+pre-trained models on downstream tasks. For example, providing the prompt
+""Let's think step by step"" improved GPT-3's reasoning accuracy to 63% on
+MutiArith while prompting ""a photo of"" filled with a class name enables CLIP to
+achieve $80$\% zero-shot accuracy on ImageNet. While previous research has
+explored prompt learning for the visual modality, analyzing what constitutes a
+good visual prompt specifically for image recognition is limited. In addition,
+existing visual prompt tuning methods' generalization ability is worse than
+text-only prompting tuning. This paper explores our key insight: synthetic text
+images are good visual prompts for vision-language models! To achieve that, we
+propose our LoGoPrompt, which reformulates the classification objective to the
+visual prompt selection and addresses the chicken-and-egg challenge of first
+adding synthetic text images as class-wise visual prompts or predicting the
+class first. Without any trainable visual prompt parameters, experimental
+results on 16 datasets demonstrate that our method consistently outperforms
+state-of-the-art methods in few-shot learning, base-to-new generalization, and
+domain generalization.
+"
+FIAT: Fusing learning paradigms with Instruction-Accelerated Tuning,Xinyi Wang,http://arxiv.org/pdf/2309.04663v2.pdf,2023-09-09,"['cs.cl', 'cs.ai']",2309.04663v2.pdf,"  Learning paradigms for large language models (LLMs) currently tend to fall
+within either in-context learning (ICL) or full fine-tuning. Each of these
+comes with their own trade-offs based on available data, model size, compute
+cost, ease-of-use, and final quality with neither solution performing well
+across-the-board. In this article, we first describe ICL and fine-tuning
+paradigms in a way that highlights their natural connections. Based on these
+connections, we propose a new learning paradigm called FIAT that fuses the best
+of these paradigms together, enabling prompt-engineered instructions and
+chain-of-thought reasoning with the very largest models while also using
+similar methods to perform parameter updates on a modestly-sized LLM with
+parameter-efficient tuning. We evaluate FIAT's effectiveness on a variety of
+multilingual tasks and observe that FIAT performs better than both ICL and
+fine-tuning at scales ranging from 100-10,000 training examples. We hope that
+FIAT provides a practical way of harnessing the full potential of LLMs without
+needing to make a hard choice between learning paradigms.
+"
+Toward Reproducing Network Research Results Using Large Language Models,Qiao Xiang,http://arxiv.org/pdf/2309.04716v1.pdf,2023-09-09,"['cs.lg', 'cs.ai', 'cs.cl']",2309.04716v1.pdf,"  Reproducing research results in the networking community is important for
+both academia and industry. The current best practice typically resorts to
+three approaches: (1) looking for publicly available prototypes; (2) contacting
+the authors to get a private prototype; and (3) manually implementing a
+prototype following the description of the publication. However, most published
+network research does not have public prototypes and private prototypes are
+hard to get. As such, most reproducing efforts are spent on manual
+implementation based on the publications, which is both time and labor
+consuming and error-prone. In this paper, we boldly propose reproducing network
+research results using the emerging large language models (LLMs). In
+particular, we first prove its feasibility with a small-scale experiment, in
+which four students with essential networking knowledge each reproduces a
+different networking system published in prominent conferences and journals by
+prompt engineering ChatGPT. We report the experiment's observations and lessons
+and discuss future open research questions of this proposal. This work raises
+no ethical issue.
+"
+Detecting Natural Language Biases with Prompt-based Learning,Md Abdul Aowal,http://arxiv.org/pdf/2309.05227v1.pdf,2023-09-11,"['cs.cl', 'cs.ai']",2309.05227v1.pdf,"  In this project, we want to explore the newly emerging field of prompt
+engineering and apply it to the downstream task of detecting LM biases. More
+concretely, we explore how to design prompts that can indicate 4 different
+types of biases: (1) gender, (2) race, (3) sexual orientation, and (4)
+religion-based. Within our project, we experiment with different manually
+crafted prompts that can draw out the subtle biases that may be present in the
+language model. We apply these prompts to multiple variations of popular and
+well-recognized models: BERT, RoBERTa, and T5 to evaluate their biases. We
+provide a comparative analysis of these models and assess them using a two-fold
+method: use human judgment to decide whether model predictions are biased and
+utilize model-level judgment (through further prompts) to understand if a model
+can self-diagnose the biases of its own prediction.
+"
+Two Timin': Repairing Smart Contracts With A Two-Layered Approach,Abhinav Jain,http://arxiv.org/pdf/2309.07841v1.pdf,2023-09-14,"['cs.cr', 'cs.ai']",2309.07841v1.pdf,"  Due to the modern relevance of blockchain technology, smart contracts present
+both substantial risks and benefits. Vulnerabilities within them can trigger a
+cascade of consequences, resulting in significant losses. Many current papers
+primarily focus on classifying smart contracts for malicious intent, often
+relying on limited contract characteristics, such as bytecode or opcode. This
+paper proposes a novel, two-layered framework: 1) classifying and 2) directly
+repairing malicious contracts. Slither's vulnerability report is combined with
+source code and passed through a pre-trained RandomForestClassifier (RFC) and
+Large Language Models (LLMs), classifying and repairing each suggested
+vulnerability. Experiments demonstrate the effectiveness of fine-tuned and
+prompt-engineered LLMs. The smart contract repair models, built from
+pre-trained GPT-3.5-Turbo and fine-tuned Llama-2-7B models, reduced the overall
+vulnerability count by 97.5% and 96.7% respectively. A manual inspection of
+repaired contracts shows that all retain functionality, indicating that the
+proposed method is appropriate for automatic batch classification and repair of
+vulnerabilities in smart contracts.
+"
+Large Language Models for Failure Mode Classification: An Investigation,Michael Stewart,http://arxiv.org/pdf/2309.08181v1.pdf,2023-09-15,['cs.cl'],2309.08181v1.pdf,"  In this paper we present the first investigation into the effectiveness of
+Large Language Models (LLMs) for Failure Mode Classification (FMC). FMC, the
+task of automatically labelling an observation with a corresponding failure
+mode code, is a critical task in the maintenance domain as it reduces the need
+for reliability engineers to spend their time manually analysing work orders.
+We detail our approach to prompt engineering to enable an LLM to predict the
+failure mode of a given observation using a restricted code list. We
+demonstrate that the performance of a GPT-3.5 model (F1=0.80) fine-tuned on
+annotated data is a significant improvement over a currently available text
+classification model (F1=0.60) trained on the same annotated data set. The
+fine-tuned model also outperforms the out-of-the box GPT-3.5 (F1=0.46). This
+investigation reinforces the need for high quality fine-tuning data sets for
+domain-specific tasks using LLMs.
+"
+Safurai 001: New Qualitative Approach for Code LLM Evaluation,Davide Cifarelli,http://arxiv.org/pdf/2309.11385v1.pdf,2023-09-20,['cs.cl'],2309.11385v1.pdf,"  This paper presents Safurai-001, a new Large Language Model (LLM) with
+significant potential in the domain of coding assistance. Driven by recent
+advancements in coding LLMs, Safurai-001 competes in performance with the
+latest models like WizardCoder [Xu et al., 2023], PanguCoder [Shen et al.,
+2023] and Phi-1 [Gunasekar et al., 2023] but aims to deliver a more
+conversational interaction. By capitalizing on the progress in data engineering
+(including latest techniques of data transformation and prompt engineering) and
+instruction tuning, this new model promises to stand toe-to-toe with recent
+closed and open source developments. Recognizing the need for an efficacious
+evaluation metric for coding LLMs, this paper also introduces GPT4-based
+MultiParameters, an evaluation benchmark that harnesses varied parameters to
+present a comprehensive insight into the models functioning and performance.
+Our assessment shows that Safurai-001 can outperform GPT-3.5 by 1.58% and
+WizardCoder by 18.78% in the Code Readability parameter and more.
+"
+A Practical Survey on Zero-shot Prompt Design for In-context Learning,Yinheng Li,http://arxiv.org/pdf/2309.13205v1.pdf,2023-09-22,"['cs.cl', 'cs.ai', 'cs.et', 'cs.lg']",2309.13205v1.pdf,"  The remarkable advancements in large language models (LLMs) have brought
+about significant improvements in Natural Language Processing(NLP) tasks. This
+paper presents a comprehensive review of in-context learning techniques,
+focusing on different types of prompts, including discrete, continuous,
+few-shot, and zero-shot, and their impact on LLM performance. We explore
+various approaches to prompt design, such as manual design, optimization
+algorithms, and evaluation methods, to optimize LLM performance across diverse
+tasks. Our review covers key research studies in prompt engineering, discussing
+their methodologies and contributions to the field. We also delve into the
+challenges faced in evaluating prompt performance, given the absence of a
+single ""best"" prompt and the importance of considering multiple metrics. In
+conclusion, the paper highlights the critical role of prompt design in
+harnessing the full potential of LLMs and provides insights into the
+combination of manual design, optimization techniques, and rigorous evaluation
+for more effective and efficient use of LLMs in various NLP tasks.
+"
+A Chat About Boring Problems: Studying GPT-based text normalization,Yang Zhang,http://arxiv.org/pdf/2309.13426v1.pdf,2023-09-23,"['cs.cl', 'cs.ai']",2309.13426v1.pdf,"  Text normalization - the conversion of text from written to spoken form - is
+traditionally assumed to be an ill-formed task for language models. In this
+work, we argue otherwise. We empirically show the capacity of Large-Language
+Models (LLM) for text normalization in few-shot scenarios. Combining
+self-consistency reasoning with linguistic-informed prompt engineering, we find
+LLM based text normalization to achieve error rates around 40\% lower than top
+normalization systems. Further, upon error analysis, we note key limitations in
+the conventional design of text normalization tasks. We create a new taxonomy
+of text normalization errors and apply it to results from GPT-3.5-Turbo and
+GPT-4.0. Through this new framework, we can identify strengths and weaknesses
+of GPT-based TN, opening opportunities for future work.
+"
+DynaCon: Dynamic Robot Planner with Contextual Awareness via LLMs,Gyeongmin Kim,http://arxiv.org/pdf/2309.16031v1.pdf,2023-09-27,['cs.ro'],2309.16031v1.pdf,"  Mobile robots often rely on pre-existing maps for effective path planning and
+navigation. However, when these maps are unavailable, particularly in
+unfamiliar environments, a different approach become essential. This paper
+introduces DynaCon, a novel system designed to provide mobile robots with
+contextual awareness and dynamic adaptability during navigation, eliminating
+the reliance of traditional maps. DynaCon integrates real-time feedback with an
+object server, prompt engineering, and navigation modules. By harnessing the
+capabilities of Large Language Models (LLMs), DynaCon not only understands
+patterns within given numeric series but also excels at categorizing objects
+into matched spaces. This facilitates dynamic path planner imbued with
+contextual awareness. We validated the effectiveness of DynaCon through an
+experiment where a robot successfully navigated to its goal using reasoning.
+Source code and experiment videos for this work can be found at:
+https://sites.google.com/view/dynacon.
+"
+Cyber Sentinel: Exploring Conversational Agents in Streamlining Security  Tasks with GPT-4,Mehrdad Kaheh,http://arxiv.org/pdf/2309.16422v1.pdf,2023-09-28,['cs.cr'],2309.16422v1.pdf,"  In an era where cyberspace is both a battleground and a backbone of modern
+society, the urgency of safeguarding digital assets against ever-evolving
+threats is paramount. This paper introduces Cyber Sentinel, an innovative
+task-oriented cybersecurity dialogue system that is effectively capable of
+managing two core functions: explaining potential cyber threats within an
+organization to the user, and taking proactive/reactive security actions when
+instructed by the user. Cyber Sentinel embodies the fusion of artificial
+intelligence, cybersecurity domain expertise, and real-time data analysis to
+combat the multifaceted challenges posed by cyber adversaries. This article
+delves into the process of creating such a system and how it can interact with
+other components typically found in cybersecurity organizations. Our work is a
+novel approach to task-oriented dialogue systems, leveraging the power of
+chaining GPT-4 models combined with prompt engineering across all sub-tasks. We
+also highlight its pivotal role in enhancing cybersecurity communication and
+interaction, concluding that not only does this framework enhance the system's
+transparency (Explainable AI) but also streamlines the decision-making process
+and responding to threats (Actionable AI), therefore marking a significant
+advancement in the realm of cybersecurity communication.
+"
+"A Sign Language Recognition System with Pepper, Lightweight-Transformer,  and LLM",JongYoon Lim,http://arxiv.org/pdf/2309.16898v1.pdf,2023-09-28,"['cs.ro', 'cs.cl', 'cs.cv', 'cs.hc']",2309.16898v1.pdf,"  This research explores using lightweight deep neural network architectures to
+enable the humanoid robot Pepper to understand American Sign Language (ASL) and
+facilitate non-verbal human-robot interaction. First, we introduce a
+lightweight and efficient model for ASL understanding optimized for embedded
+systems, ensuring rapid sign recognition while conserving computational
+resources. Building upon this, we employ large language models (LLMs) for
+intelligent robot interactions. Through intricate prompt engineering, we tailor
+interactions to allow the Pepper Robot to generate natural Co-Speech Gesture
+responses, laying the foundation for more organic and intuitive humanoid-robot
+dialogues. Finally, we present an integrated software pipeline, embodying
+advancements in a socially aware AI interaction model. Leveraging the Pepper
+Robot's capabilities, we demonstrate the practicality and effectiveness of our
+approach in real-world scenarios. The results highlight a profound potential
+for enhancing human-robot interaction through non-verbal interactions, bridging
+communication gaps, and making technology more accessible and understandable.
+"
+SPELL: Semantic Prompt Evolution based on a LLM,Yujian Betterest Li,http://arxiv.org/pdf/2310.01260v1.pdf,2023-10-02,"['cs.cl', 'cs.ai']",2310.01260v1.pdf,"  Prompt engineering is a new paradigm for enhancing the performance of trained
+neural network models. For optimizing text-style prompts, existing methods
+usually individually operate small portions of a text step by step, which
+either breaks the fluency or could not globally adjust a prompt. Since large
+language models (LLMs) have powerful ability of generating coherent texts token
+by token, can we utilize LLMs for improving prompts? Based on this motivation,
+in this paper, considering a trained LLM as a text generator, we attempt to
+design a black-box evolution algorithm for automatically optimizing texts,
+namely SPELL (Semantic Prompt Evolution based on a LLM). The proposed method is
+evaluated with different LLMs and evolution parameters in different text tasks.
+Experimental results show that SPELL could rapidly improve the prompts indeed.
+We further explore the evolution process and discuss on the limitations,
+potential possibilities and future work.
+"
+Co-audit: tools to help humans double-check AI-generated content,Andrew D. Gordon,http://arxiv.org/pdf/2310.01297v1.pdf,2023-10-02,"['cs.hc', 'cs.ai', 'cs.cl', 'cs.pl']",2310.01297v1.pdf,"  Users are increasingly being warned to check AI-generated content for
+correctness. Still, as LLMs (and other generative models) generate more complex
+output, such as summaries, tables, or code, it becomes harder for the user to
+audit or evaluate the output for quality or correctness. Hence, we are seeing
+the emergence of tool-assisted experiences to help the user double-check a
+piece of AI-generated content. We refer to these as co-audit tools. Co-audit
+tools complement prompt engineering techniques: one helps the user construct
+the input prompt, while the other helps them check the output response. As a
+specific example, this paper describes recent research on co-audit tools for
+spreadsheet computations powered by generative models. We explain why co-audit
+experiences are essential for any application of generative AI where quality is
+important and errors are consequential (as is common in spreadsheet
+computations). We propose a preliminary list of principles for co-audit, and
+outline research challenges.
+"
+Chain of Natural Language Inference for Reducing Large Language Model  Ungrounded Hallucinations,Deren Lei,http://arxiv.org/pdf/2310.03951v2.pdf,2023-10-06,"['cs.cl', 'cs.ai']",2310.03951v2.pdf,"  Large language models (LLMs) can generate fluent natural language texts when
+given relevant documents as background context. This ability has attracted
+considerable interest in developing industry applications of LLMs. However,
+LLMs are prone to generate hallucinations that are not supported by the
+provided sources. In this paper, we propose a hierarchical framework to detect
+and mitigate such ungrounded hallucination. Our framework uses Chain of Natural
+Language Inference (CoNLI) for hallucination detection and hallucination
+reduction via post-editing. Our approach achieves state-of-the-art performance
+on hallucination detection and enhances text quality through rewrite, using
+LLMs without any fine-tuning or domain-specific prompt engineering. We show
+that this simple plug-and-play framework can serve as an effective choice for
+hallucination detection and reduction, achieving competitive performance across
+various contexts.
+"
+LLM4VV: Developing LLM-Driven Testsuite for Compiler Validation,Christian Munley,http://arxiv.org/pdf/2310.04963v2.pdf,2023-10-08,['cs.ai'],2310.04963v2.pdf,"  Large language models (LLMs) are a new and powerful tool for a wide span of
+applications involving natural language and demonstrate impressive code
+generation abilities. In this paper, we explore the capabilitity of
+state-of-the-art LLMs, including closed-source options like OpenAI GPT-4 and
+open-source alternatives like Meta AI Codellama, to automatically generate
+tests and use these tests to validate and verify compiler implementations of a
+directive-based programming paradigm, OpenACC. Our approach entails exploring
+various prompt engineering techniques including a code template,
+retrieval-augmented generation (RAG) with code template, expressive prompt
+using RAG with code template, one-shot example, and RAG with one-shot example.
+This paper focuses on (a) exploring the capabilities of the latest LLMs for
+code generation, (b) investigating prompt and fine tuning methods, and (c)
+analyzing the outcome of LLMs generated tests
+"
+Large Language Models for Propaganda Detection,Kilian Sprenkamp,http://arxiv.org/pdf/2310.06422v1.pdf,2023-10-10,"['cs.cl', 'cs.ai']",2310.06422v1.pdf,"  The prevalence of propaganda in our digital society poses a challenge to
+societal harmony and the dissemination of truth. Detecting propaganda through
+NLP in text is challenging due to subtle manipulation techniques and contextual
+dependencies. To address this issue, we investigate the effectiveness of modern
+Large Language Models (LLMs) such as GPT-3 and GPT-4 for propaganda detection.
+We conduct experiments using the SemEval-2020 task 11 dataset, which features
+news articles labeled with 14 propaganda techniques as a multi-label
+classification problem. Five variations of GPT-3 and GPT-4 are employed,
+incorporating various prompt engineering and fine-tuning strategies across the
+different models. We evaluate the models' performance by assessing metrics such
+as $F1$ score, $Precision$, and $Recall$, comparing the results with the
+current state-of-the-art approach using RoBERTa. Our findings demonstrate that
+GPT-4 achieves comparable results to the current state-of-the-art. Further,
+this study analyzes the potential and challenges of LLMs in complex tasks like
+propaganda detection.
+"
+Forgetful Large Language Models: Lessons Learned from Using LLMs in  Robot Programming,Juo-Tung Chen,http://arxiv.org/pdf/2310.06646v1.pdf,2023-10-10,['cs.ro'],2310.06646v1.pdf,"  Large language models offer new ways of empowering people to program robot
+applications-namely, code generation via prompting. However, the code generated
+by LLMs is susceptible to errors. This work reports a preliminary exploration
+that empirically characterizes common errors produced by LLMs in robot
+programming. We categorize these errors into two phases: interpretation and
+execution. In this work, we focus on errors in execution and observe that they
+are caused by LLMs being ""forgetful"" of key information provided in user
+prompts. Based on this observation, we propose prompt engineering tactics
+designed to reduce errors in execution. We then demonstrate the effectiveness
+of these tactics with three language models: ChatGPT, Bard, and LLaMA-2.
+Finally, we discuss lessons learned from using LLMs in robot programming and
+call for the benchmarking of LLM-powered end-user development of robot
+applications.
+"
+LLMs Killed the Script Kiddie: How Agents Supported by Large Language  Models Change the Landscape of Network Threat Testing,Stephen Moskal,http://arxiv.org/pdf/2310.06936v1.pdf,2023-10-10,"['cs.cr', 'cs.lg']",2310.06936v1.pdf,"  In this paper, we explore the potential of Large Language Models (LLMs) to
+reason about threats, generate information about tools, and automate cyber
+campaigns. We begin with a manual exploration of LLMs in supporting specific
+threat-related actions and decisions. We proceed by automating the decision
+process in a cyber campaign. We present prompt engineering approaches for a
+plan-act-report loop for one action of a threat campaign and and a prompt
+chaining design that directs the sequential decision process of a multi-action
+campaign. We assess the extent of LLM's cyber-specific knowledge w.r.t the
+short campaign we demonstrate and provide insights into prompt design for
+eliciting actionable responses. We discuss the potential impact of LLMs on the
+threat landscape and the ethical considerations of using LLMs for accelerating
+threat actor capabilities. We report a promising, yet concerning, application
+of generative AI to cyber threats. However, the LLM's capabilities to deal with
+more complex networks, sophisticated vulnerabilities, and the sensitivity of
+prompts are open questions. This research should spur deliberations over the
+inevitable advancements in LLM-supported cyber adversarial landscape.
+"
+Beyond Factuality: A Comprehensive Evaluation of Large Language Models  as Knowledge Generators,Liang Chen,http://arxiv.org/pdf/2310.07289v1.pdf,2023-10-11,['cs.cl'],2310.07289v1.pdf,"  Large language models (LLMs) outperform information retrieval techniques for
+downstream knowledge-intensive tasks when being prompted to generate world
+knowledge. However, community concerns abound regarding the factuality and
+potential implications of using this uncensored knowledge. In light of this, we
+introduce CONNER, a COmpreheNsive kNowledge Evaluation fRamework, designed to
+systematically and automatically evaluate generated knowledge from six
+important perspectives -- Factuality, Relevance, Coherence, Informativeness,
+Helpfulness and Validity. We conduct an extensive empirical analysis of the
+generated knowledge from three different types of LLMs on two widely studied
+knowledge-intensive tasks, i.e., open-domain question answering and
+knowledge-grounded dialogue. Surprisingly, our study reveals that the
+factuality of generated knowledge, even if lower, does not significantly hinder
+downstream tasks. Instead, the relevance and coherence of the outputs are more
+important than small factual mistakes. Further, we show how to use CONNER to
+improve knowledge-intensive tasks by designing two strategies: Prompt
+Engineering and Knowledge Selection. Our evaluation code and LLM-generated
+knowledge with human annotations will be released to facilitate future
+research.
+"
+Multimodal Large Language Model for Visual Navigation,Yao-Hung Hubert Tsai,http://arxiv.org/pdf/2310.08669v2.pdf,2023-10-12,"['cs.cv', 'cs.ro']",2310.08669v2.pdf,"  Recent efforts to enable visual navigation using large language models have
+mainly focused on developing complex prompt systems. These systems incorporate
+instructions, observations, and history into massive text prompts, which are
+then combined with pre-trained large language models to facilitate visual
+navigation. In contrast, our approach aims to fine-tune large language models
+for visual navigation without extensive prompt engineering. Our design involves
+a simple text prompt, current observations, and a history collector model that
+gathers information from previous observations as input. For output, our design
+provides a probability distribution of possible actions that the agent can take
+during navigation. We train our model using human demonstrations and collision
+signals from the Habitat-Matterport 3D Dataset (HM3D). Experimental results
+demonstrate that our method outperforms state-of-the-art behavior cloning
+methods and effectively reduces collision rates.
+"
+GPTutor: an open-source AI pair programming tool alternative to Copilot,Eason Chen,http://arxiv.org/pdf/2310.13896v3.pdf,2023-10-21,['cs.hc'],2310.13896v3.pdf,"  This paper presents the latest progress of GPTutor: a ChatGPT-powered
+programming tool extension in Visual Studio Code. The emergence of Large
+Language Models (LLMs) has improved software development efficiency, but their
+performance can be hindered by training data limitations and prompt design
+issues. Existing LLM development tools often operate as black boxes, with users
+unable to view the prompts used and unable to improve performance by correcting
+prompts when errors occur. To address the aforementioned issues, GPTutor was
+introduced as an open-source AI pair programming tool, offering an alternative
+to Copilot. GPTutor empowers users to customize prompts for various programming
+languages and scenarios, with support for 120+ human languages and 50+
+programming languages. Users can fine-tune prompts to correct the errors from
+LLM for precision and efficient code generation. At the end of the paper, we
+underscore GPTutor's potential through examples, including demonstrating its
+proficiency in interpreting and generating Sui-Move, a newly introduced smart
+contract language, using prompt engineering.
+"
+Open-Ended Instructable Embodied Agents with Memory-Augmented Large  Language Models,Gabriel Sarch,http://arxiv.org/pdf/2310.15127v1.pdf,2023-10-23,"['cs.ai', 'cs.cl', 'cs.lg', 'cs.ro']",2310.15127v1.pdf,"  Pre-trained and frozen LLMs can effectively map simple scene re-arrangement
+instructions to programs over a robot's visuomotor functions through
+appropriate few-shot example prompting. To parse open-domain natural language
+and adapt to a user's idiosyncratic procedures, not known during prompt
+engineering time, fixed prompts fall short. In this paper, we introduce HELPER,
+an embodied agent equipped with an external memory of language-program pairs
+that parses free-form human-robot dialogue into action programs through
+retrieval-augmented LLM prompting: relevant memories are retrieved based on the
+current dialogue, instruction, correction or VLM description, and used as
+in-context prompt examples for LLM querying. The memory is expanded during
+deployment to include pairs of user's language and action plans, to assist
+future inferences and personalize them to the user's language and routines.
+HELPER sets a new state-of-the-art in the TEACh benchmark in both Execution
+from Dialog History (EDH) and Trajectory from Dialogue (TfD), with 1.7x
+improvement over the previous SOTA for TfD. Our models, code and video results
+can be found in our project's website: https://helper-agent-llm.github.io.
+"
+TaskDiff: A Similarity Metric for Task-Oriented Conversations,Ankita Bhaumik,http://arxiv.org/pdf/2310.15298v2.pdf,2023-10-23,"['cs.cl', 'cs.ai']",2310.15298v2.pdf,"  The popularity of conversational digital assistants has resulted in the
+availability of large amounts of conversational data which can be utilized for
+improved user experience and personalized response generation. Building these
+assistants using popular large language models like ChatGPT also require
+additional emphasis on prompt engineering and evaluation methods. Textual
+similarity metrics are a key ingredient for such analysis and evaluations.
+While many similarity metrics have been proposed in the literature, they have
+not proven effective for task-oriented conversations as they do not take
+advantage of unique conversational features. To address this gap, we present
+TaskDiff, a novel conversational similarity metric that utilizes different
+dialogue components (utterances, intents, and slots) and their distributions to
+compute similarity. Extensive experimental evaluation of TaskDiff on a
+benchmark dataset demonstrates its superior performance and improved robustness
+over other related approaches.
+"
+Large language models for aspect-based sentiment analysis,Paul F. Simmering,http://arxiv.org/pdf/2310.18025v1.pdf,2023-10-27,"['cs.cl', 'cs.ai']",2310.18025v1.pdf,"  Large language models (LLMs) offer unprecedented text completion
+capabilities. As general models, they can fulfill a wide range of roles,
+including those of more specialized models. We assess the performance of GPT-4
+and GPT-3.5 in zero shot, few shot and fine-tuned settings on the aspect-based
+sentiment analysis (ABSA) task. Fine-tuned GPT-3.5 achieves a state-of-the-art
+F1 score of 83.8 on the joint aspect term extraction and polarity
+classification task of the SemEval-2014 Task 4, improving upon InstructABSA
+[@scaria_instructabsa_2023] by 5.7%. However, this comes at the price of 1000
+times more model parameters and thus increased inference cost. We discuss the
+the cost-performance trade-offs of different models, and analyze the typical
+errors that they make. Our results also indicate that detailed prompts improve
+performance in zero-shot and few-shot settings but are not necessary for
+fine-tuned models. This evidence is relevant for practioners that are faced
+with the choice of prompt engineering versus fine-tuning when using LLMs for
+ABSA.
+"
+Can Large Language Models Capture Public Opinion about Global Warming?  An Empirical Assessment of Algorithmic Fidelity and Bias,S. Lee,http://arxiv.org/pdf/2311.00217v1.pdf,2023-11-01,"['cs.ai', 'cs.cy']",2311.00217v1.pdf,"  Large language models (LLMs) have demonstrated their potential in social
+science research by emulating human perceptions and behaviors, a concept
+referred to as algorithmic fidelity. This study assesses the algorithmic
+fidelity and bias of LLMs by utilizing two nationally representative climate
+change surveys. The LLMs were conditioned on demographics and/or psychological
+covariates to simulate survey responses. The findings indicate that LLMs can
+effectively capture presidential voting behaviors but encounter challenges in
+accurately representing global warming perspectives when relevant covariates
+are not included. GPT-4 exhibits improved performance when conditioned on both
+demographics and covariates. However, disparities emerge in LLM estimations of
+the views of certain groups, with LLMs tending to underestimate worry about
+global warming among Black Americans. While highlighting the potential of LLMs
+to aid social science research, these results underscore the importance of
+meticulous conditioning, model selection, survey question format, and bias
+assessment when employing LLMs for survey simulation. Further investigation
+into prompt engineering and algorithm auditing is essential to harness the
+power of LLMs while addressing their inherent limitations.
+"
+Noisy Exemplars Make Large Language Models More Robust: A  Domain-Agnostic Behavioral Analysis,Hongyi Zheng,http://arxiv.org/pdf/2311.00258v1.pdf,2023-11-01,"['cs.cl', 'cs.lg']",2311.00258v1.pdf,"  Recent advances in prompt engineering enable large language models (LLMs) to
+solve multi-hop logical reasoning problems with impressive accuracy. However,
+there is little existing work investigating the robustness of LLMs with
+few-shot prompting techniques. Therefore, we introduce a systematic approach to
+test the robustness of LLMs in multi-hop reasoning tasks via domain-agnostic
+perturbations. We include perturbations at multiple levels of abstractions
+(e.g. lexical perturbations such as typos, and semantic perturbations such as
+the inclusion of intermediate reasoning steps in the questions) to conduct
+behavioral analysis on the LLMs. Throughout our experiments, we find that
+models are more sensitive to certain perturbations such as replacing words with
+their synonyms. We also demonstrate that increasing the proportion of perturbed
+exemplars in the prompts improves the robustness of few-shot prompting methods.
+"
+Instruction Distillation Makes Large Language Models Efficient Zero-shot  Rankers,Weiwei Sun,http://arxiv.org/pdf/2311.01555v1.pdf,2023-11-02,"['cs.ir', 'cs.cl']",2311.01555v1.pdf,"  Recent studies have demonstrated the great potential of Large Language Models
+(LLMs) serving as zero-shot relevance rankers. The typical approach involves
+making comparisons between pairs or lists of documents. Although effective,
+these listwise and pairwise methods are not efficient and also heavily rely on
+intricate prompt engineering. To tackle this problem, we introduce a novel
+instruction distillation method. The key idea is to distill the pairwise
+ranking ability of open-sourced LLMs to a simpler but more efficient pointwise
+ranking. Specifically, given the same LLM, we first rank documents using the
+effective pairwise approach with complex instructions, and then distill the
+teacher predictions to the pointwise approach with simpler instructions.
+Evaluation results on the BEIR, TREC, and ReDial datasets demonstrate that
+instruction distillation can improve efficiency by 10 to 100x and also enhance
+the ranking performance of LLMs. Furthermore, our approach surpasses the
+performance of existing supervised methods like monoT5 and is on par with the
+state-of-the-art zero-shot methods. The code to reproduce our results is
+available at www.github.com/sunnweiwei/RankGPT.
+"
+Indicative Summarization of Long Discussions,Shahbaz Syed,http://arxiv.org/pdf/2311.01882v1.pdf,2023-11-03,['cs.cl'],2311.01882v1.pdf,"  Online forums encourage the exchange and discussion of different stances on
+many topics. Not only do they provide an opportunity to present one's own
+arguments, but may also gather a broad cross-section of others' arguments.
+However, the resulting long discussions are difficult to overview. This paper
+presents a novel unsupervised approach using large language models (LLMs) to
+generating indicative summaries for long discussions that basically serve as
+tables of contents. Our approach first clusters argument sentences, generates
+cluster labels as abstractive summaries, and classifies the generated cluster
+labels into argumentation frames resulting in a two-level summary. Based on an
+extensively optimized prompt engineering approach, we evaluate 19~LLMs for
+generative cluster labeling and frame classification. To evaluate the
+usefulness of our indicative summaries, we conduct a purpose-driven user study
+via a new visual interface called Discussion Explorer: It shows that our
+proposed indicative summaries serve as a convenient navigation tool to explore
+long discussions.
+"
+Automating Governing Knowledge Commons and Contextual Integrity (GKC-CI)  Privacy Policy Annotations with Large Language Models,Jake Chanenson,http://arxiv.org/pdf/2311.02192v1.pdf,2023-11-03,"['cs.cy', 'cs.cl', 'cs.lg']",2311.02192v1.pdf,"  Identifying contextual integrity (CI) and governing knowledge commons (GKC)
+parameters in privacy policy texts can facilitate normative privacy analysis.
+However, GKC-CI annotation has heretofore required manual or crowdsourced
+effort. This paper demonstrates that high-accuracy GKC-CI parameter annotation
+of privacy policies can be performed automatically using large language models.
+We fine-tune 18 open-source and proprietary models on 21,588 GKC-CI annotations
+from 16 ground truth privacy policies. Our best-performing model (fine-tuned
+GPT-3.5 Turbo with prompt engineering) has an accuracy of 86%, exceeding the
+performance of prior crowdsourcing approaches despite the complexity of privacy
+policy texts and the nuance of the GKC-CI annotation task. We apply our
+best-performing model to privacy policies from 164 popular online services,
+demonstrating the effectiveness of scaling GKC-CI annotation for data
+exploration. We make all annotated policies as well as the training data and
+scripts needed to fine-tune our best-performing model publicly available for
+future research.
+"
+Requirements Engineering using Generative AI: Prompts and Prompting  Patterns,Krishna Ronanki,http://arxiv.org/pdf/2311.03832v1.pdf,2023-11-07,['cs.se'],2311.03832v1.pdf,"  [Context]: Companies are increasingly recognizing the importance of
+automating Requirements Engineering (RE) tasks due to their resource-intensive
+nature. The advent of GenAI has made these tasks more amenable to automation,
+thanks to its ability to understand and interpret context effectively.
+[Problem]: However, in the context of GenAI, prompt engineering is a critical
+factor for success. Despite this, we currently lack tools and methods to
+systematically assess and determine the most effective prompt patterns to
+employ for a particular RE task. [Method]: Two tasks related to requirements,
+specifically requirement classification and tracing, were automated using the
+GPT-3.5 turbo API. The performance evaluation involved assessing various
+prompts created using 5 prompt patterns and implemented programmatically to
+perform the selected RE tasks, focusing on metrics such as precision, recall,
+accuracy, and F-Score. [Results]: This paper evaluates the effectiveness of the
+5 prompt patterns' ability to make GPT-3.5 turbo perform the selected RE tasks
+and offers recommendations on which prompt pattern to use for a specific RE
+task. Additionally, it also provides an evaluation framework as a reference for
+researchers and practitioners who want to evaluate different prompt patterns
+for different RE tasks.
+"
+Differentiable Prompt Makes Pre-trained Language Models Better Few-shot  Learners,Ningyu Zhang,http://arxiv.org/pdf/2108.13161v7.pdf,2021-08-30,"['cs.cl', 'cs.ai', 'cs.cv', 'cs.ir', 'cs.lg']",2108.13161v7.pdf,"  Large-scale pre-trained language models have contributed significantly to
+natural language processing by demonstrating remarkable abilities as few-shot
+learners. However, their effectiveness depends mainly on scaling the model
+parameters and prompt design, hindering their implementation in most real-world
+applications. This study proposes a novel pluggable, extensible, and efficient
+approach named DifferentiAble pRompT (DART), which can convert small language
+models into better few-shot learners without any prompt engineering. The main
+principle behind this approach involves reformulating potential natural
+language processing tasks into the task of a pre-trained language model and
+differentially optimizing the prompt template as well as the target label with
+backpropagation. Furthermore, the proposed approach can be: (i) Plugged to any
+pre-trained language models; (ii) Extended to widespread classification tasks.
+A comprehensive evaluation of standard NLP tasks demonstrates that the proposed
+approach achieves a better few-shot performance. Code is available in
+https://github.com/zjunlp/DART.
+"
+ActionCLIP: A New Paradigm for Video Action Recognition,Mengmeng Wang,http://arxiv.org/pdf/2109.08472v1.pdf,2021-09-17,['cs.cv'],2109.08472v1.pdf,"  The canonical approach to video action recognition dictates a neural model to
+do a classic and standard 1-of-N majority vote task. They are trained to
+predict a fixed set of predefined categories, limiting their transferable
+ability on new datasets with unseen concepts. In this paper, we provide a new
+perspective on action recognition by attaching importance to the semantic
+information of label texts rather than simply mapping them into numbers.
+Specifically, we model this task as a video-text matching problem within a
+multimodal learning framework, which strengthens the video representation with
+more semantic language supervision and enables our model to do zero-shot action
+recognition without any further labeled data or parameters requirements.
+Moreover, to handle the deficiency of label texts and make use of tremendous
+web data, we propose a new paradigm based on this multimodal learning framework
+for action recognition, which we dub ""pre-train, prompt and fine-tune"". This
+paradigm first learns powerful representations from pre-training on a large
+amount of web image-text or video-text data. Then it makes the action
+recognition task to act more like pre-training problems via prompt engineering.
+Finally, it end-to-end fine-tunes on target datasets to obtain strong
+performance. We give an instantiation of the new paradigm, ActionCLIP, which
+not only has superior and flexible zero-shot/few-shot transfer ability but also
+reaches a top performance on general action recognition task, achieving 83.8%
+top-1 accuracy on Kinetics-400 with a ViT-B/16 as the backbone. Code is
+available at https://github.com/sallymmx/ActionCLIP.git
+"
+CLIP-Adapter: Better Vision-Language Models with Feature Adapters,Peng Gao,http://arxiv.org/pdf/2110.04544v1.pdf,2021-10-09,"['cs.cv', 'cs.cl']",2110.04544v1.pdf,"  Large-scale contrastive vision-language pre-training has shown significant
+progress in visual representation learning. Unlike traditional visual systems
+trained by a fixed set of discrete labels, a new paradigm was introduced in
+\cite{radford2021learning} to directly learn to align images with raw texts in
+an open-vocabulary setting. On downstream tasks, a carefully chosen text prompt
+is employed to make zero-shot predictions.~To avoid non-trivial prompt
+engineering, context optimization \cite{zhou2021coop} has been proposed to
+learn continuous vectors as task-specific prompts with few-shot training
+examples.~In this paper, we show that there is an alternative path to achieve
+better vision-language models other than prompt tuning.~While prompt tuning is
+for the textual inputs, we propose CLIP-Adapter to conduct fine-tuning with
+feature adapters on either visual or language branch. Specifically,
+CLIP-Adapter adopts an additional bottleneck layer to learn new features and
+performs residual-style feature blending with the original pre-trained
+features.~As a consequence, CLIP-Adapter is able to outperform context
+optimization while maintains a simple design. Experiments and extensive
+ablation studies on various visual classification tasks demonstrate the
+effectiveness of our approach.
+"
+Symbolic Knowledge Distillation: from General Language Models to  Commonsense Models,Peter West,http://arxiv.org/pdf/2110.07178v2.pdf,2021-10-14,['cs.cl'],2110.07178v2.pdf,"  The common practice for training commonsense models has gone
+from-human-to-corpus-to-machine: humans author commonsense knowledge graphs in
+order to train commonsense models. In this work, we investigate an alternative,
+from-machine-to-corpus-to-machine: general language models author these
+commonsense knowledge graphs to train commonsense models. Our study leads to a
+new framework, Symbolic Knowledge Distillation. As with prior art in Knowledge
+Distillation (Hinton et al., 2015), our approach uses larger models to teach
+smaller models. A key difference is that we distill knowledge symbolically-as
+text-in addition to the neural model. We also distill only one aspect-the
+commonsense of a general language model teacher, allowing the student to be a
+different type, a commonsense model. Altogether, we show that careful prompt
+engineering and a separately trained critic model allow us to selectively
+distill high-quality causal commonsense from GPT-3, a general language model.
+Empirical results demonstrate that, for the first time, a human-authored
+commonsense knowledge graph is surpassed by our automatically distilled variant
+in all three criteria: quantity, quality, and diversity. In addition, it
+results in a neural commonsense model that surpasses the teacher model's
+commonsense capabilities despite its 100x smaller size. We apply this to the
+ATOMIC resource, and share our new symbolic knowledge graph and commonsense
+models.
+"
+Red Teaming Language Models with Language Models,Ethan Perez,http://arxiv.org/pdf/2202.03286v1.pdf,2022-02-07,"['cs.cl', 'cs.ai', 'cs.cr', 'cs.lg']",2202.03286v1.pdf,"  Language Models (LMs) often cannot be deployed because of their potential to
+harm users in hard-to-predict ways. Prior work identifies harmful behaviors
+before deployment by using human annotators to hand-write test cases. However,
+human annotation is expensive, limiting the number and diversity of test cases.
+In this work, we automatically find cases where a target LM behaves in a
+harmful way, by generating test cases (""red teaming"") using another LM. We
+evaluate the target LM's replies to generated test questions using a classifier
+trained to detect offensive content, uncovering tens of thousands of offensive
+replies in a 280B parameter LM chatbot. We explore several methods, from
+zero-shot generation to reinforcement learning, for generating test cases with
+varying levels of diversity and difficulty. Furthermore, we use prompt
+engineering to control LM-generated test cases to uncover a variety of other
+harms, automatically finding groups of people that the chatbot discusses in
+offensive ways, personal and hospital phone numbers generated as the chatbot's
+own contact info, leakage of private training data in generated text, and harms
+that occur over the course of a conversation. Overall, LM-based red teaming is
+one promising tool (among many needed) for finding and fixing diverse,
+undesirable LM behaviors before impacting users.
+"
+Learning to Prompt for Open-Vocabulary Object Detection with  Vision-Language Model,Yu Du,http://arxiv.org/pdf/2203.14940v1.pdf,2022-03-28,['cs.cv'],2203.14940v1.pdf,"  Recently, vision-language pre-training shows great potential in
+open-vocabulary object detection, where detectors trained on base classes are
+devised for detecting new classes. The class text embedding is firstly
+generated by feeding prompts to the text encoder of a pre-trained
+vision-language model. It is then used as the region classifier to supervise
+the training of a detector. The key element that leads to the success of this
+model is the proper prompt, which requires careful words tuning and ingenious
+design. To avoid laborious prompt engineering, there are some prompt
+representation learning methods being proposed for the image classification
+task, which however can only be sub-optimal solutions when applied to the
+detection task. In this paper, we introduce a novel method, detection prompt
+(DetPro), to learn continuous prompt representations for open-vocabulary object
+detection based on the pre-trained vision-language model. Different from the
+previous classification-oriented methods, DetPro has two highlights: 1) a
+background interpretation scheme to include the proposals in image background
+into the prompt training; 2) a context grading scheme to separate proposals in
+image foreground for tailored prompt training. We assemble DetPro with ViLD, a
+recent state-of-the-art open-world object detector, and conduct experiments on
+the LVIS as well as transfer learning on the Pascal VOC, COCO, Objects365
+datasets. Experimental results show that our DetPro outperforms the baseline
+ViLD in all settings, e.g., +3.4 APbox and +3.0 APmask improvements on the
+novel classes of LVIS. Code and models are available at
+https://github.com/dyabel/detpro.
+"
+No Token Left Behind: Explainability-Aided Image Classification and  Generation,Roni Paiss,http://arxiv.org/pdf/2204.04908v2.pdf,2022-04-11,['cs.cv'],2204.04908v2.pdf,"  The application of zero-shot learning in computer vision has been
+revolutionized by the use of image-text matching models. The most notable
+example, CLIP, has been widely used for both zero-shot classification and
+guiding generative models with a text prompt. However, the zero-shot use of
+CLIP is unstable with respect to the phrasing of the input text, making it
+necessary to carefully engineer the prompts used. We find that this instability
+stems from a selective similarity score, which is based only on a subset of the
+semantically meaningful input tokens. To mitigate it, we present a novel
+explainability-based approach, which adds a loss term to ensure that CLIP
+focuses on all relevant semantic parts of the input, in addition to employing
+the CLIP similarity loss used in previous works. When applied to one-shot
+classification through prompt engineering, our method yields an improvement in
+the recognition rate, without additional training or fine-tuning. Additionally,
+we show that CLIP guidance of generative models using our method significantly
+improves the generated images. Finally, we demonstrate a novel use of CLIP
+guidance for text-based image generation with spatial conditioning on object
+location, by requiring the image explainability heatmap for each object to be
+confined to a pre-determined bounding box.
+"
+On Measuring Social Biases in Prompt-Based Multi-Task Learning,Afra Feyza Akyürek,http://arxiv.org/pdf/2205.11605v1.pdf,2022-05-23,"['cs.cl', 'cs.cy']",2205.11605v1.pdf,"  Large language models trained on a mixture of NLP tasks that are converted
+into a text-to-text format using prompts, can generalize into novel forms of
+language and handle novel tasks. A large body of work within prompt engineering
+attempts to understand the effects of input forms and prompts in achieving
+superior performance. We consider an alternative measure and inquire whether
+the way in which an input is encoded affects social biases promoted in outputs.
+In this paper, we study T0, a large-scale multi-task text-to-text language
+model trained using prompt-based learning. We consider two different forms of
+semantically equivalent inputs: question-answer format and premise-hypothesis
+format. We use an existing bias benchmark for the former BBQ and create the
+first bias benchmark in natural language inference BBNLI with hand-written
+hypotheses while also converting each benchmark into the other form. The
+results on two benchmarks suggest that given two different formulations of
+essentially the same input, T0 conspicuously acts more biased in question
+answering form, which is seen during training, compared to premise-hypothesis
+form which is unlike its training examples. Code and data are released under
+https://github.com/feyzaakyurek/bbnli.
+"
+OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal  Regression,Wanhua Li,http://arxiv.org/pdf/2206.02338v2.pdf,2022-06-06,['cs.cv'],2206.02338v2.pdf,"  This paper presents a language-powered paradigm for ordinal regression.
+Existing methods usually treat each rank as a category and employ a set of
+weights to learn these concepts. These methods are easy to overfit and usually
+attain unsatisfactory performance as the learned concepts are mainly derived
+from the training set. Recent large pre-trained vision-language models like
+CLIP have shown impressive performance on various visual tasks. In this paper,
+we propose to learn the rank concepts from the rich semantic CLIP latent space.
+Specifically, we reformulate this task as an image-language matching problem
+with a contrastive objective, which regards labels as text and obtains a
+language prototype from a text encoder for each rank. While prompt engineering
+for CLIP is extremely time-consuming, we propose OrdinalCLIP, a differentiable
+prompting method for adapting CLIP for ordinal regression. OrdinalCLIP consists
+of learnable context tokens and learnable rank embeddings; The learnable rank
+embeddings are constructed by explicitly modeling numerical continuity,
+resulting in well-ordered, compact language prototypes in the CLIP space. Once
+learned, we can only save the language prototypes and discard the huge language
+model, resulting in zero additional computational overhead compared with the
+linear head counterpart. Experimental results show that our paradigm achieves
+competitive performance in general ordinal regression tasks, and gains
+improvements in few-shot and distribution shift settings for age estimation.
+The code is available at https://github.com/xk-huang/OrdinalCLIP.
+"
+P2P: Tuning Pre-trained Image Models for Point Cloud Analysis with  Point-to-Pixel Prompting,Ziyi Wang,http://arxiv.org/pdf/2208.02812v2.pdf,2022-08-04,"['cs.cv', 'cs.ai', 'cs.lg']",2208.02812v2.pdf,"  Nowadays, pre-training big models on large-scale datasets has become a
+crucial topic in deep learning. The pre-trained models with high representation
+ability and transferability achieve a great success and dominate many
+downstream tasks in natural language processing and 2D vision. However, it is
+non-trivial to promote such a pretraining-tuning paradigm to the 3D vision,
+given the limited training data that are relatively inconvenient to collect. In
+this paper, we provide a new perspective of leveraging pre-trained 2D knowledge
+in 3D domain to tackle this problem, tuning pre-trained image models with the
+novel Point-to-Pixel prompting for point cloud analysis at a minor parameter
+cost. Following the principle of prompting engineering, we transform point
+clouds into colorful images with geometry-preserved projection and
+geometry-aware coloring to adapt to pre-trained image models, whose weights are
+kept frozen during the end-to-end optimization of point cloud analysis tasks.
+We conduct extensive experiments to demonstrate that cooperating with our
+proposed Point-to-Pixel Prompting, better pre-trained image model will lead to
+consistently better performance in 3D vision. Enjoying prosperous development
+from image pre-training field, our method attains 89.3% accuracy on the hardest
+setting of ScanObjectNN, surpassing conventional point cloud models with much
+fewer trainable parameters. Our framework also exhibits very competitive
+performance on ModelNet classification and ShapeNet Part Segmentation. Code is
+available at https://github.com/wangzy22/P2P.
+"
+Unsupervised Hashing with Semantic Concept Mining,Rong-Cheng Tu,http://arxiv.org/pdf/2209.11475v1.pdf,2022-09-23,"['cs.cv', 'cs.ir']",2209.11475v1.pdf,"  Recently, to improve the unsupervised image retrieval performance, plenty of
+unsupervised hashing methods have been proposed by designing a semantic
+similarity matrix, which is based on the similarities between image features
+extracted by a pre-trained CNN model. However, most of these methods tend to
+ignore high-level abstract semantic concepts contained in images. Intuitively,
+concepts play an important role in calculating the similarity among images. In
+real-world scenarios, each image is associated with some concepts, and the
+similarity between two images will be larger if they share more identical
+concepts. Inspired by the above intuition, in this work, we propose a novel
+Unsupervised Hashing with Semantic Concept Mining, called UHSCM, which
+leverages a VLP model to construct a high-quality similarity matrix.
+Specifically, a set of randomly chosen concepts is first collected. Then, by
+employing a vision-language pretraining (VLP) model with the prompt engineering
+which has shown strong power in visual representation learning, the set of
+concepts is denoised according to the training images. Next, the proposed
+method UHSCM applies the VLP model with prompting again to mine the concept
+distribution of each image and construct a high-quality semantic similarity
+matrix based on the mined concept distributions. Finally, with the semantic
+similarity matrix as guiding information, a novel hashing loss with a modified
+contrastive loss based regularization item is proposed to optimize the hashing
+network. Extensive experiments on three benchmark datasets show that the
+proposed method outperforms the state-of-the-art baselines in the image
+retrieval task.
+"
+Robust Preference Learning for Storytelling via Contrastive  Reinforcement Learning,Louis Castricato,http://arxiv.org/pdf/2210.07792v2.pdf,2022-10-14,['cs.cl'],2210.07792v2.pdf,"  Controlled automated story generation seeks to generate natural language
+stories satisfying constraints from natural language critiques or preferences.
+Existing methods to control for story preference utilize prompt engineering
+which is labor intensive and often inconsistent. They may also use
+logit-manipulation methods which require annotated datasets to exist for the
+desired attributes. To address these issues, we first train a contrastive
+bi-encoder model to align stories with corresponding human critiques, named
+CARP, building a general purpose preference model. This is subsequently used as
+a reward function to fine-tune a generative language model via reinforcement
+learning. However, simply fine-tuning a generative language model with a
+contrastive reward model does not always reliably result in a story generation
+system capable of generating stories that meet user preferences. To increase
+story generation robustness we further fine-tune the contrastive reward model
+using a prompt-learning technique. A human participant study is then conducted
+comparing generations from our full system, ablations, and two baselines. We
+show that the full fine-tuning pipeline results in a story generator preferred
+over a LLM 20x as large as well as logit-based methods. This motivates the use
+of contrastive learning for general purpose human preference modeling.
+"
+Towards Equitable Representation in Text-to-Image Synthesis Models with  the Cross-Cultural Understanding Benchmark (CCUB) Dataset,Zhixuan Liu,http://arxiv.org/pdf/2301.12073v2.pdf,2023-01-28,['cs.cv'],2301.12073v2.pdf,"  It has been shown that accurate representation in media improves the
+well-being of the people who consume it. By contrast, inaccurate
+representations can negatively affect viewers and lead to harmful perceptions
+of other cultures. To achieve inclusive representation in generated images, we
+propose a culturally-aware priming approach for text-to-image synthesis using a
+small but culturally curated dataset that we collected, known here as
+Cross-Cultural Understanding Benchmark (CCUB) Dataset, to fight the bias
+prevalent in giant datasets. Our proposed approach is comprised of two
+fine-tuning techniques: (1) Adding visual context via fine-tuning a pre-trained
+text-to-image synthesis model, Stable Diffusion, on the CCUB text-image pairs,
+and (2) Adding semantic context via automated prompt engineering using the
+fine-tuned large language model, GPT-3, trained on our CCUB culturally-aware
+text data. CCUB dataset is curated and our approach is evaluated by people who
+have a personal relationship with that particular culture. Our experiments
+indicate that priming using both text and image is effective in improving the
+cultural relevance and decreasing the offensiveness of generated images while
+maintaining quality.
+"
+Trash to Treasure: Using text-to-image models to inform the design of  physical artefacts,Amy Smith,http://arxiv.org/pdf/2302.00561v1.pdf,2023-02-01,['cs.ai'],2302.00561v1.pdf,"  Text-to-image generative models have recently exploded in popularity and
+accessibility. Yet so far, use of these models in creative tasks that bridge
+the 2D digital world and the creation of physical artefacts has been
+understudied. We conduct a pilot study to investigate if and how text-to-image
+models can be used to assist in upstream tasks within the creative process,
+such as ideation and visualization, prior to a sculpture-making activity.
+Thirty participants selected sculpture-making materials and generated three
+images using the Stable Diffusion text-to-image generator, each with text
+prompts of their choice, with the aim of informing and then creating a physical
+sculpture. The majority of participants (23/30) reported that the generated
+images informed their sculptures, and 28/30 reported interest in using
+text-to-image models to help them in a creative task in the future. We identify
+several prompt engineering strategies and find that a participant's prompting
+strategy relates to their stage in the creative process. We discuss how our
+findings can inform support for users at different stages of the design process
+and for using text-to-image models for physical artefact design.
+"
+"Chat2VIS: Generating Data Visualisations via Natural Language using  ChatGPT, Codex and GPT-3 Large Language Models",Paula Maddigan,http://arxiv.org/pdf/2302.02094v2.pdf,2023-02-04,['cs.hc'],2302.02094v2.pdf,"  The field of data visualisation has long aimed to devise solutions for
+generating visualisations directly from natural language text. Research in
+Natural Language Interfaces (NLIs) has contributed towards the development of
+such techniques. However, the implementation of workable NLIs has always been
+challenging due to the inherent ambiguity of natural language, as well as in
+consequence of unclear and poorly written user queries which pose problems for
+existing language models in discerning user intent. Instead of pursuing the
+usual path of developing new iterations of language models, this study uniquely
+proposes leveraging the advancements in pre-trained large language models
+(LLMs) such as ChatGPT and GPT-3 to convert free-form natural language directly
+into code for appropriate visualisations. This paper presents a novel system,
+Chat2VIS, which takes advantage of the capabilities of LLMs and demonstrates
+how, with effective prompt engineering, the complex problem of language
+understanding can be solved more efficiently, resulting in simpler and more
+accurate end-to-end solutions than prior approaches. Chat2VIS shows that LLMs
+together with the proposed prompts offer a reliable approach to rendering
+visualisations from natural language queries, even when queries are highly
+misspecified and underspecified. This solution also presents a significant
+reduction in costs for the development of NLI systems, while attaining greater
+visualisation inference abilities compared to traditional NLP approaches that
+use hand-crafted grammar rules and tailored models. This study also presents
+how LLM prompts can be constructed in a way that preserves data security and
+privacy while being generalisable to different datasets. This work compares the
+performance of GPT-3, Codex and ChatGPT across a number of case studies and
+contrasts the performances with prior studies.
+"
+CHiLS: Zero-Shot Image Classification with Hierarchical Label Sets,Zachary Novack,http://arxiv.org/pdf/2302.02551v3.pdf,2023-02-06,"['cs.cv', 'cs.lg']",2302.02551v3.pdf,"  Open vocabulary models (e.g. CLIP) have shown strong performance on zero-shot
+classification through their ability generate embeddings for each class based
+on their (natural language) names. Prior work has focused on improving the
+accuracy of these models through prompt engineering or by incorporating a small
+amount of labeled downstream data (via finetuning). However, there has been
+little focus on improving the richness of the class names themselves, which can
+pose issues when class labels are coarsely-defined and are uninformative. We
+propose Classification with Hierarchical Label Sets (or CHiLS), an alternative
+strategy for zero-shot classification specifically designed for datasets with
+implicit semantic hierarchies. CHiLS proceeds in three steps: (i) for each
+class, produce a set of subclasses, using either existing label hierarchies or
+by querying GPT-3; (ii) perform the standard zero-shot CLIP procedure as though
+these subclasses were the labels of interest; (iii) map the predicted subclass
+back to its parent to produce the final prediction. Across numerous datasets
+with underlying hierarchical structure, CHiLS leads to improved accuracy in
+situations both with and without ground-truth hierarchical information. CHiLS
+is simple to implement within existing zero-shot pipelines and requires no
+additional training cost. Code is available at:
+https://github.com/acmi-lab/CHILS.
+"
+"A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on  Reasoning, Hallucination, and Interactivity",Yejin Bang,http://arxiv.org/pdf/2302.04023v2.pdf,2023-02-08,"['cs.cl', 'cs.ai']",2302.04023v2.pdf,"  This paper proposes a framework for quantitatively evaluating interactive
+LLMs such as ChatGPT using publicly available data sets. We carry out an
+extensive technical evaluation of ChatGPT using 23 data sets covering 8
+different common NLP application tasks. We evaluate the multitask, multilingual
+and multi-modal aspects of ChatGPT based on these data sets and a newly
+designed multimodal dataset. We find that ChatGPT outperforms LLMs with
+zero-shot learning on most tasks and even outperforms fine-tuned models on some
+tasks. We find that it is better at understanding non-Latin script languages
+than generating them. It is able to generate multimodal content from textual
+prompts, via an intermediate code generation step. Moreover, we find that
+ChatGPT is 63.41% accurate on average in 10 different reasoning categories
+under logical reasoning, non-textual reasoning, and commonsense reasoning,
+hence making it an unreliable reasoner. It is, for example, better at deductive
+than inductive reasoning. ChatGPT suffers from hallucination problems like
+other LLMs and it generates more extrinsic hallucinations from its parametric
+memory as it does not have access to an external knowledge base. Finally, the
+interactive feature of ChatGPT enables human collaboration with the underlying
+LLM to improve its performance, i.e, 8% ROUGE-1 on summarization and 2% ChrF++
+on machine translation, in a multi-turn ""prompt engineering"" fashion. We also
+release codebase for evaluation set extraction.
+"
+Prompt Stealing Attacks Against Text-to-Image Generation Models,Xinyue Shen,http://arxiv.org/pdf/2302.09923v1.pdf,2023-02-20,"['cs.cr', 'cs.lg']",2302.09923v1.pdf,"  Text-to-Image generation models have revolutionized the artwork design
+process and enabled anyone to create high-quality images by entering text
+descriptions called prompts. Creating a high-quality prompt that consists of a
+subject and several modifiers can be time-consuming and costly. In consequence,
+a trend of trading high-quality prompts on specialized marketplaces has
+emerged. In this paper, we propose a novel attack, namely prompt stealing
+attack, which aims to steal prompts from generated images by text-to-image
+generation models. Successful prompt stealing attacks direct violate the
+intellectual property and privacy of prompt engineers and also jeopardize the
+business model of prompt trading marketplaces. We first perform a large-scale
+analysis on a dataset collected by ourselves and show that a successful prompt
+stealing attack should consider a prompt's subject as well as its modifiers. We
+then propose the first learning-based prompt stealing attack, PromptStealer,
+and demonstrate its superiority over two baseline methods quantitatively and
+qualitatively. We also make some initial attempts to defend PromptStealer. In
+general, our study uncovers a new attack surface in the ecosystem created by
+the popular text-to-image generation models. We hope our results can help to
+mitigate the threat. To facilitate research in this field, we will share our
+dataset and code with the community.
+"
+Controlled and Conditional Text to Image Generation with Diffusion Prior,Pranav Aggarwal,http://arxiv.org/pdf/2302.11710v2.pdf,2023-02-23,['cs.cv'],2302.11710v2.pdf,"  Denoising Diffusion models have shown remarkable performance in generating
+diverse, high quality images from text. Numerous techniques have been proposed
+on top of or in alignment with models like Stable Diffusion and Imagen that
+generate images directly from text. A lesser explored approach is DALLE-2's two
+step process comprising a Diffusion Prior that generates a CLIP image embedding
+from text and a Diffusion Decoder that generates an image from a CLIP image
+embedding. We explore the capabilities of the Diffusion Prior and the
+advantages of an intermediate CLIP representation. We observe that Diffusion
+Prior can be used in a memory and compute efficient way to constrain the
+generation to a specific domain without altering the larger Diffusion Decoder.
+Moreover, we show that the Diffusion Prior can be trained with additional
+conditional information such as color histogram to further control the
+generation. We show quantitatively and qualitatively that the proposed
+approaches perform better than prompt engineering for domain specific
+generation and existing baselines for color conditioned generation. We believe
+that our observations and results will instigate further research into the
+diffusion prior and uncover more of its capabilities.
+"
+EvoPrompting: Language Models for Code-Level Neural Architecture Search,Angelica Chen,http://arxiv.org/pdf/2302.14838v2.pdf,2023-02-28,"['cs.ne', 'cs.ai', 'cs.cl', 'cs.lg']",2302.14838v2.pdf,"  Given the recent impressive accomplishments of language models (LMs) for code
+generation, we explore the use of LMs as adaptive mutation and crossover
+operators for an evolutionary neural architecture search (NAS) algorithm. While
+NAS still proves too difficult a task for LMs to succeed at solely through
+prompting, we find that the combination of evolutionary prompt engineering with
+soft prompt-tuning, a method we term EvoPrompting, consistently finds diverse
+and high performing models. We first demonstrate that EvoPrompting is effective
+on the computationally efficient MNIST-1D dataset, where EvoPrompting produces
+convolutional architecture variants that outperform both those designed by
+human experts and naive few-shot prompting in terms of accuracy and model size.
+We then apply our method to searching for graph neural networks on the CLRS
+Algorithmic Reasoning Benchmark, where EvoPrompting is able to design novel
+architectures that outperform current state-of-the-art models on 21 out of 30
+algorithmic reasoning tasks while maintaining similar model size. EvoPrompting
+is successful at designing accurate and efficient neural network architectures
+across a variety of machine learning tasks, while also being general enough for
+easy adaptation to other tasks beyond neural network design.
+"
+Extracting Accurate Materials Data from Research Papers with  Conversational Language Models and Prompt Engineering,Maciej P. Polak,http://arxiv.org/pdf/2303.05352v2.pdf,2023-03-07,"['cs.cl', 'cond-mat.mtrl-sci']",2303.05352v2.pdf,"  There has been a growing effort to replace hand extraction of data from
+research papers with automated data extraction based on natural language
+processing, language models, and recently, large language models (LLMs).
+Although these methods enable efficient extraction of data from large sets of
+research papers, they require a significant amount of up-front effort,
+expertise, and coding. In this work we propose the ChatExtract method that can
+fully automate very accurate data extraction with minimal initial effort and
+background, using an advanced conversational LLM. ChatExtract consists of a set
+of engineered prompts applied to a conversational LLM that both identify
+sentences with data, extract that data, and assure the data's correctness
+through a series of follow-up questions. These follow-up questions largely
+overcome known issues with LLMs providing factually inaccurate responses.
+ChatExtract can be applied with any conversational LLMs and yields very high
+quality data extraction. In tests on materials data we find precision and
+recall both close to 90% from the best conversational LLMs, like ChatGPT-4. We
+demonstrate that the exceptional performance is enabled by the information
+retention in a conversational model combined with purposeful redundancy and
+introducing uncertainty through follow-up prompts. These results suggest that
+approaches similar to ChatExtract, due to their simplicity, transferability,
+and accuracy are likely to become powerful tools for data extraction in the
+near future. Finally, databases for critical cooling rates of metallic glasses
+and yield strengths of high entropy alloys are developed using ChatExtract.
+"
+On Codex Prompt Engineering for OCL Generation: An Empirical Study,Seif Abukhalaf,http://arxiv.org/pdf/2303.16244v1.pdf,2023-03-28,"['cs.se', 'cs.ai']",2303.16244v1.pdf,"  The Object Constraint Language (OCL) is a declarative language that adds
+constraints and object query expressions to MOF models. Despite its potential
+to provide precision and conciseness to UML models, the unfamiliar syntax of
+OCL has hindered its adoption. Recent advancements in LLMs, such as GPT-3, have
+shown their capability in many NLP tasks, including semantic parsing and text
+generation. Codex, a GPT-3 descendant, has been fine-tuned on publicly
+available code from GitHub and can generate code in many programming languages.
+We investigate the reliability of OCL constraints generated by Codex from
+natural language specifications. To achieve this, we compiled a dataset of 15
+UML models and 168 specifications and crafted a prompt template with slots to
+populate with UML information and the target task, using both zero- and
+few-shot learning methods. By measuring the syntactic validity and execution
+accuracy metrics of the generated OCL constraints, we found that enriching the
+prompts with UML information and enabling few-shot learning increases the
+reliability of the generated OCL constraints. Furthermore, the results reveal a
+close similarity based on sentence embedding between the generated OCL
+constraints and the human-written ones in the ground truth, implying a level of
+clarity and understandability in the generated OCL constraints by Codex.
+"
+Ten Quick Tips for Harnessing the Power of ChatGPT/GPT-4 in  Computational Biology,Tiago Lubiana,http://arxiv.org/pdf/2303.16429v1.pdf,2023-03-29,"['q-bio.ot', '92-04']",2303.16429v1.pdf,"  The rise of advanced chatbots, such as ChatGPT, has sparked curiosity in the
+scientific community. ChatGPT is a general-purpose chatbot powered by large
+language models (LLMs) GPT-3.5 and GPT-4, with the potential to impact numerous
+fields, including computational biology. In this article, we offer ten tips
+based on our experience with ChatGPT to assist computational biologists in
+optimizing their workflows. We have collected relevant prompts and reviewed the
+nascent literature in the field, compiling tips we project to remain pertinent
+for future ChatGPT and LLM iterations, ranging from code refactoring to
+scientific writing to prompt engineering. We hope our work will help
+bioinformaticians to complement their workflows while staying aware of the
+various implications of using this technology. Additionally, to track new and
+creative applications for bioinformatics tools such as ChatGPT, we have
+established a GitHub repository at
+https://github.com/csbl-br/awesome-compbio-chatgpt. Our belief is that ethical
+adherence to ChatGPT and other LLMs will increase the efficiency of
+computational biologists, ultimately advancing the pace of scientific discovery
+in the life sciences.
+"
+Humans in Humans Out: On GPT Converging Toward Common Sense in both  Success and Failure,Philipp Koralus,http://arxiv.org/pdf/2303.17276v1.pdf,2023-03-30,"['cs.ai', 'cs.cl', 'cs.hc', 'cs.lg', '00, 68', 'i.2.0; i.2.6']",2303.17276v1.pdf,"  Increase in computational scale and fine-tuning has seen a dramatic
+improvement in the quality of outputs of large language models (LLMs) like GPT.
+Given that both GPT-3 and GPT-4 were trained on large quantities of
+human-generated text, we might ask to what extent their outputs reflect
+patterns of human thinking, both for correct and incorrect cases. The Erotetic
+Theory of Reason (ETR) provides a symbolic generative model of both human
+success and failure in thinking, across propositional, quantified, and
+probabilistic reasoning, as well as decision-making. We presented GPT-3,
+GPT-3.5, and GPT-4 with 61 central inference and judgment problems from a
+recent book-length presentation of ETR, consisting of experimentally verified
+data-points on human judgment and extrapolated data-points predicted by ETR,
+with correct inference patterns as well as fallacies and framing effects (the
+ETR61 benchmark). ETR61 includes classics like Wason's card task, illusory
+inferences, the decoy effect, and opportunity-cost neglect, among others. GPT-3
+showed evidence of ETR-predicted outputs for 59% of these examples, rising to
+77% in GPT-3.5 and 75% in GPT-4. Remarkably, the production of human-like
+fallacious judgments increased from 18% in GPT-3 to 33% in GPT-3.5 and 34% in
+GPT-4. This suggests that larger and more advanced LLMs may develop a tendency
+toward more human-like mistakes, as relevant thought patterns are inherent in
+human-produced training data. According to ETR, the same fundamental patterns
+are involved both in successful and unsuccessful ordinary reasoning, so that
+the ""bad"" cases could paradoxically be learned from the ""good"" cases. We
+further present preliminary evidence that ETR-inspired prompt engineering could
+reduce instances of these mistakes.
+"
+Pair Programming with Large Language Models for Sampling and Estimation  of Copulas,Jan Górecki,http://arxiv.org/pdf/2303.18116v1.pdf,2023-03-31,"['cs.cl', 'stat.co', '65c60, 68n19, 68t50']",2303.18116v1.pdf,"  Without writing a single line of code by a human, an example Monte Carlo
+simulation based application for stochastic dependence modeling with copulas is
+developed using a state-of-the-art large language model (LLM) fine-tuned for
+conversations. This includes interaction with ChatGPT in natural language and
+using mathematical formalism, which, under careful supervision by a
+human-expert, led to producing a working code in MATLAB, Python and R for
+sampling from a given copula model, evaluation of the model's density,
+performing maximum likelihood estimation, optimizing the code for parallel
+computing for CPUs as well as for GPUs, and visualization of the computed
+results. In contrast to other emerging studies that assess the accuracy of LLMs
+like ChatGPT on tasks from a selected area, this work rather investigates ways
+how to achieve a successful solution of a standard statistical task in a
+collaboration of a human-expert and artificial intelligence (AI). Particularly,
+through careful prompt engineering, we separate successful solutions generated
+by ChatGPT from unsuccessful ones, resulting in a comprehensive list of related
+pros and cons. It is demonstrated that if the typical pitfalls are avoided, we
+can substantially benefit from collaborating with an AI partner. For example,
+we show that if ChatGPT is not able to provide a correct solution due to a lack
+of or incorrect knowledge, the human-expert can feed it with the correct
+knowledge, e.g., in the form of mathematical theorems and formulas, and make it
+to apply the gained knowledge in order to provide a solution that is correct.
+Such ability presents an attractive opportunity to achieve a programmed
+solution even for users with rather limited knowledge of programming
+techniques.
+"
+"Unlocking the Potential of ChatGPT: A Comprehensive Exploration of its  Applications, Advantages, Limitations, and Future Directions in Natural  Language Processing",Walid Hariri,http://arxiv.org/pdf/2304.02017v5.pdf,2023-03-27,['cs.cl'],2304.02017v5.pdf,"  Large language models have revolutionized the field of artificial
+intelligence and have been used in various applications. Among these models,
+ChatGPT (Chat Generative Pre-trained Transformer) has been developed by OpenAI,
+it stands out as a powerful tool that has been widely adopted. ChatGPT has been
+successfully applied in numerous areas, including chatbots, content generation,
+language translation, personalized recommendations, and even medical diagnosis
+and treatment. Its success in these applications can be attributed to its
+ability to generate human-like responses, understand natural language, and
+adapt to different contexts. Its versatility and accuracy make it a powerful
+tool for natural language processing (NLP). However, there are also limitations
+to ChatGPT, such as its tendency to produce biased responses and its potential
+to perpetuate harmful language patterns. This article provides a comprehensive
+overview of ChatGPT, its applications, advantages, and limitations.
+Additionally, the paper emphasizes the importance of ethical considerations
+when using this robust tool in real-world scenarios. Finally, This paper
+contributes to ongoing discussions surrounding artificial intelligence and its
+impact on vision and NLP domains by providing insights into prompt engineering
+techniques.
+"
+TagGPT: Large Language Models are Zero-shot Multimodal Taggers,Chen Li,http://arxiv.org/pdf/2304.03022v1.pdf,2023-04-06,['cs.ir'],2304.03022v1.pdf,"  Tags are pivotal in facilitating the effective distribution of multimedia
+content in various applications in the contemporary Internet era, such as
+search engines and recommendation systems. Recently, large language models
+(LLMs) have demonstrated impressive capabilities across a wide range of tasks.
+In this work, we propose TagGPT, a fully automated system capable of tag
+extraction and multimodal tagging in a completely zero-shot fashion. Our core
+insight is that, through elaborate prompt engineering, LLMs are able to extract
+and reason about proper tags given textual clues of multimodal data, e.g., OCR,
+ASR, title, etc. Specifically, to automatically build a high-quality tag set
+that reflects user intent and interests for a specific application, TagGPT
+predicts large-scale candidate tags from a series of raw data via prompting
+LLMs, filtered with frequency and semantics. Given a new entity that needs
+tagging for distribution, TagGPT introduces two alternative options for
+zero-shot tagging, i.e., a generative method with late semantic matching with
+the tag set, and another selective method with early matching in prompts. It is
+well noticed that TagGPT provides a system-level solution based on a modular
+framework equipped with a pre-trained LLM (GPT-3.5 used here) and a sentence
+embedding model (SimCSE used here), which can be seamlessly replaced with any
+more advanced one you want. TagGPT is applicable for various modalities of data
+in modern social media and showcases strong generalization ability to a wide
+range of applications. We evaluate TagGPT on publicly available datasets, i.e.,
+Kuaishou and Food.com, and demonstrate the effectiveness of TagGPT compared to
+existing hashtags and off-the-shelf taggers. Project page:
+https://github.com/TencentARC/TagGPT.
+"
+Towards Interpretable Mental Health Analysis with Large Language Models,Kailai Yang,http://arxiv.org/pdf/2304.03347v4.pdf,2023-04-06,['cs.cl'],2304.03347v4.pdf,"  The latest large language models (LLMs) such as ChatGPT, exhibit strong
+capabilities in automated mental health analysis. However, existing relevant
+studies bear several limitations, including inadequate evaluations, lack of
+prompting strategies, and ignorance of exploring LLMs for explainability. To
+bridge these gaps, we comprehensively evaluate the mental health analysis and
+emotional reasoning ability of LLMs on 11 datasets across 5 tasks. We explore
+the effects of different prompting strategies with unsupervised and distantly
+supervised emotional information. Based on these prompts, we explore LLMs for
+interpretable mental health analysis by instructing them to generate
+explanations for each of their decisions. We convey strict human evaluations to
+assess the quality of the generated explanations, leading to a novel dataset
+with 163 human-assessed explanations. We benchmark existing automatic
+evaluation metrics on this dataset to guide future related works. According to
+the results, ChatGPT shows strong in-context learning ability but still has a
+significant gap with advanced task-specific methods. Careful prompt engineering
+with emotional cues and expert-written few-shot examples can also effectively
+improve performance on mental health analysis. In addition, ChatGPT generates
+explanations that approach human performance, showing its great potential in
+explainable mental health analysis.
+"
+Low-code LLM: Visual Programming over LLMs,Yuzhe Cai,http://arxiv.org/pdf/2304.08103v2.pdf,2023-04-17,"['cs.cl', 'cs.hc']",2304.08103v2.pdf,"  Effectively utilizing LLMs for complex tasks is challenging, often involving
+a time-consuming and uncontrollable prompt engineering process. This paper
+introduces a novel human-LLM interaction framework, Low-code LLM. It
+incorporates six types of simple low-code visual programming interactions, all
+supported by clicking, dragging, or text editing, to achieve more controllable
+and stable responses. Through visual interaction with a graphical user
+interface, users can incorporate their ideas into the workflow without writing
+trivial prompts. The proposed Low-code LLM framework consists of a Planning LLM
+that designs a structured planning workflow for complex tasks, which can be
+correspondingly edited and confirmed by users through low-code visual
+programming operations, and an Executing LLM that generates responses following
+the user-confirmed workflow. We highlight three advantages of the low-code LLM:
+controllable generation results, user-friendly human-LLM interaction, and
+broadly applicable scenarios. We demonstrate its benefits using four typical
+applications. By introducing this approach, we aim to bridge the gap between
+humans and LLMs, enabling more effective and efficient utilization of LLMs for
+complex tasks. Our system will be soon publicly available at LowCodeLLM.
+"
+Inducing anxiety in large language models increases exploration and bias,Julian Coda-Forno,http://arxiv.org/pdf/2304.11111v1.pdf,2023-04-21,"['cs.cl', 'cs.ai', 'cs.lg']",2304.11111v1.pdf,"  Large language models are transforming research on machine learning while
+galvanizing public debates. Understanding not only when these models work well
+and succeed but also why they fail and misbehave is of great societal
+relevance. We propose to turn the lens of computational psychiatry, a framework
+used to computationally describe and modify aberrant behavior, to the outputs
+produced by these models. We focus on the Generative Pre-Trained Transformer
+3.5 and subject it to tasks commonly studied in psychiatry. Our results show
+that GPT-3.5 responds robustly to a common anxiety questionnaire, producing
+higher anxiety scores than human subjects. Moreover, GPT-3.5's responses can be
+predictably changed by using emotion-inducing prompts. Emotion-induction not
+only influences GPT-3.5's behavior in a cognitive task measuring exploratory
+decision-making but also influences its behavior in a previously-established
+task measuring biases such as racism and ableism. Crucially, GPT-3.5 shows a
+strong increase in biases when prompted with anxiety-inducing text. Thus, it is
+likely that how prompts are communicated to large language models has a strong
+influence on their behavior in applied settings. These results progress our
+understanding of prompt engineering and demonstrate the usefulness of methods
+taken from computational psychiatry for studying the capable algorithms to
+which we increasingly delegate authority and autonomy.
+"
+Is ChatGPT the Ultimate Programming Assistant -- How far is it?,Haoye Tian,http://arxiv.org/pdf/2304.11938v2.pdf,2023-04-24,"['cs.se', 'cs.ai']",2304.11938v2.pdf,"  Recently, the ChatGPT LLM has received great attention: it can be used as a
+bot for discussing source code, prompting it to suggest changes, provide
+descriptions or even generate code. Typical demonstrations generally focus on
+existing benchmarks, which may have been used in model training (i.e., data
+leakage). To assess the feasibility of using an LLM as a useful assistant bot
+for programmers, we must assess its realistic capabilities on unseen problems
+as well as its capabilities on various tasks. In this paper, we present an
+empirical study of ChatGPT's potential as a fully automated programming
+assistant, focusing on the tasks of code generation, program repair, and code
+summariziation. The study investigates ChatGPT's performance on common
+programming problems and compares it with state-of-the-art approaches on two
+benchmarks. Among several findings, our study shows that ChatGPT is effective
+in dealing with common programming problems. However, our experiments also
+reveal limitations in terms of its attention span: detailed descriptions will
+constrain the focus of ChatGPT and prevent it from leveraging its vast
+knowledge to solve the actual problem. Surprisingly, we have identified the
+ability of ChatGPT to reason the original intention of the code. We expect
+future work to build on this insight for dealing with the open question of the
+oracle problem. Our findings contribute interesting insights to the development
+of LLMs for programming assistance, notably by demonstrating the importance of
+prompt engineering, and providing a better understanding of ChatGPT's practical
+applications for software engineering.
+"
+Framing the News:From Human Perception to Large Language Model  Inferences,David Alonso del Barrio,http://arxiv.org/pdf/2304.14456v1.pdf,2023-04-27,"['cs.cl', 'cs.hc']",2304.14456v1.pdf,"  Identifying the frames of news is important to understand the articles'
+vision, intention, message to be conveyed, and which aspects of the news are
+emphasized. Framing is a widely studied concept in journalism, and has emerged
+as a new topic in computing, with the potential to automate processes and
+facilitate the work of journalism professionals. In this paper, we study this
+issue with articles related to the Covid-19 anti-vaccine movement. First, to
+understand the perspectives used to treat this theme, we developed a protocol
+for human labeling of frames for 1786 headlines of No-Vax movement articles of
+European newspapers from 5 countries. Headlines are key units in the written
+press, and worth of analysis as many people only read headlines (or use them to
+guide their decision for further reading.) Second, considering advances in
+Natural Language Processing (NLP) with large language models, we investigated
+two approaches for frame inference of news headlines: first with a GPT-3.5
+fine-tuning approach, and second with GPT-3.5 prompt-engineering. Our work
+contributes to the study and analysis of the performance that these models have
+to facilitate journalistic tasks like classification of frames, while
+understanding whether the models are able to replicate human perception in the
+identification of these frames.
+"
+"ChatGPT Evaluation on Sentence Level Relations: A Focus on Temporal,  Causal, and Discourse Relations",Chunkit Chan,http://arxiv.org/pdf/2304.14827v2.pdf,2023-04-28,['cs.cl'],2304.14827v2.pdf,"  This paper aims to quantitatively evaluate the performance of ChatGPT, an
+interactive large language model, on inter-sentential relations such as
+temporal relations, causal relations, and discourse relations. Given ChatGPT's
+promising performance across various tasks, we conduct extensive evaluations on
+the whole test sets of 13 datasets, including temporal and causal relations,
+PDTB2.0-based and dialogue-based discourse relations, and downstream
+applications on discourse understanding. To achieve reliable results, we adopt
+three tailored prompt templates for each task, including the zero-shot prompt
+template, zero-shot prompt engineering (PE) template, and in-context learning
+(ICL) prompt template, to establish the initial baseline scores for all popular
+sentence-pair relation classification tasks for the first time. We find that
+ChatGPT exhibits strong performance in detecting and reasoning about causal
+relations, while it may not be proficient in identifying the temporal order
+between two events. It can recognize most discourse relations with existing
+explicit discourse connectives, but the implicit discourse relation still
+remains a challenging task. Meanwhile, ChatGPT performs poorly in the dialogue
+discourse parsing task that requires structural understanding in a dialogue
+before being aware of the discourse relation.
+"
+Large Language Models Can Be Used To Effectively Scale Spear Phishing  Campaigns,Julian Hazell,http://arxiv.org/pdf/2305.06972v2.pdf,2023-05-11,"['cs.cy', 'cs.ai', 'cs.cr']",2305.06972v2.pdf,"  Recent progress in artificial intelligence (AI), particularly in the domain
+of large language models (LLMs), has resulted in powerful and versatile
+dual-use systems. Indeed, cognition can be put towards a wide variety of tasks,
+some of which can result in harm. This study investigates how LLMs can be used
+for spear phishing, a form of cybercrime that involves manipulating targets
+into divulging sensitive information. I first explore LLMs' ability to assist
+with the reconnaissance and message generation stages of a successful spear
+phishing attack, where I find that advanced LLMs are capable of improving
+cybercriminals' efficiency during these stages. To explore how LLMs can be used
+to scale spear phishing campaigns, I then create unique spear phishing messages
+for over 600 British Members of Parliament using OpenAI's GPT-3.5 and GPT-4
+models. My findings reveal that these messages are not only realistic but also
+cost-effective, with each email costing only a fraction of a cent to generate.
+Next, I demonstrate how basic prompt engineering can circumvent safeguards
+installed in LLMs by the reinforcement learning from human feedback fine-tuning
+process, highlighting the need for more robust governance interventions aimed
+at preventing misuse. To address these evolving risks, I propose two potential
+solutions: structured access schemes, such as application programming
+interfaces, and LLM-based defensive systems.
+"
+Text2Cohort: Democratizing the NCI Imaging Data Commons with Natural  Language Cohort Discovery,Pranav Kulkarni,http://arxiv.org/pdf/2305.07637v2.pdf,2023-05-12,"['cs.lg', 'cs.cl', 'cs.hc', 'cs.ir']",2305.07637v2.pdf,"  The Imaging Data Commons (IDC) is a cloud-based database that provides
+researchers with open access to cancer imaging data, with the goal of
+facilitating collaboration in medical imaging research. However, querying the
+IDC database for cohort discovery and access to imaging data has a significant
+learning curve for researchers due to its complex nature. We developed
+Text2Cohort, a large language model (LLM) based toolkit to facilitate
+user-friendly and intuitive natural language cohort discovery in the IDC.
+Text2Cohorts translates user input into IDC database queries using prompt
+engineering and autocorrection and returns the query's response to the user.
+Autocorrection resolves errors in queries by passing the errors back to the
+model for interpretation and correction. We evaluate Text2Cohort on 50 natural
+language user inputs ranging from information extraction to cohort discovery.
+The resulting queries and outputs were verified by two computer scientists to
+measure Text2Cohort's accuracy and F1 score. Text2Cohort successfully generated
+queries and their responses with an 88% accuracy and F1 score of 0.94. However,
+it failed to generate queries for 6/50 (12%) user inputs due to syntax and
+semantic errors. Our results indicate that Text2Cohort succeeded at generating
+queries with correct responses, but occasionally failed due to a lack of
+understanding of the data schema. Despite these shortcomings, Text2Cohort
+demonstrates the utility of LLMs to enable researchers to discover and curate
+cohorts using data hosted on IDC with high levels of accuracy using natural
+language in a more intuitive and user-friendly way.
+"
+Sensitivity and Robustness of Large Language Models to Prompt Template  in Japanese Text Classification Tasks,Chengguang Gan,http://arxiv.org/pdf/2305.08714v2.pdf,2023-05-15,"['cs.cl', 'cs.ai']",2305.08714v2.pdf,"  Prompt engineering relevance research has seen a notable surge in recent
+years, primarily driven by advancements in pre-trained language models and
+large language models. However, a critical issue has been identified within
+this domain: the inadequate of sensitivity and robustness of these models
+towards Prompt Templates, particularly in lesser-studied languages such as
+Japanese. This paper explores this issue through a comprehensive evaluation of
+several representative Large Language Models (LLMs) and a widely-utilized
+pre-trained model(PLM). These models are scrutinized using a benchmark dataset
+in Japanese, with the aim to assess and analyze the performance of the current
+multilingual models in this context. Our experimental results reveal startling
+discrepancies. A simple modification in the sentence structure of the Prompt
+Template led to a drastic drop in the accuracy of GPT-4 from 49.21 to 25.44.
+This observation underscores the fact that even the highly performance GPT-4
+model encounters significant stability issues when dealing with diverse
+Japanese prompt templates, rendering the consistency of the model's output
+results questionable. In light of these findings, we conclude by proposing
+potential research trajectories to further enhance the development and
+performance of Large Language Models in their current stage.
+"
+Knowledge Graph Completion Models are Few-shot Learners: An Empirical  Study of Relation Labeling in E-commerce with LLMs,Jiao Chen,http://arxiv.org/pdf/2305.09858v1.pdf,2023-05-17,"['cs.ir', 'cs.ai', 'cs.cl', 'cs.lg']",2305.09858v1.pdf,"  Knowledge Graphs (KGs) play a crucial role in enhancing e-commerce system
+performance by providing structured information about entities and their
+relationships, such as complementary or substitutable relations between
+products or product types, which can be utilized in recommender systems.
+However, relation labeling in KGs remains a challenging task due to the dynamic
+nature of e-commerce domains and the associated cost of human labor. Recently,
+breakthroughs in Large Language Models (LLMs) have shown surprising results in
+numerous natural language processing tasks. In this paper, we conduct an
+empirical study of LLMs for relation labeling in e-commerce KGs, investigating
+their powerful learning capabilities in natural language and effectiveness in
+predicting relations between product types with limited labeled data. We
+evaluate various LLMs, including PaLM and GPT-3.5, on benchmark datasets,
+demonstrating their ability to achieve competitive performance compared to
+humans on relation labeling tasks using just 1 to 5 labeled examples per
+relation. Additionally, we experiment with different prompt engineering
+techniques to examine their impact on model performance. Our results show that
+LLMs significantly outperform existing KG completion models in relation
+labeling for e-commerce KGs and exhibit performance strong enough to replace
+human labeling.
+"
+VisorGPT: Learning Visual Prior via Generative Pre-Training,Jinheng Xie,http://arxiv.org/pdf/2305.13777v4.pdf,2023-05-23,['cs.cv'],2305.13777v4.pdf,"  Various stuff and things in visual data possess specific traits, which can be
+learned by deep neural networks and are implicitly represented as the visual
+prior, e.g., object location and shape, in the model. Such prior potentially
+impacts many vision tasks. For example, in conditional image synthesis, spatial
+conditions failing to adhere to the prior can result in visually inaccurate
+synthetic results. This work aims to explicitly learn the visual prior and
+enable the customization of sampling. Inspired by advances in language
+modeling, we propose to learn Visual prior via Generative Pre-Training, dubbed
+VisorGPT. By discretizing visual locations of objects, e.g., bounding boxes,
+human pose, and instance masks, into sequences, VisorGPT can model visual prior
+through likelihood maximization. Besides, prompt engineering is investigated to
+unify various visual locations and enable customized sampling of sequential
+outputs from the learned prior. Experimental results demonstrate that VisorGPT
+can effectively model the visual prior, which can be employed for many vision
+tasks, such as customizing accurate human pose for conditional image synthesis
+models like ControlNet. Code will be released at
+https://github.com/Sierkinhane/VisorGPT.
+"
+Game of Tones: Faculty detection of GPT-4 generated content in  university assessments,Mike Perkins,http://arxiv.org/pdf/2305.18081v1.pdf,2023-05-29,"['cs.cy', 'cs.ai', 'k.4']",2305.18081v1.pdf,"  This study explores the robustness of university assessments against the use
+of Open AI's Generative Pre-Trained Transformer 4 (GPT-4) generated content and
+evaluates the ability of academic staff to detect its use when supported by the
+Turnitin Artificial Intelligence (AI) detection tool. The research involved
+twenty-two GPT-4 generated submissions being created and included in the
+assessment process to be marked by fifteen different faculty members. The study
+reveals that although the detection tool identified 91% of the experimental
+submissions as containing some AI-generated content, the total detected content
+was only 54.8%. This suggests that the use of adversarial techniques regarding
+prompt engineering is an effective method in evading AI detection tools and
+highlights that improvements to AI detection software are needed. Using the
+Turnitin AI detect tool, faculty reported 54.5% of the experimental submissions
+to the academic misconduct process, suggesting the need for increased awareness
+and training into these tools. Genuine submissions received a mean score of
+54.4, whereas AI-generated content scored 52.3, indicating the comparable
+performance of GPT-4 in real-life situations. Recommendations include adjusting
+assessment strategies to make them more resistant to the use of AI tools, using
+AI-inclusive assessment where possible, and providing comprehensive training
+programs for faculty and students. This research contributes to understanding
+the relationship between AI-generated content and academic assessment, urging
+further investigation to preserve academic integrity.
+"
+Responsible Task Automation: Empowering Large Language Models as  Responsible Task Automators,Zhizheng Zhang,http://arxiv.org/pdf/2306.01242v1.pdf,2023-06-02,"['cs.ai', 'cs.cl']",2306.01242v1.pdf,"  The recent success of Large Language Models (LLMs) signifies an impressive
+stride towards artificial general intelligence. They have shown a promising
+prospect in automatically completing tasks upon user instructions, functioning
+as brain-like coordinators. The associated risks will be revealed as we
+delegate an increasing number of tasks to machines for automated completion. A
+big question emerges: how can we make machines behave responsibly when helping
+humans automate tasks as personal copilots? In this paper, we explore this
+question in depth from the perspectives of feasibility, completeness and
+security. In specific, we present Responsible Task Automation (ResponsibleTA)
+as a fundamental framework to facilitate responsible collaboration between
+LLM-based coordinators and executors for task automation with three empowered
+capabilities: 1) predicting the feasibility of the commands for executors; 2)
+verifying the completeness of executors; 3) enhancing the security (e.g., the
+protection of users' privacy). We further propose and compare two paradigms for
+implementing the first two capabilities. One is to leverage the generic
+knowledge of LLMs themselves via prompt engineering while the other is to adopt
+domain-specific learnable models. Moreover, we introduce a local memory
+mechanism for achieving the third capability. We evaluate our proposed
+ResponsibleTA on UI task automation and hope it could bring more attentions to
+ensuring LLMs more responsible in diverse scenarios. The research project
+homepage is at
+https://task-automation-research.github.io/responsible_task_automation.
+"
+A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets  Prompt Engineering,Chaoning Zhang,http://arxiv.org/pdf/2306.06211v3.pdf,2023-05-12,['cs.cv'],2306.06211v3.pdf,"  Segment anything model (SAM) developed by Meta AI Research has recently
+attracted significant attention. Trained on a large segmentation dataset of
+over 1 billion masks, SAM is capable of segmenting any object on a certain
+image. In the original SAM work, the authors turned to zero-short transfer
+tasks (like edge detection) for evaluating the performance of SAM. Recently,
+numerous works have attempted to investigate the performance of SAM in various
+scenarios to recognize and segment objects. Moreover, numerous projects have
+emerged to show the versatility of SAM as a foundation model by combining it
+with other models, like Grounding DINO, Stable Diffusion, ChatGPT, etc. With
+the relevant papers and projects increasing exponentially, it is challenging
+for the readers to catch up with the development of SAM. To this end, this work
+conducts the first yet comprehensive survey on SAM. This is an ongoing project
+and we intend to update the manuscript on a regular basis. Therefore, readers
+are welcome to contact us if they complete new works related to SAM so that we
+can include them in our next version.
+"
+The economic trade-offs of large language models: A case study,Kristen Howell,http://arxiv.org/pdf/2306.07402v1.pdf,2023-06-08,"['cs.cl', 'cs.ai']",2306.07402v1.pdf,"  Contacting customer service via chat is a common practice. Because employing
+customer service agents is expensive, many companies are turning to NLP that
+assists human agents by auto-generating responses that can be used directly or
+with modifications. Large Language Models (LLMs) are a natural fit for this use
+case; however, their efficacy must be balanced with the cost of training and
+serving them. This paper assesses the practical cost and impact of LLMs for the
+enterprise as a function of the usefulness of the responses that they generate.
+We present a cost framework for evaluating an NLP model's utility for this use
+case and apply it to a single brand as a case study in the context of an
+existing agent assistance product. We compare three strategies for specializing
+an LLM - prompt engineering, fine-tuning, and knowledge distillation - using
+feedback from the brand's customer service agents. We find that the usability
+of a model's responses can make up for a large difference in inference cost for
+our case study brand, and we extrapolate our findings to the broader enterprise
+space.
+"
+TART: A plug-and-play Transformer module for task-agnostic reasoning,Kush Bhatia,http://arxiv.org/pdf/2306.07536v1.pdf,2023-06-13,"['cs.lg', 'cs.ai', 'cs.cl']",2306.07536v1.pdf,"  Large language models (LLMs) exhibit in-context learning abilities which
+enable the same model to perform several tasks without any task-specific
+training. In contrast, traditional adaptation approaches, such as fine-tuning,
+modify the underlying models for each specific task. In-context learning,
+however, consistently underperforms task-specific tuning approaches even when
+presented with the same examples. While most existing approaches (e.g., prompt
+engineering) focus on the LLM's learned representations to patch this
+performance gap, our analysis actually reveal that LLM representations contain
+sufficient information to make good predictions. As such, we focus on the LLM's
+reasoning abilities and demonstrate that this performance gap exists due to
+their inability to perform simple probabilistic reasoning tasks. This raises an
+intriguing question: Are LLMs actually capable of learning how to reason in a
+task-agnostic manner? We answer this in the affirmative and propose TART which
+generically improves an LLM's reasoning abilities using a synthetically trained
+Transformer-based reasoning module. TART trains this reasoning module in a
+task-agnostic manner using only synthetic logistic regression tasks and
+composes it with an arbitrary real-world pre-trained model without any
+additional training. With a single inference module, TART improves performance
+across different model families (GPT-Neo, Pythia, BLOOM), model sizes (100M -
+6B), tasks (14 NLP binary classification tasks), and even across different
+modalities (audio and vision). Additionally, on the RAFT Benchmark, TART
+improves GPT-Neo (125M)'s performance such that it outperforms BLOOM (176B),
+and is within 4% of GPT-3 (175B). Our code and models are available at
+https://github.com/HazyResearch/TART .
+"
+Exploring the Effectiveness of Dataset Synthesis: An application of  Apple Detection in Orchards,Alexander van Meekeren,http://arxiv.org/pdf/2306.11763v1.pdf,2023-06-20,['cs.cv'],2306.11763v1.pdf,"  Deep object detection models have achieved notable successes in recent years,
+but one major obstacle remains: the requirement for a large amount of training
+data. Obtaining such data is a tedious process and is mainly time consuming,
+leading to the exploration of new research avenues like synthetic data
+generation techniques. In this study, we explore the usability of Stable
+Diffusion 2.1-base for generating synthetic datasets of apple trees for object
+detection and compare it to a baseline model trained on real-world data. After
+creating a dataset of realistic apple trees with prompt engineering and
+utilizing a previously trained Stable Diffusion model, the custom dataset was
+annotated and evaluated by training a YOLOv5m object detection model to predict
+apples in a real-world apple detection dataset. YOLOv5m was chosen for its
+rapid inference time and minimal hardware demands. Results demonstrate that the
+model trained on generated data is slightly underperforming compared to a
+baseline model trained on real-world images when evaluated on a set of
+real-world images. However, these findings remain highly promising, as the
+average precision difference is only 0.09 and 0.06, respectively. Qualitative
+results indicate that the model can accurately predict the location of apples,
+except in cases of heavy shading. These findings illustrate the potential of
+synthetic data generation techniques as a viable alternative to the collection
+of extensive training data for object detection models.
+"
+Do you still need a manual smart contract audit?,Isaac David,http://arxiv.org/pdf/2306.12338v2.pdf,2023-06-21,['cs.cr'],2306.12338v2.pdf,"  We investigate the feasibility of employing large language models (LLMs) for
+conducting the security audit of smart contracts, a traditionally
+time-consuming and costly process. Our research focuses on the optimization of
+prompt engineering for enhanced security analysis, and we evaluate the
+performance and accuracy of LLMs using a benchmark dataset comprising 52
+Decentralized Finance (DeFi) smart contracts that have previously been
+compromised.
+  Our findings reveal that, when applied to vulnerable contracts, both GPT-4
+and Claude models correctly identify the vulnerability type in 40% of the
+cases. However, these models also demonstrate a high false positive rate,
+necessitating continued involvement from manual auditors. The LLMs tested
+outperform a random model by 20% in terms of F1-score.
+  To ensure the integrity of our study, we conduct mutation testing on five
+newly developed and ostensibly secure smart contracts, into which we manually
+insert two and 15 vulnerabilities each. This testing yielded a remarkable
+best-case 78.7% true positive rate for the GPT-4-32k model. We tested both,
+asking the models to perform a binary classification on whether a contract is
+vulnerable, and a non-binary prompt. We also examined the influence of model
+temperature variations and context length on the LLM's performance.
+  Despite the potential for many further enhancements, this work lays the
+groundwork for a more efficient and economical approach to smart contract
+security audits.
+"
+MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language  Models,Chaoyou Fu,http://arxiv.org/pdf/2306.13394v2.pdf,2023-06-23,['cs.cv'],2306.13394v2.pdf,"  Multimodal Large Language Model (MLLM) relies on the powerful LLM to perform
+multimodal tasks, showing amazing emergent abilities in recent studies, such as
+writing poems based on an image. However, it is difficult for these case
+studies to fully reflect the performance of MLLM, lacking a comprehensive
+evaluation. In this paper, we fill in this blank, presenting the first MLLM
+Evaluation benchmark MME. It measures both perception and cognition abilities
+on a total of 14 subtasks. In order to avoid data leakage that may arise from
+direct use of public datasets for evaluation, the annotations of
+instruction-answer pairs are all manually designed. The concise instruction
+design allows us to fairly compare MLLMs, instead of struggling in prompt
+engineering. Besides, with such an instruction, we can also easily carry out
+quantitative statistics. A total of 12 advanced MLLMs are comprehensively
+evaluated on our MME, which not only suggests that existing MLLMs still have a
+large room for improvement, but also reveals the potential directions for the
+subsequent model optimization.
+"
+Zero-shot Nuclei Detection via Visual-Language Pre-trained Models,Yongjian Wu,http://arxiv.org/pdf/2306.17659v1.pdf,2023-06-30,['cs.cv'],2306.17659v1.pdf,"  Large-scale visual-language pre-trained models (VLPM) have proven their
+excellent performance in downstream object detection for natural scenes.
+However, zero-shot nuclei detection on H\&E images via VLPMs remains
+underexplored. The large gap between medical images and the web-originated
+text-image pairs used for pre-training makes it a challenging task. In this
+paper, we attempt to explore the potential of the object-level VLPM, Grounded
+Language-Image Pre-training (GLIP) model, for zero-shot nuclei detection.
+Concretely, an automatic prompts design pipeline is devised based on the
+association binding trait of VLPM and the image-to-text VLPM BLIP, avoiding
+empirical manual prompts engineering. We further establish a self-training
+framework, using the automatically designed prompts to generate the preliminary
+results as pseudo labels from GLIP and refine the predicted boxes in an
+iterative manner. Our method achieves a remarkable performance for label-free
+nuclei detection, surpassing other comparison methods. Foremost, our work
+demonstrates that the VLPM pre-trained on natural image-text pairs exhibits
+astonishing potential for downstream tasks in the medical field as well. Code
+will be released at https://github.com/wuyongjianCODE/VLPMNuD.
+"
+Comparative Analysis of GPT-4 and Human Graders in Evaluating Praise  Given to Students in Synthetic Dialogues,Dollaya Hirunyasiri,http://arxiv.org/pdf/2307.02018v1.pdf,2023-07-05,"['cs.cl', 'cs.ai', 'cs.hc']",2307.02018v1.pdf,"  Research suggests that providing specific and timely feedback to human tutors
+enhances their performance. However, it presents challenges due to the
+time-consuming nature of assessing tutor performance by human evaluators. Large
+language models, such as the AI-chatbot ChatGPT, hold potential for offering
+constructive feedback to tutors in practical settings. Nevertheless, the
+accuracy of AI-generated feedback remains uncertain, with scant research
+investigating the ability of models like ChatGPT to deliver effective feedback.
+In this work-in-progress, we evaluate 30 dialogues generated by GPT-4 in a
+tutor-student setting. We use two different prompting approaches, the zero-shot
+chain of thought and the few-shot chain of thought, to identify specific
+components of effective praise based on five criteria. These approaches are
+then compared to the results of human graders for accuracy. Our goal is to
+assess the extent to which GPT-4 can accurately identify each praise criterion.
+We found that both zero-shot and few-shot chain of thought approaches yield
+comparable results. GPT-4 performs moderately well in identifying instances
+when the tutor offers specific and immediate praise. However, GPT-4
+underperforms in identifying the tutor's ability to deliver sincere praise,
+particularly in the zero-shot prompting scenario where examples of sincere
+tutor praise statements were not provided. Future work will focus on enhancing
+prompt engineering, developing a more general tutoring rubric, and evaluating
+our method using real-life tutoring dialogues.
+"
+"Right to be Forgotten in the Era of Large Language Models: Implications,  Challenges, and Solutions",Dawen Zhang,http://arxiv.org/pdf/2307.03941v3.pdf,2023-07-08,"['cs.cy', 'cs.ai', 'cs.cl']",2307.03941v3.pdf,"  The Right to be Forgotten (RTBF) was first established as the result of the
+ruling of Google Spain SL, Google Inc. v AEPD, Mario Costeja Gonz\'alez, and
+was later included as the Right to Erasure under the General Data Protection
+Regulation (GDPR) of European Union to allow individuals the right to request
+personal data be deleted by organizations. Specifically for search engines,
+individuals can send requests to organizations to exclude their information
+from the query results. It was a significant emergent right as the result of
+the evolution of technology. With the recent development of Large Language
+Models (LLMs) and their use in chatbots, LLM-enabled software systems have
+become popular. But they are not excluded from the RTBF. Compared with the
+indexing approach used by search engines, LLMs store, and process information
+in a completely different way. This poses new challenges for compliance with
+the RTBF. In this paper, we explore these challenges and provide our insights
+on how to implement technical solutions for the RTBF, including the use of
+differential privacy, machine unlearning, model editing, and prompt
+engineering. With the rapid advancement of AI and the increasing need of
+regulating this powerful technology, learning from the case of RTBF can provide
+valuable lessons for technical practitioners, legal experts, organizations, and
+authorities.
+"
+"Software Testing with Large Language Model: Survey, Landscape, and  Vision",Junjie Wang,http://arxiv.org/pdf/2307.07221v1.pdf,2023-07-14,['cs.se'],2307.07221v1.pdf,"  Pre-trained large language models (LLMs) have recently emerged as a
+breakthrough technology in natural language processing and artificial
+intelligence, with the ability to handle large-scale datasets and exhibit
+remarkable performance across a wide range of tasks. Meanwhile, software
+testing is a crucial undertaking that serves as a cornerstone for ensuring the
+quality and reliability of software products. As the scope and complexity of
+software systems continue to grow, the need for more effective software testing
+techniques becomes increasingly urgent, and making it an area ripe for
+innovative approaches such as the use of LLMs. This paper provides a
+comprehensive review of the utilization of LLMs in software testing. It
+analyzes 52 relevant studies that have used LLMs for software testing, from
+both the software testing and LLMs perspectives. The paper presents a detailed
+discussion of the software testing tasks for which LLMs are commonly used,
+among which test case preparation and program repair are the most
+representative ones. It also analyzes the commonly used LLMs, the types of
+prompt engineering that are employed, as well as the accompanied techniques
+with these LLMs. It also summarizes the key challenges and potential
+opportunities in this direction. This work can serve as a roadmap for future
+research in this area, highlighting potential avenues for exploration, and
+identifying gaps in our current understanding of the use of LLMs in software
+testing.
+"
+The Potential and Pitfalls of using a Large Language Model such as  ChatGPT or GPT-4 as a Clinical Assistant,Jingqing Zhang,http://arxiv.org/pdf/2307.08152v1.pdf,2023-07-16,['cs.cl'],2307.08152v1.pdf,"  Recent studies have demonstrated promising performance of ChatGPT and GPT-4
+on several medical domain tasks. However, none have assessed its performance
+using a large-scale real-world electronic health record database, nor have
+evaluated its utility in providing clinical diagnostic assistance for patients
+across a full range of disease presentation. We performed two analyses using
+ChatGPT and GPT-4, one to identify patients with specific medical diagnoses
+using a real-world large electronic health record database and the other, in
+providing diagnostic assistance to healthcare workers in the prospective
+evaluation of hypothetical patients. Our results show that GPT-4 across disease
+classification tasks with chain of thought and few-shot prompting can achieve
+performance as high as 96% F1 scores. For patient assessment, GPT-4 can
+accurately diagnose three out of four times. However, there were mentions of
+factually incorrect statements, overlooking crucial medical findings,
+recommendations for unnecessary investigations and overtreatment. These issues
+coupled with privacy concerns, make these models currently inadequate for real
+world clinical use. However, limited data and time needed for prompt
+engineering in comparison to configuration of conventional machine learning
+workflows highlight their potential for scalability across healthcare
+applications.
+"
+A Lightweight Framework for High-Quality Code Generation,Mohammed Latif Siddiq,http://arxiv.org/pdf/2307.08220v1.pdf,2023-07-17,"['cs.se', 'cs.lg']",2307.08220v1.pdf,"  In recent years, the use of automated source code generation utilizing
+transformer-based generative models has expanded, and these models can generate
+functional code according to the requirements of the developers. However,
+recent research revealed that these automatically generated source codes can
+contain vulnerabilities and other quality issues. Despite researchers' and
+practitioners' attempts to enhance code generation models, retraining and
+fine-tuning large language models is time-consuming and resource-intensive.
+Thus, we describe FRANC, a lightweight framework for recommending more secure
+and high-quality source code derived from transformer-based code generation
+models. FRANC includes a static filter to make the generated code compilable
+with heuristics and a quality-aware ranker to sort the code snippets based on a
+quality score. Moreover, the framework uses prompt engineering to fix
+persistent quality issues. We evaluated the framework with five Python and Java
+code generation models and six prompt datasets, including a newly created one
+in this work (SOEval). The static filter improves 9% to 46% Java suggestions
+and 10% to 43% Python suggestions regarding compilability. The average
+improvement over the NDCG@10 score for the ranking system is 0.0763, and the
+repairing techniques repair the highest 80% of prompts. FRANC takes, on
+average, 1.98 seconds for Java; for Python, it takes 0.08 seconds.
+"
+"Multi-Method Self-Training: Improving Code Generation With Text, And  Vice Versa",Shriyash K. Upadhyay,http://arxiv.org/pdf/2307.10633v1.pdf,2023-07-20,"['cs.cl', 'cs.lg']",2307.10633v1.pdf,"  Large Language Models have many methods for solving the same problem. This
+introduces novel strengths (different methods may work well for different
+problems) and weaknesses (it may be difficult for users to know which method to
+use). In this paper, we introduce Multi-Method Self-Training (MMST), where one
+method is trained on the filtered outputs of another, allowing us to augment
+the strengths and ameliorate the weaknesses of each method. Using a 176B
+parameter model trained on both language and code, we show that MMST can 1)
+improve the less performant method (up to 30%) making the model easier to use,
+2) improve the more performant method (up to 32.2%) making the model more
+performant, and 3) improve the performance of related but distinct tasks (up to
+10.3%) by improving the ability of the model to generate rationales. We then
+conduct ablation analyses to explore why MMST works. We show that MMST
+generates more data than traditional self-training, but the improvement in
+performance is driven by the use of multiple methods. We also analyze
+prompt-engineering and anti-correlated performance between methods as means of
+making MMST more effective. We hope the evidence from our paper motivates
+machine learning researchers to explore ways in which advances in language
+models allow for new forms of training.
+"
+Enhancing CLIP with GPT-4: Harnessing Visual Descriptions as Prompts,Mayug Maniparambil,http://arxiv.org/pdf/2307.11661v2.pdf,2023-07-21,"['cs.cv', 'cs.ai', 'cs.cl', 'cs.lg']",2307.11661v2.pdf,"  Contrastive pretrained large Vision-Language Models (VLMs) like CLIP have
+revolutionized visual representation learning by providing good performance on
+downstream datasets. VLMs are 0-shot adapted to a downstream dataset by
+designing prompts that are relevant to the dataset. Such prompt engineering
+makes use of domain expertise and a validation dataset. Meanwhile, recent
+developments in generative pretrained models like GPT-4 mean they can be used
+as advanced internet search tools. They can also be manipulated to provide
+visual information in any structure. In this work, we show that GPT-4 can be
+used to generate text that is visually descriptive and how this can be used to
+adapt CLIP to downstream tasks. We show considerable improvements in 0-shot
+transfer accuracy on specialized fine-grained datasets like EuroSAT (~7%), DTD
+(~7%), SUN397 (~4.6%), and CUB (~3.3%) when compared to CLIP's default prompt.
+We also design a simple few-shot adapter that learns to choose the best
+possible sentences to construct generalizable classifiers that outperform the
+recently proposed CoCoOP by ~2% on average and by over 4% on 4 specialized
+fine-grained datasets. The code, prompts, and auxiliary text dataset is
+available at https://github.com/mayug/VDT-Adapter.
+"
+GPT-3 Models are Few-Shot Financial Reasoners,Raul Salles de Padua,http://arxiv.org/pdf/2307.13617v2.pdf,2023-07-25,"['cs.cl', 'cs.ai']",2307.13617v2.pdf,"  Financial analysis is an important tool for evaluating company performance.
+Practitioners work to answer financial questions to make profitable investment
+decisions, and use advanced quantitative analyses to do so. As a result,
+Financial Question Answering (QA) is a question answering task that requires
+deep reasoning about numbers. Furthermore, it is unknown how well pre-trained
+language models can reason in the financial domain. The current
+state-of-the-art requires a retriever to collect relevant facts about the
+financial question from the text and a generator to produce a valid financial
+program and a final answer. However, recently large language models like GPT-3
+have achieved state-of-the-art performance on wide variety of tasks with just a
+few shot examples. We run several experiments with GPT-3 and find that a
+separate retrieval model and logic engine continue to be essential components
+to achieving SOTA performance in this task, particularly due to the precise
+nature of financial questions and the complex information stored in financial
+documents. With this understanding, our refined prompt-engineering approach on
+GPT-3 achieves near SOTA accuracy without any fine-tuning.
+"
+S3: Social-network Simulation System with Large Language Model-Empowered  Agents,Chen Gao,http://arxiv.org/pdf/2307.14984v2.pdf,2023-07-27,['cs.si'],2307.14984v2.pdf,"  Social network simulation plays a crucial role in addressing various
+challenges within social science. It offers extensive applications such as
+state prediction, phenomena explanation, and policy-making support, among
+others. In this work, we harness the formidable human-like capabilities
+exhibited by large language models (LLMs) in sensing, reasoning, and behaving,
+and utilize these qualities to construct the S$^3$ system (short for
+$\textbf{S}$ocial network $\textbf{S}$imulation $\textbf{S}$ystem). Adhering to
+the widely employed agent-based simulation paradigm, we employ prompt
+engineering and prompt tuning techniques to ensure that the agent's behavior
+closely emulates that of a genuine human within the social network.
+Specifically, we simulate three pivotal aspects: emotion, attitude, and
+interaction behaviors. By endowing the agent in the system with the ability to
+perceive the informational environment and emulate human actions, we observe
+the emergence of population-level phenomena, including the propagation of
+information, attitudes, and emotions. We conduct an evaluation encompassing two
+levels of simulation, employing real-world social network data. Encouragingly,
+the results demonstrate promising accuracy. This work represents an initial
+step in the realm of social network simulation empowered by LLM-based agents.
+We anticipate that our endeavors will serve as a source of inspiration for the
+development of simulation systems within, but not limited to, social science.
+"
+Flows: Building Blocks of Reasoning and Collaborating AI,Martin Josifoski,http://arxiv.org/pdf/2308.01285v1.pdf,2023-08-02,"['cs.ai', 'cs.hc']",2308.01285v1.pdf,"  Recent advances in artificial intelligence (AI) have produced highly capable
+and controllable systems. This creates unprecedented opportunities for
+structured reasoning as well as collaboration among multiple AI systems and
+humans. To fully realize this potential, it is essential to develop a
+principled way of designing and studying such structured interactions. For this
+purpose, we introduce the conceptual framework of Flows: a systematic approach
+to modeling complex interactions. Flows are self-contained building blocks of
+computation, with an isolated state, communicating through a standardized
+message-based interface. This modular design allows Flows to be recursively
+composed into arbitrarily nested interactions, with a substantial reduction of
+complexity. Crucially, any interaction can be implemented using this framework,
+including prior work on AI--AI and human--AI interactions, prompt engineering
+schemes, and tool augmentation. We demonstrate the potential of Flows on the
+task of competitive coding, a challenging task on which even GPT-4 struggles.
+Our results suggest that structured reasoning and collaboration substantially
+improve generalization, with AI-only Flows adding +$21$ and human--AI Flows
+adding +$54$ absolute points in terms of solve rate. To support rapid and
+rigorous research, we introduce the aiFlows library. The library comes with a
+repository of Flows that can be easily used, extended, and composed into novel,
+more complex Flows.
+  The aiFlows library is available at https://github.com/epfl-dlab/aiflows.
+Data and Flows for reproducing our experiments are available at
+https://github.com/epfl-dlab/cc_flows.
+"
+Evaluating ChatGPT text-mining of clinical records for obesity  monitoring,Ivo S. Fins,http://arxiv.org/pdf/2308.01666v1.pdf,2023-08-03,"['cs.ir', 'cs.cl']",2308.01666v1.pdf,"  Background: Veterinary clinical narratives remain a largely untapped resource
+for addressing complex diseases. Here we compare the ability of a large
+language model (ChatGPT) and a previously developed regular expression (RegexT)
+to identify overweight body condition scores (BCS) in veterinary narratives.
+Methods: BCS values were extracted from 4,415 anonymised clinical narratives
+using either RegexT or by appending the narrative to a prompt sent to ChatGPT
+coercing the model to return the BCS information. Data were manually reviewed
+for comparison. Results: The precision of RegexT was higher (100%, 95% CI
+94.81-100%) than the ChatGPT (89.3%; 95% CI82.75-93.64%). However, the recall
+of ChatGPT (100%. 95% CI 96.18-100%) was considerably higher than that of
+RegexT (72.6%, 95% CI 63.92-79.94%). Limitations: Subtle prompt engineering is
+needed to improve ChatGPT output. Conclusions: Large language models create
+diverse opportunities and, whilst complex, present an intuitive interface to
+information but require careful implementation to avoid unpredictable errors.
+"
+ParaFuzz: An Interpretability-Driven Technique for Detecting Poisoned  Samples in NLP,Lu Yan,http://arxiv.org/pdf/2308.02122v2.pdf,2023-08-04,"['cs.cr', 'cs.cl']",2308.02122v2.pdf,"  Backdoor attacks have emerged as a prominent threat to natural language
+processing (NLP) models, where the presence of specific triggers in the input
+can lead poisoned models to misclassify these inputs to predetermined target
+classes. Current detection mechanisms are limited by their inability to address
+more covert backdoor strategies, such as style-based attacks. In this work, we
+propose an innovative test-time poisoned sample detection framework that hinges
+on the interpretability of model predictions, grounded in the semantic meaning
+of inputs. We contend that triggers (e.g., infrequent words) are not supposed
+to fundamentally alter the underlying semantic meanings of poisoned samples as
+they want to stay stealthy. Based on this observation, we hypothesize that
+while the model's predictions for paraphrased clean samples should remain
+stable, predictions for poisoned samples should revert to their true labels
+upon the mutations applied to triggers during the paraphrasing process. We
+employ ChatGPT, a state-of-the-art large language model, as our paraphraser and
+formulate the trigger-removal task as a prompt engineering problem. We adopt
+fuzzing, a technique commonly used for unearthing software vulnerabilities, to
+discover optimal paraphrase prompts that can effectively eliminate triggers
+while concurrently maintaining input semantics. Experiments on 4 types of
+backdoor attacks, including the subtle style backdoors, and 4 distinct datasets
+demonstrate that our approach surpasses baseline methods, including STRIP, RAP,
+and ONION, in precision and recall.
+"
+IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image  Diffusion Models,Hu Ye,http://arxiv.org/pdf/2308.06721v1.pdf,2023-08-13,"['cs.cv', 'cs.ai']",2308.06721v1.pdf,"  Recent years have witnessed the strong power of large text-to-image diffusion
+models for the impressive generative capability to create high-fidelity images.
+However, it is very tricky to generate desired images using only text prompt as
+it often involves complex prompt engineering. An alternative to text prompt is
+image prompt, as the saying goes: ""an image is worth a thousand words"".
+Although existing methods of direct fine-tuning from pretrained models are
+effective, they require large computing resources and are not compatible with
+other base models, text prompt, and structural controls. In this paper, we
+present IP-Adapter, an effective and lightweight adapter to achieve image
+prompt capability for the pretrained text-to-image diffusion models. The key
+design of our IP-Adapter is decoupled cross-attention mechanism that separates
+cross-attention layers for text features and image features. Despite the
+simplicity of our method, an IP-Adapter with only 22M parameters can achieve
+comparable or even better performance to a fully fine-tuned image prompt model.
+As we freeze the pretrained diffusion model, the proposed IP-Adapter can be
+generalized not only to other custom models fine-tuned from the same base
+model, but also to controllable generation using existing controllable tools.
+With the benefit of the decoupled cross-attention strategy, the image prompt
+can also work well with the text prompt to achieve multimodal image generation.
+The project page is available at \url{https://ip-adapter.github.io}.
+"
+LogPrompt: Prompt Engineering Towards Zero-Shot and Interpretable Log  Analysis,Yilun Liu,http://arxiv.org/pdf/2308.07610v1.pdf,2023-08-15,"['cs.se', 'cs.cl']",2308.07610v1.pdf,"  Automated log analysis is crucial in modern software-intensive systems for
+ensuring reliability and resilience throughout software maintenance and
+engineering life cycles. Existing methods perform tasks such as log parsing and
+log anomaly detection by providing a single prediction value without
+interpretation. However, given the increasing volume of system events, the
+limited interpretability of analysis results hinders analysts' trust and their
+ability to take appropriate actions. Moreover, these methods require
+substantial in-domain training data, and their performance declines sharply (by
+up to 62.5%) in online scenarios involving unseen logs from new domains, a
+common occurrence due to rapid software updates. In this paper, we propose
+LogPrompt, a novel zero-shot and interpretable log analysis approach. LogPrompt
+employs large language models (LLMs) to perform zero-shot log analysis tasks
+via a suite of advanced prompt strategies tailored for log tasks, which
+enhances LLMs' performance by up to 107.5% compared with simple prompts.
+Experiments on nine publicly available evaluation datasets across two tasks
+demonstrate that LogPrompt, despite using no training data, outperforms
+existing approaches trained on thousands of logs by up to around 50%. We also
+conduct a human evaluation of LogPrompt's interpretability, with six
+practitioners possessing over 10 years of experience, who highly rated the
+generated content in terms of usefulness and readability (averagely 4.42/5).
+LogPrompt also exhibits remarkable compatibility with open-source and
+smaller-scale LLMs, making it flexible for practical deployment.
+"
+Transforming Sentiment Analysis in the Financial Domain with ChatGPT,Georgios Fatouros,http://arxiv.org/pdf/2308.07935v1.pdf,2023-08-13,"['cs.cl', 'cs.ai', 'cs.ce', 'cs.ir', '68t01, 68t50, 91b28, 91b30']",2308.07935v1.pdf,"  Financial sentiment analysis plays a crucial role in decoding market trends
+and guiding strategic trading decisions. Despite the deployment of advanced
+deep learning techniques and language models to refine sentiment analysis in
+finance, this study breaks new ground by investigating the potential of large
+language models, particularly ChatGPT 3.5, in financial sentiment analysis,
+with a strong emphasis on the foreign exchange market (forex). Employing a
+zero-shot prompting approach, we examine multiple ChatGPT prompts on a
+meticulously curated dataset of forex-related news headlines, measuring
+performance using metrics such as precision, recall, f1-score, and Mean
+Absolute Error (MAE) of the sentiment class. Additionally, we probe the
+correlation between predicted sentiment and market returns as an additional
+evaluation approach. ChatGPT, compared to FinBERT, a well-established sentiment
+analysis model for financial texts, exhibited approximately 35\% enhanced
+performance in sentiment classification and a 36\% higher correlation with
+market returns. By underlining the significance of prompt engineering,
+particularly in zero-shot contexts, this study spotlights ChatGPT's potential
+to substantially boost sentiment analysis in financial applications. By sharing
+the utilized dataset, our intention is to stimulate further research and
+advancements in the field of financial services.
+"
+ChatGPT-HealthPrompt. Harnessing the Power of XAI in Prompt-Based  Healthcare Decision Support using ChatGPT,Fatemeh Nazary,http://arxiv.org/pdf/2308.09731v1.pdf,2023-08-17,"['cs.ai', 'cs.cl', 'cs.lg']",2308.09731v1.pdf,"  This study presents an innovative approach to the application of large
+language models (LLMs) in clinical decision-making, focusing on OpenAI's
+ChatGPT. Our approach introduces the use of contextual prompts-strategically
+designed to include task description, feature description, and crucially,
+integration of domain knowledge-for high-quality binary classification tasks
+even in data-scarce scenarios. The novelty of our work lies in the utilization
+of domain knowledge, obtained from high-performing interpretable ML models, and
+its seamless incorporation into prompt design. By viewing these ML models as
+medical experts, we extract key insights on feature importance to aid in
+decision-making processes. This interplay of domain knowledge and AI holds
+significant promise in creating a more insightful diagnostic tool.
+  Additionally, our research explores the dynamics of zero-shot and few-shot
+prompt learning based on LLMs. By comparing the performance of OpenAI's ChatGPT
+with traditional supervised ML models in different data conditions, we aim to
+provide insights into the effectiveness of prompt engineering strategies under
+varied data availability. In essence, this paper bridges the gap between AI and
+healthcare, proposing a novel methodology for LLMs application in clinical
+decision support systems. It highlights the transformative potential of
+effective prompt design, domain knowledge integration, and flexible learning
+approaches in enhancing automated decision-making.
+"
+Synergistic Integration of Large Language Models and Cognitive  Architectures for Robust AI: An Exploratory Analysis,Oscar J. Romero,http://arxiv.org/pdf/2308.09830v3.pdf,2023-08-18,['cs.ai'],2308.09830v3.pdf,"  This paper explores the integration of two AI subdisciplines employed in the
+development of artificial agents that exhibit intelligent behavior: Large
+Language Models (LLMs) and Cognitive Architectures (CAs). We present three
+integration approaches, each grounded in theoretical models and supported by
+preliminary empirical evidence. The modular approach, which introduces four
+models with varying degrees of integration, makes use of chain-of-thought
+prompting, and draws inspiration from augmented LLMs, the Common Model of
+Cognition, and the simulation theory of cognition. The agency approach,
+motivated by the Society of Mind theory and the LIDA cognitive architecture,
+proposes the formation of agent collections that interact at micro and macro
+cognitive levels, driven by either LLMs or symbolic components. The
+neuro-symbolic approach, which takes inspiration from the CLARION cognitive
+architecture, proposes a model where bottom-up learning extracts symbolic
+representations from an LLM layer and top-down guidance utilizes symbolic
+representations to direct prompt engineering in the LLM layer. These approaches
+aim to harness the strengths of both LLMs and CAs, while mitigating their
+weaknesses, thereby advancing the development of more robust AI systems. We
+discuss the tradeoffs and challenges associated with each approach.
+"
+Manipulating Embeddings of Stable Diffusion Prompts,Niklas Deckers,http://arxiv.org/pdf/2308.12059v1.pdf,2023-08-23,"['cs.cv', 'cs.lg']",2308.12059v1.pdf,"  Generative text-to-image models such as Stable Diffusion allow users to
+generate images based on a textual description, the prompt. Changing the prompt
+is still the primary means for the user to change a generated image as desired.
+However, changing the image by reformulating the prompt remains a difficult
+process of trial and error, which has led to the emergence of prompt
+engineering as a new field of research. We propose and analyze methods to
+change the embedding of a prompt directly instead of the prompt text. It allows
+for more fine-grained and targeted control that takes into account user
+intentions. Our approach treats the generative text-to-image model as a
+continuous function and passes gradients between the image space and the prompt
+embedding space. By addressing different user interaction problems, we can
+apply this idea in three scenarios: (1) Optimization of a metric defined in
+image space that could measure, for example, image style. (2) Assistance of
+users in creative tasks by enabling them to navigate the image space along a
+selection of directions of ""near"" prompt embeddings. (3) Changing the embedding
+of the prompt to include information that the user has seen in a particular
+seed but finds difficult to describe in the prompt. Our experiments demonstrate
+the feasibility of the described methods.
+"
+Large Language Models in Fault Localisation,Yonghao Wu,http://arxiv.org/pdf/2308.15276v3.pdf,2023-08-29,['cs.se'],2308.15276v3.pdf,"  Large Language Models (LLMs) have shown promise in multiple software
+engineering tasks including code generation, program repair, code
+summarisation, and test generation. Fault localisation is instrumental in
+enabling automated debugging and repair of programs and was prominently
+featured as a highlight during the launch event of ChatGPT-4. Nevertheless, the
+performance of LLMs compared to state-of-the-art methods, as well as the impact
+of prompt design and context length on their efficacy, remains unclear. To fill
+this gap, this paper presents an in-depth investigation into the capability of
+ChatGPT-3.5 and ChatGPT-4, the two state-of-the-art LLMs, on fault
+localisation. Using the widely-adopted large-scale Defects4J dataset, we
+compare the two LLMs with the existing fault localisation techniques. We also
+investigate the consistency of LLMs in fault localisation, as well as how
+prompt engineering and the length of code context affect the fault localisation
+effectiveness.
+  Our findings demonstrate that within function-level context, ChatGPT-4
+outperforms all the existing fault localisation methods. Additional error logs
+can further improve ChatGPT models' localisation accuracy and consistency, with
+an average 46.9% higher accuracy over the state-of-the-art baseline SmartFL on
+the Defects4J dataset in terms of TOP-1 metric. However, when the code context
+of the Defects4J dataset expands to the class-level, ChatGPT-4's performance
+suffers a significant drop, with 49.9% lower accuracy than SmartFL under TOP-1
+metric. These observations indicate that although ChatGPT can effectively
+localise faults under specific conditions, limitations are evident. Further
+research is needed to fully harness the potential of LLMs like ChatGPT for
+practical fault localisation applications.
+"
+Leveraging Large Language Models for Exploiting ASR Uncertainty,Pranay Dighe,http://arxiv.org/pdf/2309.04842v2.pdf,2023-09-09,"['cs.cl', 'cs.hc', 'cs.sd', 'eess.as']",2309.04842v2.pdf,"  While large language models excel in a variety of natural language processing
+(NLP) tasks, to perform well on spoken language understanding (SLU) tasks, they
+must either rely on off-the-shelf automatic speech recognition (ASR) systems
+for transcription, or be equipped with an in-built speech modality. This work
+focuses on the former scenario, where LLM's accuracy on SLU tasks is
+constrained by the accuracy of a fixed ASR system on the spoken input.
+Specifically, we tackle speech-intent classification task, where a high
+word-error-rate can limit the LLM's ability to understand the spoken intent.
+Instead of chasing a high accuracy by designing complex or specialized
+architectures regardless of deployment costs, we seek to answer how far we can
+go without substantially changing the underlying ASR and LLM, which can
+potentially be shared by multiple unrelated tasks. To this end, we propose
+prompting the LLM with an n-best list of ASR hypotheses instead of only the
+error-prone 1-best hypothesis. We explore prompt-engineering to explain the
+concept of n-best lists to the LLM; followed by the finetuning of Low-Rank
+Adapters on the downstream tasks. Our approach using n-best lists proves to be
+effective on a device-directed speech detection task as well as on a keyword
+spotting task, where systems using n-best list prompts outperform those using
+1-best ASR hypothesis; thus paving the way for an efficient method to exploit
+ASR uncertainty via LLMs for speech-based applications.
+"
+Unveiling the potential of large language models in generating semantic  and cross-language clones,Palash R. Roy,http://arxiv.org/pdf/2309.06424v1.pdf,2023-09-12,"['cs.se', 'cs.ai', 'cs.lg']",2309.06424v1.pdf,"  Semantic and Cross-language code clone generation may be useful for code
+reuse, code comprehension, refactoring and benchmarking. OpenAI's GPT model has
+potential in such clone generation as GPT is used for text generation. When
+developers copy/paste codes from Stack Overflow (SO) or within a system, there
+might be inconsistent changes leading to unexpected behaviours. Similarly, if
+someone possesses a code snippet in a particular programming language but seeks
+equivalent functionality in a different language, a semantic cross-language
+code clone generation approach could provide valuable assistance. In this
+study, using SemanticCloneBench as a vehicle, we evaluated how well the GPT-3
+model could help generate semantic and cross-language clone variants for a
+given fragment.We have comprised a diverse set of code fragments and assessed
+GPT-3s performance in generating code variants.Through extensive
+experimentation and analysis, where 9 judges spent 158 hours to validate, we
+investigate the model's ability to produce accurate and semantically correct
+variants. Our findings shed light on GPT-3's strengths in code generation,
+offering insights into the potential applications and challenges of using
+advanced language models in software development. Our quantitative analysis
+yields compelling results. In the realm of semantic clones, GPT-3 attains an
+impressive accuracy of 62.14% and 0.55 BLEU score, achieved through few-shot
+prompt engineering. Furthermore, the model shines in transcending linguistic
+confines, boasting an exceptional 91.25% accuracy in generating cross-language
+clones
+"
+Is GPT4 a Good Trader?,Bingzhe Wu,http://arxiv.org/pdf/2309.10982v1.pdf,2023-09-20,['cs.ai'],2309.10982v1.pdf,"  Recently, large language models (LLMs), particularly GPT-4, have demonstrated
+significant capabilities in various planning and reasoning tasks
+\cite{cheng2023gpt4,bubeck2023sparks}. Motivated by these advancements, there
+has been a surge of interest among researchers to harness the capabilities of
+GPT-4 for the automated design of quantitative factors that do not overlap with
+existing factor libraries, with an aspiration to achieve alpha returns
+\cite{webpagequant}. In contrast to these work, this study aims to examine the
+fidelity of GPT-4's comprehension of classic trading theories and its
+proficiency in applying its code interpreter abilities to real-world trading
+data analysis. Such an exploration is instrumental in discerning whether the
+underlying logic GPT-4 employs for trading is intrinsically reliable.
+Furthermore, given the acknowledged interpretative latitude inherent in most
+trading theories, we seek to distill more precise methodologies of deploying
+these theories from GPT-4's analytical process, potentially offering invaluable
+insights to human traders.
+  To achieve this objective, we selected daily candlestick (K-line) data from
+specific periods for certain assets, such as the Shanghai Stock Index. Through
+meticulous prompt engineering, we guided GPT-4 to analyze the technical
+structures embedded within this data, based on specific theories like the
+Elliott Wave Theory. We then subjected its analytical output to manual
+evaluation, assessing its interpretative depth and accuracy vis-\`a-vis these
+trading theories from multiple dimensions. The results and findings from this
+study could pave the way for a synergistic amalgamation of human expertise and
+AI-driven insights in the realm of trading.
+"
+AI-Copilot for Business Optimisation: A Framework and A Case Study in  Production Scheduling,Pivithuru Thejan Amarasinghe,http://arxiv.org/pdf/2309.13218v3.pdf,2023-09-22,['cs.ai'],2309.13218v3.pdf,"  Business optimisation refers to the process of finding and implementing
+efficient and cost-effective means of operation to bring a competitive
+advantage for businesses. Synthesizing problem formulations is an integral part
+of business optimisation, which relies on human expertise to construct problem
+formulations using optimisation languages. Interestingly, with advancements in
+Large Language Models (LLMs), the human expertise needed in problem formulation
+can be minimized. However, developing an LLM for problem formulation is
+challenging, due to training data, token limitations, and lack of appropriate
+performance metrics. For the requirement of training data, recent attention has
+been directed towards fine-tuning pre-trained LLMs for downstream tasks rather
+than training an LLM from scratch for a specific task. In this paper, we adopt
+an LLM fine-tuning approach and propose an AI-Copilot for business optimisation
+problem formulation. For token limitations, we introduce modularization and
+prompt engineering techniques to synthesize complex problem formulations as
+modules that fit into the token limits of LLMs. Additionally, we design
+performance evaluation metrics that are better suited for assessing the
+accuracy and quality of problem formulations. The experiment results
+demonstrate that with this approach we can synthesize complex and large problem
+formulations for a typical business optimisation problem in production
+scheduling.
+"
+An AI Chatbot for Explaining Deep Reinforcement Learning Decisions of  Service-oriented Systems,Andreas Metzger,http://arxiv.org/pdf/2309.14391v1.pdf,2023-09-25,"['cs.lg', 'cs.ai', 'cs.cl']",2309.14391v1.pdf,"  Deep Reinforcement Learning (Deep RL) is increasingly used to cope with the
+open-world assumption in service-oriented systems. Deep RL was successfully
+applied to problems such as dynamic service composition, job scheduling, and
+offloading, as well as service adaptation. While Deep RL offers many benefits,
+understanding the decision-making of Deep RL is challenging because its learned
+decision-making policy essentially appears as a black box. Yet, understanding
+the decision-making of Deep RL is key to help service developers perform
+debugging, support service providers to comply with relevant legal frameworks,
+and facilitate service users to build trust. We introduce Chat4XAI to
+facilitate the understanding of the decision-making of Deep RL by providing
+natural-language explanations. Compared with visual explanations, the reported
+benefits of natural-language explanations include better understandability for
+non-technical users, increased user acceptance and trust, as well as more
+efficient explanations. Chat4XAI leverages modern AI chatbot technology and
+dedicated prompt engineering. Compared to earlier work on natural-language
+explanations using classical software-based dialogue systems, using an AI
+chatbot eliminates the need for eliciting and defining potential questions and
+answers up-front. We prototypically realize Chat4XAI using OpenAI's ChatGPT API
+and evaluate the fidelity and stability of its explanations using an adaptive
+service exemplar.
+"
+Batch Calibration: Rethinking Calibration for In-Context Learning and  Prompt Engineering,Han Zhou,http://arxiv.org/pdf/2309.17249v1.pdf,2023-09-29,"['cs.cl', 'cs.ai', 'cs.lg']",2309.17249v1.pdf,"  Prompting and in-context learning (ICL) have become efficient learning
+paradigms for large language models (LLMs). However, LLMs suffer from prompt
+brittleness and various bias factors in the prompt, including but not limited
+to the formatting, the choice verbalizers, and the ICL examples. To address
+this problem that results in unexpected performance degradation, calibration
+methods have been developed to mitigate the effects of these biases while
+recovering LLM performance. In this work, we first conduct a systematic
+analysis of the existing calibration methods, where we both provide a unified
+view and reveal the failure cases. Inspired by these analyses, we propose Batch
+Calibration (BC), a simple yet intuitive method that controls the contextual
+bias from the batched input, unifies various prior approaches, and effectively
+addresses the aforementioned issues. BC is zero-shot, inference-only, and
+incurs negligible additional costs. In the few-shot setup, we further extend BC
+to allow it to learn the contextual bias from labeled data. We validate the
+effectiveness of BC with PaLM 2-(S, M, L) and CLIP models and demonstrate
+state-of-the-art performance over previous calibration baselines across more
+than 10 natural language understanding and image classification tasks.
+"
+Suspicion-Agent: Playing Imperfect Information Games with Theory of Mind  Aware GPT-4,Jiaxian Guo,http://arxiv.org/pdf/2309.17277v2.pdf,2023-09-29,['cs.ai'],2309.17277v2.pdf,"  Unlike perfect information games, where all elements are known to every
+player, imperfect information games emulate the real-world complexities of
+decision-making under uncertain or incomplete information. GPT-4, the recent
+breakthrough in large language models (LLMs) trained on massive passive data,
+is notable for its knowledge retrieval and reasoning abilities. This paper
+delves into the applicability of GPT-4's learned knowledge for imperfect
+information games. To achieve this, we introduce \textbf{Suspicion-Agent}, an
+innovative agent that leverages GPT-4's capabilities for performing in
+imperfect information games. With proper prompt engineering to achieve
+different functions, Suspicion-Agent based on GPT-4 demonstrates remarkable
+adaptability across a range of imperfect information card games. Importantly,
+GPT-4 displays a strong high-order theory of mind (ToM) capacity, meaning it
+can understand others and intentionally impact others' behavior. Leveraging
+this, we design a planning strategy that enables GPT-4 to competently play
+against different opponents, adapting its gameplay style as needed, while
+requiring only the game rules and descriptions of observations as input. In the
+experiments, we qualitatively showcase the capabilities of Suspicion-Agent
+across three different imperfect information games and then quantitatively
+evaluate it in Leduc Hold'em. The results show that Suspicion-Agent can
+potentially outperform traditional algorithms designed for imperfect
+information games, without any specialized training or examples. In order to
+encourage and foster deeper insights within the community, we make our
+game-related data publicly available.
+"
+Investigating the Limitation of CLIP Models: The Worst-Performing  Categories,Jie-Jing Shao,http://arxiv.org/pdf/2310.03324v1.pdf,2023-10-05,"['cs.cv', 'cs.lg']",2310.03324v1.pdf,"  Contrastive Language-Image Pre-training (CLIP) provides a foundation model by
+integrating natural language into visual concepts, enabling zero-shot
+recognition on downstream tasks. It is usually expected that satisfactory
+overall accuracy can be achieved across numerous domains through well-designed
+textual prompts. However, we found that their performance in the worst
+categories is significantly inferior to the overall performance. For example,
+on ImageNet, there are a total of 10 categories with class-wise accuracy as low
+as 0\%, even though the overall performance has achieved 64.1\%. This
+phenomenon reveals the potential risks associated with using CLIP models,
+particularly in risk-sensitive applications where specific categories hold
+significant importance. To address this issue, we investigate the alignment
+between the two modalities in the CLIP model and propose the Class-wise
+Matching Margin (\cmm) to measure the inference confusion. \cmm\ can
+effectively identify the worst-performing categories and estimate the potential
+performance of the candidate prompts. We further query large language models to
+enrich descriptions of worst-performing categories and build a weighted
+ensemble to highlight the efficient prompts. Experimental results clearly
+verify the effectiveness of our proposal, where the accuracy on the worst-10
+categories on ImageNet is boosted to 5.2\%, without manual prompt engineering,
+laborious optimization, or access to labeled validation data.
+"
+Thought Propagation: An Analogical Approach to Complex Reasoning with  Large Language Models,Junchi Yu,http://arxiv.org/pdf/2310.03965v2.pdf,2023-10-06,"['cs.ai', 'cs.cl']",2310.03965v2.pdf,"  Large Language Models (LLMs) have achieved remarkable success in reasoning
+tasks with the development of prompting methods. However, existing prompting
+approaches cannot reuse insights of solving similar problems and suffer from
+accumulated errors in multi-step reasoning, since they prompt LLMs to reason
+\textit{from scratch}. To address these issues, we propose
+\textbf{\textit{Thought Propagation} (TP)}, which explores the analogous
+problems and leverages their solutions to enhance the complex reasoning ability
+of LLMs. These analogous problems are related to the input one, with reusable
+solutions and problem-solving strategies. Thus, it is promising to propagate
+insights of solving previous analogous problems to inspire new problem-solving.
+To achieve this, TP first prompts LLMs to propose and solve a set of analogous
+problems that are related to the input one. Then, TP reuses the results of
+analogous problems to directly yield a new solution or derive a
+knowledge-intensive plan for execution to amend the initial solution obtained
+from scratch. TP is compatible with existing prompting approaches, allowing
+plug-and-play generalization and enhancement in a wide range of tasks without
+much labor in task-specific prompt engineering. Experiments across three
+challenging tasks demonstrate TP enjoys a substantial improvement over the
+baselines by an average of 12\% absolute increase in finding the optimal
+solutions in Shortest-path Reasoning, 13\% improvement of human preference in
+Creative Writing, and 15\% enhancement in the task completion rate of LLM-Agent
+Planning.
+"
+JVNV: A Corpus of Japanese Emotional Speech with Verbal Content and  Nonverbal Expressions,Detai Xin,http://arxiv.org/pdf/2310.06072v1.pdf,2023-10-09,"['cs.sd', 'eess.as']",2310.06072v1.pdf,"  We present the JVNV, a Japanese emotional speech corpus with verbal content
+and nonverbal vocalizations whose scripts are generated by a large-scale
+language model. Existing emotional speech corpora lack not only proper
+emotional scripts but also nonverbal vocalizations (NVs) that are essential
+expressions in spoken language to express emotions. We propose an automatic
+script generation method to produce emotional scripts by providing seed words
+with sentiment polarity and phrases of nonverbal vocalizations to ChatGPT using
+prompt engineering. We select 514 scripts with balanced phoneme coverage from
+the generated candidate scripts with the assistance of emotion confidence
+scores and language fluency scores. We demonstrate the effectiveness of JVNV by
+showing that JVNV has better phoneme coverage and emotion recognizability than
+previous Japanese emotional speech corpora. We then benchmark JVNV on emotional
+text-to-speech synthesis using discrete codes to represent NVs. We show that
+there still exists a gap between the performance of synthesizing read-aloud
+speech and emotional speech, and adding NVs in the speech makes the task even
+harder, which brings new challenges for this task and makes JVNV a valuable
+resource for relevant works in the future. To our best knowledge, JVNV is the
+first speech corpus that generates scripts automatically using large language
+models.
+"
+Large Language Model-Empowered Agents for Simulating Macroeconomic  Activities,Nian Li,http://arxiv.org/pdf/2310.10436v1.pdf,2023-10-16,['cs.ai'],2310.10436v1.pdf,"  The advent of the Web has brought about a paradigm shift in traditional
+economics, particularly in the digital economy era, enabling the precise
+recording and analysis of individual economic behavior. This has led to a
+growing emphasis on data-driven modeling in macroeconomics. In macroeconomic
+research, Agent-based modeling (ABM) emerged as an alternative, evolving
+through rule-based agents, machine learning-enhanced decision-making, and, more
+recently, advanced AI agents. However, the existing works are suffering from
+three main challenges when endowing agents with human-like decision-making,
+including agent heterogeneity, the influence of macroeconomic trends, and
+multifaceted economic factors. Large language models (LLMs) have recently
+gained prominence in offering autonomous human-like characteristics. Therefore,
+leveraging LLMs in macroeconomic simulation presents an opportunity to overcome
+traditional limitations. In this work, we take an early step in introducing a
+novel approach that leverages LLMs in macroeconomic simulation. We design
+prompt-engineering-driven LLM agents to exhibit human-like decision-making and
+adaptability in the economic environment, with the abilities of perception,
+reflection, and decision-making to address the abovementioned challenges.
+Simulation experiments on macroeconomic activities show that LLM-empowered
+agents can make realistic work and consumption decisions and emerge more
+reasonable macroeconomic phenomena than existing rule-based or AI agents. Our
+work demonstrates the promising potential to simulate macroeconomics based on
+LLM and its human-like characteristics.
+"
+Large Language Model for Multi-objective Evolutionary Optimization,Fei Liu,http://arxiv.org/pdf/2310.12541v2.pdf,2023-10-19,"['cs.ne', 'cs.ai', 'cs.cl', 'cs.et']",2310.12541v2.pdf,"  Multiobjective evolutionary algorithms (MOEAs) are major methods for solving
+multiobjective optimization problems (MOPs). Many MOEAs have been proposed in
+the past decades, of which the search operators need a carefully handcrafted
+design with domain knowledge. Recently, some attempts have been made to replace
+the manually designed operators in MOEAs with learning-based operators (e.g.,
+neural network models). However, much effort is still required for designing
+and training such models, and the learned operators might not generalize well
+on new problems. To tackle the above challenges, this work investigates a novel
+approach that leverages the powerful large language model (LLM) to design MOEA
+operators. With proper prompt engineering, we successfully let a general LLM
+serve as a black-box search operator for decomposition-based MOEA (MOEA/D) in a
+zero-shot manner. In addition, by learning from the LLM behavior, we further
+design an explicit white-box operator with randomness and propose a new version
+of decomposition-based MOEA, termed MOEA/D-LO. Experimental studies on
+different test benchmarks show that our proposed method can achieve competitive
+performance with widely used MOEAs. It is also promising to see the operator
+only learned from a few instances can have robust generalization performance on
+unseen problems with quite different patterns and settings. The results reveal
+the potential benefits of using pre-trained LLMs in the design of MOEAs.
+"
+Vision-Language Models are Zero-Shot Reward Models for Reinforcement  Learning,Juan Rocamonde,http://arxiv.org/pdf/2310.12921v1.pdf,2023-10-19,"['cs.lg', 'cs.ai']",2310.12921v1.pdf,"  Reinforcement learning (RL) requires either manually specifying a reward
+function, which is often infeasible, or learning a reward model from a large
+amount of human feedback, which is often very expensive. We study a more
+sample-efficient alternative: using pretrained vision-language models (VLMs) as
+zero-shot reward models (RMs) to specify tasks via natural language. We propose
+a natural and general approach to using VLMs as reward models, which we call
+VLM-RMs. We use VLM-RMs based on CLIP to train a MuJoCo humanoid to learn
+complex tasks without a manually specified reward function, such as kneeling,
+doing the splits, and sitting in a lotus position. For each of these tasks, we
+only provide a single sentence text prompt describing the desired task with
+minimal prompt engineering. We provide videos of the trained agents at:
+https://sites.google.com/view/vlm-rm. We can improve performance by providing a
+second ``baseline'' prompt and projecting out parts of the CLIP embedding space
+irrelevant to distinguish between goal and baseline. Further, we find a strong
+scaling effect for VLM-RMs: larger VLMs trained with more compute and data are
+better reward models. The failure modes of VLM-RMs we encountered are all
+related to known capability limitations of current VLMs, such as limited
+spatial reasoning ability or visually unrealistic environments that are far
+off-distribution for the VLM. We find that VLM-RMs are remarkably robust as
+long as the VLM is large enough. This suggests that future VLMs will become
+more and more useful reward models for a wide range of RL applications.
+"
+Enhancing Zero-Shot Crypto Sentiment with Fine-tuned Language Model and  Prompt Engineering,Rahman S M Wahidur,http://arxiv.org/pdf/2310.13226v1.pdf,2023-10-20,['cs.cl'],2310.13226v1.pdf,"  Blockchain technology has revolutionized the financial landscape, with
+cryptocurrencies gaining widespread adoption for their decentralized and
+transparent nature. As the sentiment expressed on social media platforms can
+significantly influence cryptocurrency discussions and market movements,
+sentiment analysis has emerged as a crucial tool for understanding public
+opinion and predicting market trends. Motivated by the aim to enhance sentiment
+analysis accuracy in the cryptocurrency domain, this paper investigates
+fine-tuning techniques on large language models. This paper also investigates
+the efficacy of supervised fine-tuning and instruction-based fine-tuning on
+large language models for unseen tasks. Experimental results demonstrate a
+significant average zero-shot performance gain of 40% after fine-tuning,
+highlighting the potential of this technique in optimizing pre-trained language
+model efficiency. Additionally, the impact of instruction tuning on models of
+varying scales is examined, revealing that larger models benefit from
+instruction tuning, achieving the highest average accuracy score of 75.16%. In
+contrast, smaller-scale models may experience reduced generalization due to the
+complete utilization of model capacity. To gain deeper insight about how
+instruction works with these language models, this paper presents an
+experimental investigation into the response of an instruction-based model
+under different instruction tuning setups. The investigation demonstrates that
+the model achieves an average accuracy score of 72.38% for short and simple
+instructions. This performance significantly outperforms its accuracy under
+long and complex instructions by over 12%, thereby effectively highlighting the
+profound significance of instruction characteristics in maximizing model
+performance.
+"
+Can LLMs Grade Short-answer Reading Comprehension Questions :  Foundational Literacy Assessment in LMICs,Owen Henkel,http://arxiv.org/pdf/2310.18373v1.pdf,2023-10-26,"['cs.cl', 'cs.ai']",2310.18373v1.pdf,"  This paper presents emerging evidence of using generative large language
+models (i.e., GPT-4) to reliably evaluate short-answer reading comprehension
+questions. Specifically, we explore how various configurations of generative
+(LLMs) are able to evaluate student responses from a new dataset, drawn from a
+battery of reading assessments conducted with over 150 students in Ghana. As
+this dataset is novel and hence not used in training runs of GPT, it offers an
+opportunity to test for domain shift and evaluate the generalizability of
+generative LLMs, which are predominantly designed and trained on data from
+high-income North American countries. We found that GPT-4, with minimal prompt
+engineering performed extremely well on evaluating the novel dataset (Quadratic
+Weighted Kappa 0.923, F1 0.88), substantially outperforming transfer-learning
+based approaches, and even exceeding expert human raters (Quadratic Weighted
+Kappa 0.915, F1 0.87). To the best of our knowledge, our work is the first to
+empirically evaluate the performance of generative LLMs on short-answer reading
+comprehension questions, using real student data, and suggests that generative
+LLMs have the potential to reliably evaluate foundational literacy. Currently
+the assessment of formative literacy and numeracy is infrequent in many low and
+middle-income countries (LMICs) due to the cost and operational complexities of
+conducting them at scale. Automating the grading process for reading assessment
+could enable wider usage, and in turn improve decision-making regarding
+curricula, school management, and teaching practice at the classroom level.
+Importantly, in contrast transfer learning based approaches, generative LLMs
+generalize well and the technical barriers to their use are low, making them
+more feasible to implement and scale in lower resource educational contexts.
+"
+Promise:Prompt-driven 3D Medical Image Segmentation Using Pretrained  Image Foundation Models,Hao Li,http://arxiv.org/pdf/2310.19721v2.pdf,2023-10-30,"['eess.iv', 'cs.cv']",2310.19721v2.pdf,"  To address prevalent issues in medical imaging, such as data acquisition
+challenges and label availability, transfer learning from natural to medical
+image domains serves as a viable strategy to produce reliable segmentation
+results. However, several existing barriers between domains need to be broken
+down, including addressing contrast discrepancies, managing anatomical
+variability, and adapting 2D pretrained models for 3D segmentation tasks. In
+this paper, we propose ProMISe,a prompt-driven 3D medical image segmentation
+model using only a single point prompt to leverage knowledge from a pretrained
+2D image foundation model. In particular, we use the pretrained vision
+transformer from the Segment Anything Model (SAM) and integrate lightweight
+adapters to extract depth-related (3D) spatial context without updating the
+pretrained weights. For robust results, a hybrid network with complementary
+encoders is designed, and a boundary-aware loss is proposed to achieve precise
+boundaries. We evaluate our model on two public datasets for colon and pancreas
+tumor segmentations, respectively. Compared to the state-of-the-art
+segmentation methods with and without prompt engineering, our proposed method
+achieves superior performance. The code is publicly available at
+https://github.com/MedICL-VU/ProMISe.
+"
+Making Large Language Models Better Data Creators,Dong-Ho Lee,http://arxiv.org/pdf/2310.20111v1.pdf,2023-10-31,['cs.cl'],2310.20111v1.pdf,"  Although large language models (LLMs) have advanced the state-of-the-art in
+NLP significantly, deploying them for downstream applications is still
+challenging due to cost, responsiveness, control, or concerns around privacy
+and security. As such, trainable models are still the preferred option in some
+cases. However, these models still require human-labeled data for optimal
+performance, which is expensive and time-consuming to obtain. In order to
+address this issue, several techniques to reduce human effort involve labeling
+or generating data using LLMs. Although these methods are effective for certain
+applications, in practice they encounter difficulties in real-world scenarios.
+Labeling data requires careful data selection, while generating data
+necessitates task-specific prompt engineering. In this paper, we propose a
+unified data creation pipeline that requires only a single formatting example,
+and which is applicable to a broad range of tasks, including traditionally
+problematic ones with semantically devoid label spaces. In our experiments we
+demonstrate that instruction-following LLMs are highly cost-effective data
+creators, and that models trained with these data exhibit performance better
+than those trained with human-labeled data (by up to 17.5%) on
+out-of-distribution evaluation, while maintaining comparable performance on
+in-distribution tasks. These results have important implications for the
+robustness of NLP systems deployed in the real-world.
+"
+VisPercep: A Vision-Language Approach to Enhance Visual Perception for  People with Blindness and Low Vision,Yu Hao,http://arxiv.org/pdf/2310.20225v1.pdf,2023-10-31,"['cs.cv', 'cs.ai']",2310.20225v1.pdf,"  People with blindness and low vision (pBLV) encounter substantial challenges
+when it comes to comprehensive scene recognition and precise object
+identification in unfamiliar environments. Additionally, due to the vision
+loss, pBLV have difficulty in accessing and identifying potential tripping
+hazards on their own. In this paper, we present a pioneering approach that
+leverages a large vision-language model to enhance visual perception for pBLV,
+offering detailed and comprehensive descriptions of the surrounding
+environments and providing warnings about the potential risks. Our method
+begins by leveraging a large image tagging model (i.e., Recognize Anything
+(RAM)) to identify all common objects present in the captured images. The
+recognition results and user query are then integrated into a prompt, tailored
+specifically for pBLV using prompt engineering. By combining the prompt and
+input image, a large vision-language model (i.e., InstructBLIP) generates
+detailed and comprehensive descriptions of the environment and identifies
+potential risks in the environment by analyzing the environmental objects and
+scenes, relevant to the prompt. We evaluate our approach through experiments
+conducted on both indoor and outdoor datasets. Our results demonstrate that our
+method is able to recognize objects accurately and provide insightful
+descriptions and analysis of the environment for pBLV.
+"
+BigBIO: A Framework for Data-Centric Biomedical Natural Language  Processing,Jason Alan Fries,http://arxiv.org/pdf/2206.15076v1.pdf,2022-06-30,['cs.cl'],2206.15076v1.pdf,"  Training and evaluating language models increasingly requires the
+construction of meta-datasets --diverse collections of curated data with clear
+provenance. Natural language prompting has recently lead to improved zero-shot
+generalization by transforming existing, supervised datasets into a diversity
+of novel pretraining tasks, highlighting the benefits of meta-dataset curation.
+While successful in general-domain text, translating these data-centric
+approaches to biomedical language modeling remains challenging, as labeled
+biomedical datasets are significantly underrepresented in popular data hubs. To
+address this challenge, we introduce BigBIO a community library of 126+
+biomedical NLP datasets, currently covering 12 task categories and 10+
+languages. BigBIO facilitates reproducible meta-dataset curation via
+programmatic access to datasets and their metadata, and is compatible with
+current platforms for prompt engineering and end-to-end few/zero shot language
+model evaluation. We discuss our process for task schema harmonization, data
+auditing, contribution guidelines, and outline two illustrative use cases:
+zero-shot evaluation of biomedical prompts and large-scale, multi-task
+learning. BigBIO is an ongoing community effort and is available at
+https://github.com/bigscience-workshop/biomedical
+"
+GPT Takes the Bar Exam,Michael Bommarito II,http://arxiv.org/pdf/2212.14402v1.pdf,2022-12-29,"['cs.cl', 'cs.ai', 'cs.lg']",2212.14402v1.pdf,"  Nearly all jurisdictions in the United States require a professional license
+exam, commonly referred to as ""the Bar Exam,"" as a precondition for law
+practice. To even sit for the exam, most jurisdictions require that an
+applicant completes at least seven years of post-secondary education, including
+three years at an accredited law school. In addition, most test-takers also
+undergo weeks to months of further, exam-specific preparation. Despite this
+significant investment of time and capital, approximately one in five
+test-takers still score under the rate required to pass the exam on their first
+try. In the face of a complex task that requires such depth of knowledge, what,
+then, should we expect of the state of the art in ""AI?"" In this research, we
+document our experimental evaluation of the performance of OpenAI's
+`text-davinci-003` model, often-referred to as GPT-3.5, on the multistate
+multiple choice (MBE) section of the exam. While we find no benefit in
+fine-tuning over GPT-3.5's zero-shot performance at the scale of our training
+data, we do find that hyperparameter optimization and prompt engineering
+positively impacted GPT-3.5's zero-shot performance. For best prompt and
+parameters, GPT-3.5 achieves a headline correct rate of 50.3% on a complete
+NCBE MBE practice exam, significantly in excess of the 25% baseline guessing
+rate, and performs at a passing rate for both Evidence and Torts. GPT-3.5's
+ranking of responses is also highly-correlated with correctness; its top two
+and top three choices are correct 71% and 88% of the time, respectively,
+indicating very strong non-entailment performance. While our ability to
+interpret these results is limited by nascent scientific understanding of LLMs
+and the proprietary nature of GPT, we believe that these results strongly
+suggest that an LLM will pass the MBE component of the Bar Exam in the near
+future.
+"
+Few-shot Multimodal Multitask Multilingual Learning,Aman Chadha,http://arxiv.org/pdf/2303.12489v1.pdf,2023-02-19,"['cs.lg', 'cs.ai', 'cs.cl', 'cs.cv', 'cs.mm']",2303.12489v1.pdf,"  While few-shot learning as a transfer learning paradigm has gained
+significant traction for scenarios with limited data, it has primarily been
+explored in the context of building unimodal and unilingual models.
+Furthermore, a significant part of the existing literature in the domain of
+few-shot multitask learning perform in-context learning which requires manually
+generated prompts as the input, yielding varying outcomes depending on the
+level of manual prompt-engineering. In addition, in-context learning suffers
+from substantial computational, memory, and storage costs which eventually
+leads to high inference latency because it involves running all of the prompt's
+examples through the model every time a prediction is made. In contrast,
+methods based on the transfer learning via the fine-tuning paradigm avoid the
+aforementioned issues at a one-time cost of fine-tuning weights on a per-task
+basis. However, such methods lack exposure to few-shot multimodal multitask
+learning. In this paper, we propose few-shot learning for a multimodal
+multitask multilingual (FM3) setting by adapting pre-trained vision and
+language models using task-specific hypernetworks and contrastively fine-tuning
+them to enable few-shot learning. FM3's architecture combines the best of both
+worlds of in-context and fine-tuning based learning and consists of three major
+components: (i) multimodal contrastive fine-tuning to enable few-shot learning,
+(ii) hypernetwork task adaptation to perform multitask learning, and (iii)
+task-specific output heads to cater to a plethora of diverse tasks. FM3 learns
+the most prominent tasks in the vision and language domains along with their
+intersections, namely visual entailment (VE), visual question answering (VQA),
+and natural language understanding (NLU) tasks such as neural entity
+recognition (NER) and the GLUE benchmark including QNLI, MNLI, QQP, and SST-2.
+"
+Improving Few-Shot Prompts with Relevant Static Analysis Products,Toufique Ahmed,http://arxiv.org/pdf/2304.06815v2.pdf,2023-04-13,"['cs.se', 'cs.lg']",2304.06815v2.pdf,"  Large Language Models (LLM) are a new class of computation engines,
+""programmed"" via prompt engineering. We are still learning how to best
+""program"" these LLMs to help developers. We start with the intuition that
+developers tend to consciously and unconsciously have a collection of semantics
+facts in mind when working on coding tasks. Mostly these are shallow, simple
+facts arising from a quick read. For a function, examples of facts might
+include parameter and local variable names, return expressions, simple pre- and
+post-conditions, and basic control and data flow, etc.
+  One might assume that the powerful multi-layer architecture of
+transformer-style LLMs makes them inherently capable of doing this simple level
+of ""code analysis"" and extracting such information, implicitly, while
+processing code: but are they, really? If they aren't, could explicitly adding
+this information help? Our goal here is to investigate this question, using the
+code summarization task and evaluate whether automatically augmenting an LLM's
+prompt with semantic facts explicitly, actually helps.
+  Prior work shows that LLM performance on code summarization benefits from
+few-shot samples drawn either from the same-project or from examples found via
+information retrieval methods (such as BM25). While summarization performance
+has steadily increased since the early days, there is still room for
+improvement: LLM performance on code summarization still lags its performance
+on natural-language tasks like translation and text summarization.
+  We find that adding semantic facts actually does help! This approach improves
+performance in several different settings suggested by prior work, including
+for two different Large Language Models. In most cases, improvement nears or
+exceeds 2 BLEU; for the PHP language in the challenging CodeSearchNet dataset,
+this augmentation actually yields performance surpassing 30 BLEU.
+"
+Evaluation of GPT-3.5 and GPT-4 for supporting real-world information  needs in healthcare delivery,Debadutta Dash,http://arxiv.org/pdf/2304.13714v3.pdf,2023-04-26,"['cs.ai', 'cs.cl', 'cs.ir']",2304.13714v3.pdf,"  Despite growing interest in using large language models (LLMs) in healthcare,
+current explorations do not assess the real-world utility and safety of LLMs in
+clinical settings. Our objective was to determine whether two LLMs can serve
+information needs submitted by physicians as questions to an informatics
+consultation service in a safe and concordant manner. Sixty six questions from
+an informatics consult service were submitted to GPT-3.5 and GPT-4 via simple
+prompts. 12 physicians assessed the LLM responses' possibility of patient harm
+and concordance with existing reports from an informatics consultation service.
+Physician assessments were summarized based on majority vote. For no questions
+did a majority of physicians deem either LLM response as harmful. For GPT-3.5,
+responses to 8 questions were concordant with the informatics consult report,
+20 discordant, and 9 were unable to be assessed. There were 29 responses with
+no majority on ""Agree"", ""Disagree"", and ""Unable to assess"". For GPT-4,
+responses to 13 questions were concordant, 15 discordant, and 3 were unable to
+be assessed. There were 35 responses with no majority. Responses from both LLMs
+were largely devoid of overt harm, but less than 20% of the responses agreed
+with an answer from an informatics consultation service, responses contained
+hallucinated references, and physicians were divided on what constitutes harm.
+These results suggest that while general purpose LLMs are able to provide safe
+and credible responses, they often do not meet the specific information need of
+a given question. A definitive evaluation of the usefulness of LLMs in
+healthcare settings will likely require additional research on prompt
+engineering, calibration, and custom-tailoring of general purpose models.
+"
+Zelda: Video Analytics using Vision-Language Models,Francisco Romero,http://arxiv.org/pdf/2305.03785v2.pdf,2023-05-05,['cs.db'],2305.03785v2.pdf,"  Advances in ML have motivated the design of video analytics systems that
+allow for structured queries over video datasets. However, existing systems
+limit query expressivity, require users to specify an ML model per predicate,
+rely on complex optimizations that trade off accuracy for performance, and
+return large amounts of redundant and low-quality results. This paper focuses
+on the recently developed Vision-Language Models (VLMs) that allow users to
+query images using natural language like ""cars during daytime at traffic
+intersections."" Through an in-depth analysis, we show VLMs address three
+limitations of current video analytics systems: general expressivity, a single
+general purpose model to query many predicates, and are both simple and fast.
+However, VLMs still return large numbers of redundant and low-quality results
+that can overwhelm and burden users. In addition, VLMs often require manual
+prompt engineering to improve result relevance.
+  We present Zelda: a video analytics system that uses VLMs to return both
+relevant and semantically diverse results for top-K queries on large video
+datasets. Zelda prompts the VLM with the user's query in natural language.
+Zelda then automatically adds discriminator and synonym terms to boost
+accuracy, and terms to identify low-quality frames. To improve result
+diversity, Zelda uses semantic-rich VLM embeddings in an algorithm that prunes
+similar frames while considering their relevance to the query and the number of
+top-K results requested. We evaluate Zelda across five datasets and 19 queries
+and quantitatively show it achieves higher mean average precision (up to 1.15x)
+and improves average pairwise similarity (up to 1.16x) compared to using VLMs
+out-of-the-box. We also compare Zelda to a state-of-the-art video analytics
+engine and show that Zelda retrieves results 7.5x (up to 10.4x) faster for the
+same accuracy and frame diversity.
+"
+ConES: Concept Embedding Search for Parameter Efficient Tuning Large  Vision Language Models,Huahui Yi,http://arxiv.org/pdf/2305.18993v1.pdf,2023-05-30,['cs.cv'],2305.18993v1.pdf,"  Large pre-trained vision-language models have shown great prominence in
+transferring pre-acquired knowledge to various domains and downstream tasks
+with appropriate prompting or tuning. Existing prevalent tuning methods can be
+generally categorized into three genres: 1) prompt engineering by creating
+suitable prompt texts, which is time-consuming and requires domain expertise;
+2) or simply fine-tuning the whole model, which is extremely inefficient; 3)
+prompt tuning through parameterized prompt embeddings with the text encoder.
+Nevertheless, all methods rely on the text encoder for bridging the modality
+gap between vision and language. In this work, we question the necessity of the
+cumbersome text encoder for a more lightweight and efficient tuning paradigm as
+well as more representative prompt embeddings closer to the image
+representations. To achieve this, we propose a Concept Embedding Search (ConES)
+approach by optimizing prompt embeddings -- without the need of the text
+encoder -- to capture the 'concept' of the image modality through a variety of
+task objectives. By dropping the text encoder, we are able to significantly
+speed up the learning process, \eg, from about an hour to just ten minutes in
+our experiments for personalized text-to-image generation without impairing the
+generation quality. Moreover, our proposed approach is orthogonal to current
+existing tuning methods since the searched concept embeddings can be further
+utilized in the next stage of fine-tuning the pre-trained large models for
+boosting performance. Extensive experiments show that our approach can beat the
+prompt tuning and textual inversion methods in a variety of downstream tasks
+including objection detection, instance segmentation, and image generation. Our
+approach also shows better generalization capability for unseen concepts in
+specialized domains, such as the medical domain.
+"
+ChatGPT Chemistry Assistant for Text Mining and Prediction of MOF  Synthesis,Zhiling Zheng,http://arxiv.org/pdf/2306.11296v2.pdf,2023-06-20,"['cs.ir', 'cond-mat.mtrl-sci', 'cs.cl', 'physics.chem-ph']",2306.11296v2.pdf,"  We use prompt engineering to guide ChatGPT in the automation of text mining
+of metal-organic frameworks (MOFs) synthesis conditions from diverse formats
+and styles of the scientific literature. This effectively mitigates ChatGPT's
+tendency to hallucinate information -- an issue that previously made the use of
+Large Language Models (LLMs) in scientific fields challenging. Our approach
+involves the development of a workflow implementing three different processes
+for text mining, programmed by ChatGPT itself. All of them enable parsing,
+searching, filtering, classification, summarization, and data unification with
+different tradeoffs between labor, speed, and accuracy. We deploy this system
+to extract 26,257 distinct synthesis parameters pertaining to approximately 800
+MOFs sourced from peer-reviewed research articles. This process incorporates
+our ChemPrompt Engineering strategy to instruct ChatGPT in text mining,
+resulting in impressive precision, recall, and F1 scores of 90-99%.
+Furthermore, with the dataset built by text mining, we constructed a
+machine-learning model with over 86% accuracy in predicting MOF experimental
+crystallization outcomes and preliminarily identifying important factors in MOF
+crystallization. We also developed a reliable data-grounded MOF chatbot to
+answer questions on chemical reactions and synthesis procedures. Given that the
+process of using ChatGPT reliably mines and tabulates diverse MOF synthesis
+information in a unified format, while using only narrative language requiring
+no coding expertise, we anticipate that our ChatGPT Chemistry Assistant will be
+very useful across various other chemistry sub-disciplines.
+"
+Identifying and Extracting Rare Disease Phenotypes with Large Language  Models,Cathy Shyr,http://arxiv.org/pdf/2306.12656v1.pdf,2023-06-22,"['cs.cl', 'cs.ai']",2306.12656v1.pdf,"  Rare diseases (RDs) are collectively common and affect 300 million people
+worldwide. Accurate phenotyping is critical for informing diagnosis and
+treatment, but RD phenotypes are often embedded in unstructured text and
+time-consuming to extract manually. While natural language processing (NLP)
+models can perform named entity recognition (NER) to automate extraction, a
+major bottleneck is the development of a large, annotated corpus for model
+training. Recently, prompt learning emerged as an NLP paradigm that can lead to
+more generalizable results without any (zero-shot) or few labeled samples
+(few-shot). Despite growing interest in ChatGPT, a revolutionary large language
+model capable of following complex human prompts and generating high-quality
+responses, none have studied its NER performance for RDs in the zero- and
+few-shot settings. To this end, we engineered novel prompts aimed at extracting
+RD phenotypes and, to the best of our knowledge, are the first the establish a
+benchmark for evaluating ChatGPT's performance in these settings. We compared
+its performance to the traditional fine-tuning approach and conducted an
+in-depth error analysis. Overall, fine-tuning BioClinicalBERT resulted in
+higher performance (F1 of 0.689) than ChatGPT (F1 of 0.472 and 0.591 in the
+zero- and few-shot settings, respectively). Despite this, ChatGPT achieved
+similar or higher accuracy for certain entities (i.e., rare diseases and signs)
+in the one-shot setting (F1 of 0.776 and 0.725). This suggests that with
+appropriate prompt engineering, ChatGPT has the potential to match or
+outperform fine-tuned language models for certain entity types with just one
+labeled sample. While the proliferation of large language models may provide
+opportunities for supporting RD diagnosis and treatment, researchers and
+clinicians should critically evaluate model outputs and be well-informed of
+their limitations.
+"
+Demonstrations of the Potential of AI-based Political Issue Polling,Nathan E. Sanders,http://arxiv.org/pdf/2307.04781v2.pdf,2023-07-10,['cs.cy'],2307.04781v2.pdf,"  Political polling is a multi-billion dollar industry with outsized influence
+on the societal trajectory of the United States and nations around the world.
+However, it has been challenged by factors that stress its cost, availability,
+and accuracy. At the same time, artificial intelligence (AI) chatbots have
+become compelling stand-ins for human behavior, powered by increasingly
+sophisticated large language models (LLMs). Could AI chatbots be an effective
+tool for anticipating public opinion on controversial issues to the extent that
+they could be used by campaigns, interest groups, and polling firms? We have
+developed a prompt engineering methodology for eliciting human-like survey
+responses from ChatGPT, which simulate the response to a policy question of a
+person described by a set of demographic factors, and produce both an ordinal
+numeric response score and a textual justification. We execute large scale
+experiments, querying for thousands of simulated responses at a cost far lower
+than human surveys. We compare simulated data to human issue polling data from
+the Cooperative Election Study (CES). We find that ChatGPT is effective at
+anticipating both the mean level and distribution of public opinion on a
+variety of policy issues such as abortion bans and approval of the US Supreme
+Court, particularly in their ideological breakdown (correlation typically
+>85%). However, it is less successful at anticipating demographic-level
+differences. Moreover, ChatGPT tends to overgeneralize to new policy issues
+that arose after its training data was collected, such as US support for
+involvement in the war in Ukraine. Our work has implications for our
+understanding of the strengths and limitations of the current generation of AI
+chatbots as virtual publics or online listening platforms, future directions
+for LLM development, and applications of AI tools to the political domain.
+(Abridged)
+"
+Go Beyond The Obvious: Probing the gap of INFORMAL reasoning ability  between Humanity and LLMs by Detective Reasoning Puzzle Benchmark,Zhouhon Gu,http://arxiv.org/pdf/2307.05113v2.pdf,2023-07-11,['cs.cl'],2307.05113v2.pdf,"  Informal reasoning ability is the ability to reason based on common sense,
+experience, and intuition.Humans use informal reasoning every day to extract
+the most influential elements for their decision-making from a large amount of
+life-like information.With the rapid development of language models, the
+realization of general artificial intelligence has emerged with hope. Given the
+outstanding informal reasoning ability of humans, how much informal reasoning
+ability language models have has not been well studied by scholars.In order to
+explore the gap between humans and language models in informal reasoning
+ability, this paper constructs a Detective Reasoning Benchmark, which is an
+assembly of 1,200 questions gathered from accessible online resources, aims at
+evaluating the model's informal reasoning ability in real-life
+context.Considering the improvement of the model's informal reasoning ability
+restricted by the lack of benchmark, we further propose a Self-Question Prompt
+Framework that mimics human thinking to enhance the model's informal reasoning
+ability.The goals of self-question are to find key elements, deeply investigate
+the connections between these elements, encourage the relationship between each
+element and the problem, and finally, require the model to reasonably answer
+the problem.The experimental results show that human performance greatly
+outperforms the SoTA Language Models in Detective Reasoning Benchmark.Besides,
+Self-Question is proven to be the most effective prompt engineering in
+improving GPT-4's informal reasoning ability, but it still does not even
+surpass the lowest score made by human participants.Upon acceptance of the
+paper, the source code for the benchmark will be made publicly accessible.
+"
+Benchmarking Causal Study to Interpret Large Language Models for Source  Code,Daniel Rodriguez-Cardenas,http://arxiv.org/pdf/2308.12415v1.pdf,2023-08-23,"['cs.se', 'cs.ai']",2308.12415v1.pdf,"  One of the most common solutions adopted by software researchers to address
+code generation is by training Large Language Models (LLMs) on massive amounts
+of source code. Although a number of studies have shown that LLMs have been
+effectively evaluated on popular accuracy metrics (e.g., BLEU, CodeBleu),
+previous research has largely overlooked the role of Causal Inference as a
+fundamental component of the interpretability of LLMs' performance. Existing
+benchmarks and datasets are meant to highlight the difference between the
+expected and the generated outcome, but do not take into account confounding
+variables (e.g., lines of code, prompt size) that equally influence the
+accuracy metrics. The fact remains that, when dealing with generative software
+tasks by LLMs, no benchmark is available to tell researchers how to quantify
+neither the causal effect of SE-based treatments nor the correlation of
+confounders to the model's performance. In an effort to bring statistical rigor
+to the evaluation of LLMs, this paper introduces a benchmarking strategy named
+Galeras comprised of curated testbeds for three SE tasks (i.e., code
+completion, code summarization, and commit generation) to help aid the
+interpretation of LLMs' performance. We illustrate the insights of our
+benchmarking strategy by conducting a case study on the performance of ChatGPT
+under distinct prompt engineering methods. The results of the case study
+demonstrate the positive causal influence of prompt semantics on ChatGPT's
+generative performance by an average treatment effect of $\approx 3\%$.
+Moreover, it was found that confounders such as prompt size are highly
+correlated with accuracy metrics ($\approx 0.412\%$). The end result of our
+case study is to showcase causal inference evaluations, in practice, to reduce
+confounding bias. By reducing the bias, we offer an interpretable solution for
+the accuracy metric under analysis.
+"
+GPTCloneBench: A comprehensive benchmark of semantic clones and  cross-language clones using GPT-3 model and SemanticCloneBench,Ajmain Inqiad Alam,http://arxiv.org/pdf/2308.13963v2.pdf,2023-08-26,['cs.se'],2308.13963v2.pdf,"  With the emergence of Machine Learning, there has been a surge in leveraging
+its capabilities for problem-solving across various domains. In the code clone
+realm, the identification of type-4 or semantic clones has emerged as a crucial
+yet challenging task. Researchers aim to utilize Machine Learning to tackle
+this challenge, often relying on the BigCloneBench dataset. However, it's worth
+noting that BigCloneBench, originally not designed for semantic clone
+detection, presents several limitations that hinder its suitability as a
+comprehensive training dataset for this specific purpose. Furthermore, CLCDSA
+dataset suffers from a lack of reusable examples aligning with real-world
+software systems, rendering it inadequate for cross-language clone detection
+approaches. In this work, we present a comprehensive semantic clone and
+cross-language clone benchmark, GPTCloneBench by exploiting SemanticCloneBench
+and OpenAI's GPT-3 model. In particular, using code fragments from
+SemanticCloneBench as sample inputs along with appropriate prompt engineering
+for GPT-3 model, we generate semantic and cross-language clones for these
+specific fragments and then conduct a combination of extensive manual analysis,
+tool-assisted filtering, functionality testing and automated validation in
+building the benchmark. From 79,928 clone pairs of GPT-3 output, we created a
+benchmark with 37,149 true semantic clone pairs, 19,288 false semantic
+pairs(Type-1/Type-2), and 20,770 cross-language clones across four languages
+(Java, C, C#, and Python). Our benchmark is 15-fold larger than
+SemanticCloneBench, has more functional code examples for software systems and
+programming language support than CLCDSA, and overcomes BigCloneBench's
+qualities, quantification, and language variety limitations.
+"
+"AI Foundation Models for Weather and Climate: Applications, Design, and  Implementation",S. Karthik Mukkavilli,http://arxiv.org/pdf/2309.10808v2.pdf,2023-09-19,"['cs.lg', 'cs.ai', 'physics.ao-ph', '68t07 (primary), 68t01, 86a08', 'i.2.0; i.4.0; j.2.5']",2309.10808v2.pdf,"  Machine learning and deep learning methods have been widely explored in
+understanding the chaotic behavior of the atmosphere and furthering weather
+forecasting. There has been increasing interest from technology companies,
+government institutions, and meteorological agencies in building digital twins
+of the Earth. Recent approaches using transformers, physics-informed machine
+learning, and graph neural networks have demonstrated state-of-the-art
+performance on relatively narrow spatiotemporal scales and specific tasks. With
+the recent success of generative artificial intelligence (AI) using pre-trained
+transformers for language modeling and vision with prompt engineering and
+fine-tuning, we are now moving towards generalizable AI. In particular, we are
+witnessing the rise of AI foundation models that can perform competitively on
+multiple domain-specific downstream tasks. Despite this progress, we are still
+in the nascent stages of a generalizable AI model for global Earth system
+models, regional climate models, and mesoscale weather models. Here, we review
+current state-of-the-art AI approaches, primarily from transformer and operator
+learning literature in the context of meteorology. We provide our perspective
+on criteria for success towards a family of foundation models for nowcasting
+and forecasting weather and climate predictions. We also discuss how such
+models can perform competitively on downstream tasks such as downscaling
+(super-resolution), identifying conditions conducive to the occurrence of
+wildfires, and predicting consequential meteorological phenomena across various
+spatiotemporal scales such as hurricanes and atmospheric rivers. In particular,
+we examine current AI methodologies and contend they have matured enough to
+design and implement a weather foundation model.
+"
+Exploring Small Language Models with Prompt-Learning Paradigm for  Efficient Domain-Specific Text Classification,Hengyu Luo,http://arxiv.org/pdf/2309.14779v1.pdf,2023-09-26,"['cs.cl', 'cs.ai', 'cs.lg']",2309.14779v1.pdf,"  Domain-specific text classification faces the challenge of scarce labeled
+data due to the high cost of manual labeling. Prompt-learning, known for its
+efficiency in few-shot scenarios, is proposed as an alternative to traditional
+fine-tuning methods. And besides, although large language models (LLMs) have
+gained prominence, small language models (SLMs, with under 1B parameters) offer
+significant customizability, adaptability, and cost-effectiveness for
+domain-specific tasks, given industry constraints. In this study, we
+investigate the potential of SLMs combined with prompt-learning paradigm for
+domain-specific text classification, specifically within customer-agent
+interactions in retail. Our evaluations show that, in few-shot settings when
+prompt-based model fine-tuning is possible, T5-base, a typical SLM with 220M
+parameters, achieve approximately 75% accuracy with limited labeled data (up to
+15% of full data), which shows great potentials of SLMs with prompt-learning.
+Based on this, We further validate the effectiveness of active few-shot
+sampling and the ensemble strategy in the prompt-learning pipeline that
+contribute to a remarkable performance gain. Besides, in zero-shot settings
+with a fixed model, we underscore a pivotal observation that, although the
+GPT-3.5-turbo equipped with around 154B parameters garners an accuracy of
+55.16%, the power of well designed prompts becomes evident when the
+FLAN-T5-large, a model with a mere 0.5% of GPT-3.5-turbo's parameters, achieves
+an accuracy exceeding 31% with the optimized prompt, a leap from its sub-18%
+performance with an unoptimized one. Our findings underscore the promise of
+prompt-learning in classification tasks with SLMs, emphasizing the benefits of
+active few-shot sampling, and ensemble strategies in few-shot settings, and the
+importance of prompt engineering in zero-shot settings.
+"
+Label Supervised LLaMA Finetuning,Zongxi Li,http://arxiv.org/pdf/2310.01208v1.pdf,2023-10-02,['cs.cl'],2310.01208v1.pdf,"  The recent success of Large Language Models (LLMs) has gained significant
+attention in both academia and industry. Substantial efforts have been made to
+enhance the zero- and few-shot generalization capabilities of open-source LLMs
+through finetuning. Currently, the prevailing approach is instruction-tuning,
+which trains LLMs to complete real-world tasks by generating responses guided
+by natural language instructions. It is worth noticing that such an approach
+may underperform in sequence and token classification tasks. Unlike text
+generation tasks, classification tasks have a limited label space, where
+precise label prediction is more appreciated than generating diverse and
+human-like responses. Prior research has unveiled that instruction-tuned LLMs
+cannot outperform BERT, prompting us to explore the potential of leveraging
+latent representations from LLMs for supervised label prediction. In this
+paper, we introduce a label-supervised adaptation for LLMs, which aims to
+finetuning the model with discriminant labels. We evaluate this approach with
+Label Supervised LLaMA (LS-LLaMA), based on LLaMA-2-7B, a relatively
+small-scale LLM, and can be finetuned on a single GeForce RTX4090 GPU. We
+extract latent representations from the final LLaMA layer and project them into
+the label space to compute the cross-entropy loss. The model is finetuned by
+Low-Rank Adaptation (LoRA) to minimize this loss. Remarkably, without intricate
+prompt engineering or external knowledge, LS-LLaMA substantially outperforms
+LLMs ten times its size in scale and demonstrates consistent improvements
+compared to robust baselines like BERT-Large and RoBERTa-Large in text
+classification. Moreover, by removing the causal mask from decoders, LS-unLLaMA
+achieves the state-of-the-art performance in named entity recognition (NER).
+Our work will shed light on a novel approach to adapting LLMs for various
+downstream tasks.
+"
+Mini-DALLE3: Interactive Text to Image by Prompting Large Language  Models,Zeqiang Lai,http://arxiv.org/pdf/2310.07653v2.pdf,2023-10-11,['cs.ai'],2310.07653v2.pdf,"  The revolution of artificial intelligence content generation has been rapidly
+accelerated with the booming text-to-image (T2I) diffusion models. Within just
+two years of development, it was unprecedentedly of high-quality, diversity,
+and creativity that the state-of-the-art models could generate. However, a
+prevalent limitation persists in the effective communication with these popular
+T2I models, such as Stable Diffusion, using natural language descriptions. This
+typically makes an engaging image hard to obtain without expertise in prompt
+engineering with complex word compositions, magic tags, and annotations.
+Inspired by the recently released DALLE3 - a T2I model directly built-in
+ChatGPT that talks human language, we revisit the existing T2I systems
+endeavoring to align human intent and introduce a new task - interactive text
+to image (iT2I), where people can interact with LLM for interleaved
+high-quality image generation/edit/refinement and question answering with
+stronger images and text correspondences using natural language. In addressing
+the iT2I problem, we present a simple approach that augments LLMs for iT2I with
+prompting techniques and off-the-shelf T2I models. We evaluate our approach for
+iT2I in a variety of common-used scenarios under different LLMs, e.g., ChatGPT,
+LLAMA, Baichuan, and InternLM. We demonstrate that our approach could be a
+convenient and low-cost way to introduce the iT2I ability for any existing LLMs
+and any text-to-image models without any training while bringing little
+degradation on LLMs' inherent capabilities in, e.g., question answering and
+code generation. We hope this work could draw broader attention and provide
+inspiration for boosting user experience in human-machine interactions
+alongside the image quality of the next-generation T2I systems.
+"
+Promptor: A Conversational and Autonomous Prompt Generation Agent for  Intelligent Text Entry Techniques,Junxiao Shen,http://arxiv.org/pdf/2310.08101v2.pdf,2023-10-12,"['cs.cl', 'cs.ai']",2310.08101v2.pdf,"  Text entry is an essential task in our day-to-day digital interactions.
+Numerous intelligent features have been developed to streamline this process,
+making text entry more effective, efficient, and fluid. These improvements
+include sentence prediction and user personalization. However, as deep
+learning-based language models become the norm for these advanced features, the
+necessity for data collection and model fine-tuning increases. These challenges
+can be mitigated by harnessing the in-context learning capability of large
+language models such as GPT-3.5. This unique feature allows the language model
+to acquire new skills through prompts, eliminating the need for data collection
+and fine-tuning. Consequently, large language models can learn various text
+prediction techniques. We initially showed that, for a sentence prediction
+task, merely prompting GPT-3.5 surpassed a GPT-2 backed system and is
+comparable with a fine-tuned GPT-3.5 model, with the latter two methods
+requiring costly data collection, fine-tuning and post-processing. However, the
+task of prompting large language models to specialize in specific text
+prediction tasks can be challenging, particularly for designers without
+expertise in prompt engineering. To address this, we introduce Promptor, a
+conversational prompt generation agent designed to engage proactively with
+designers. Promptor can automatically generate complex prompts tailored to meet
+specific needs, thus offering a solution to this challenge. We conducted a user
+study involving 24 participants creating prompts for three intelligent text
+entry tasks, half of the participants used Promptor while the other half
+designed prompts themselves. The results show that Promptor-designed prompts
+result in a 35% increase in similarity and 22% in coherence over those by
+designers.
+"
+Human-in-the-loop Machine Translation with Large Language Model,Xinyi Yang,http://arxiv.org/pdf/2310.08908v1.pdf,2023-10-13,['cs.cl'],2310.08908v1.pdf,"  The large language model (LLM) has garnered significant attention due to its
+in-context learning mechanisms and emergent capabilities. The research
+community has conducted several pilot studies to apply LLMs to machine
+translation tasks and evaluate their performance from diverse perspectives.
+However, previous research has primarily focused on the LLM itself and has not
+explored human intervention in the inference process of LLM. The
+characteristics of LLM, such as in-context learning and prompt engineering,
+closely mirror human cognitive abilities in language tasks, offering an
+intuitive solution for human-in-the-loop generation. In this study, we propose
+a human-in-the-loop pipeline that guides LLMs to produce customized outputs
+with revision instructions. The pipeline initiates by prompting the LLM to
+produce a draft translation, followed by the utilization of automatic retrieval
+or human feedback as supervision signals to enhance the LLM's translation
+through in-context learning. The human-machine interactions generated in this
+pipeline are also stored in an external database to expand the in-context
+retrieval database, enabling us to leverage human supervision in an offline
+setting. We evaluate the proposed pipeline using GPT-3.5-turbo API on five
+domain-specific benchmarks for German-English translation. The results
+demonstrate the effectiveness of the pipeline in tailoring in-domain
+translations and improving translation performance compared to direct
+translation. Additionally, we discuss the results from the following
+perspectives: 1) the effectiveness of different in-context retrieval methods;
+2) the construction of a retrieval database under low-resource scenarios; 3)
+the observed domains differences; 4) the quantitative analysis of linguistic
+statistics; and 5) the qualitative analysis of translation cases. The code and
+data are available at https://github.com/NLP2CT/HIL-MT/.
+"
+ConstitutionMaker: Interactively Critiquing Large Language Models by  Converting Feedback into Principles,Savvas Petridis,http://arxiv.org/pdf/2310.15428v1.pdf,2023-10-24,"['cs.hc', 'cs.ai']",2310.15428v1.pdf,"  Large language model (LLM) prompting is a promising new approach for users to
+create and customize their own chatbots. However, current methods for steering
+a chatbot's outputs, such as prompt engineering and fine-tuning, do not support
+users in converting their natural feedback on the model's outputs to changes in
+the prompt or model. In this work, we explore how to enable users to
+interactively refine model outputs through their feedback, by helping them
+convert their feedback into a set of principles (i.e. a constitution) that
+dictate the model's behavior. From a formative study, we (1) found that users
+needed support converting their feedback into principles for the chatbot and
+(2) classified the different principle types desired by users. Inspired by
+these findings, we developed ConstitutionMaker, an interactive tool for
+converting user feedback into principles, to steer LLM-based chatbots. With
+ConstitutionMaker, users can provide either positive or negative feedback in
+natural language, select auto-generated feedback, or rewrite the chatbot's
+response; each mode of feedback automatically generates a principle that is
+inserted into the chatbot's prompt. In a user study with 14 participants, we
+compare ConstitutionMaker to an ablated version, where users write their own
+principles. With ConstitutionMaker, participants felt that their principles
+could better guide the chatbot, that they could more easily convert their
+feedback into principles, and that they could write principles more
+efficiently, with less mental demand. ConstitutionMaker helped users identify
+ways to improve the chatbot, formulate their intuitive responses to the model
+into feedback, and convert this feedback into specific and clear principles.
+Together, these findings inform future tools that support the interactive
+critiquing of LLM outputs.
+"
+Few-shot learning for sentence pair classification and its applications  in software engineering,Robert Kraig Helmeczi,http://arxiv.org/pdf/2306.08058v1.pdf,2023-06-13,['cs.se'],2306.08058v1.pdf,"  Few-shot learning-the ability to train models with access to limited data-has
+become increasingly popular in the natural language processing (NLP) domain, as
+large language models such as GPT and T0 have been empirically shown to achieve
+high performance in numerous tasks with access to just a handful of labeled
+examples. Smaller language models such as BERT and its variants have also been
+shown to achieve strong performance with just a handful of labeled examples
+when combined with few-shot learning algorithms like pattern-exploiting
+training (PET) and SetFit. The focus of this work is to investigate the
+performance of alternative few-shot learning approaches with BERT-based models.
+Specifically, vanilla fine-tuning, PET and SetFit are compared for numerous
+BERT-based checkpoints over an array of training set sizes. To facilitate this
+investigation, applications of few-shot learning are considered in software
+engineering. For each task, high-performance techniques and their associated
+model checkpoints are identified through detailed empirical analysis. Our
+results establish PET as a strong few-shot learning approach, and our analysis
+shows that with just a few hundred labeled examples it can achieve performance
+near that of fine-tuning on full-sized data sets.
+"
+FewCLUE: A Chinese Few-shot Learning Evaluation Benchmark,Liang Xu,http://arxiv.org/pdf/2107.07498v2.pdf,2021-07-15,"['cs.cl', 'cs.ai']",2107.07498v2.pdf,"  Pretrained Language Models (PLMs) have achieved tremendous success in natural
+language understanding tasks. While different learning schemes -- fine-tuning,
+zero-shot, and few-shot learning -- have been widely explored and compared for
+languages such as English, there is comparatively little work in Chinese to
+fairly and comprehensively evaluate and compare these methods and thus hinders
+cumulative progress. In this paper, we introduce the Chinese Few-shot Learning
+Evaluation Benchmark (FewCLUE), the first comprehensive few-shot evaluation
+benchmark in Chinese. It includes nine tasks, ranging from single-sentence and
+sentence-pair classification tasks to machine reading comprehension tasks. We
+systematically evaluate five state-of-the-art (SOTA) few-shot learning methods
+(including PET, ADAPET, LM-BFF, P-tuning and EFL), and compare their
+performance with fine-tuning and zero-shot learning schemes on the newly
+constructed FewCLUE benchmark. Experimental results reveal that: 1) The effect
+of different few-shot learning methods is sensitive to the pre-trained model to
+which the methods are applied; 2) PET and P-tuning achieve the best overall
+performance with RoBERTa and ERNIE respectively. Our benchmark is used in the
+few-shot learning contest of NLPCC 2021. In addition, we provide a
+user-friendly toolkit, as well as an online leaderboard to help facilitate
+further progress on Chinese few-shot learning. We provide a baseline
+performance on different learning methods, a reference for future research.
+"
+Proto-CLIP: Vision-Language Prototypical Network for Few-Shot Learning,Jishnu Jaykumar P,http://arxiv.org/pdf/2307.03073v2.pdf,2023-07-06,"['cs.cv', 'cs.ro']",2307.03073v2.pdf,"  We propose a novel framework for few-shot learning by leveraging large-scale
+vision-language models such as CLIP. Motivated by the unimodal prototypical
+networks for few-shot learning, we introduce PROTO-CLIP that utilizes image
+prototypes and text prototypes for few-shot learning. Specifically, PROTO-CLIP
+adapts the image encoder and text encoder in CLIP in a joint fashion using
+few-shot examples. The two encoders are used to compute prototypes of image
+classes for classification. During adaptation, we propose aligning the image
+and text prototypes of corresponding classes. Such a proposed alignment is
+beneficial for few-shot classification due to the contributions from both types
+of prototypes. We demonstrate the effectiveness of our method by conducting
+experiments on benchmark datasets for few-shot learning as well as in the real
+world for robot perception.
+"
+A Survey on Recent Named Entity Recognition and Relation Classification  Methods with Focus on Few-Shot Learning Approaches,Sakher Alqaaidi,http://arxiv.org/pdf/2310.19055v1.pdf,2023-10-29,['cs.cl'],2310.19055v1.pdf,"  Named entity recognition and relation classification are key stages for
+extracting information from unstructured text. Several natural language
+processing applications utilize the two tasks, such as information retrieval,
+knowledge graph construction and completion, question answering and other
+domain-specific applications, such as biomedical data mining. We present a
+survey of recent approaches in the two tasks with focus on few-shot learning
+approaches. Our work compares the main approaches followed in the two
+paradigms. Additionally, we report the latest metric scores in the two tasks
+with a structured analysis that considers the results in the few-shot learning
+scope.
+"
+True Few-Shot Learning with Prompts -- A Real-World Perspective,Timo Schick,http://arxiv.org/pdf/2111.13440v1.pdf,2021-11-26,['cs.cl'],2111.13440v1.pdf,"  Prompt-based approaches are strong at few-shot learning. However, Perez et
+al. (2021) have recently cast doubt on their performance because they had
+difficulty getting good results in a ""true"" few-shot setting in which prompts
+and hyperparameters cannot be tuned on a dev set. In view of this, we conduct
+an extensive study of PET, a method that combines textual instructions with
+example-based finetuning. We show that, if correctly configured, PET performs
+strongly in a true few-shot setting, i.e., without a dev set. Crucial for this
+strong performance is PET's ability to intelligently handle multiple prompts.
+We then put our findings to a real-world test by running PET on RAFT, a
+benchmark of tasks taken directly from realistic NLP applications for which no
+labeled dev or test sets are available. PET achieves a new state of the art on
+RAFT and performs close to non-expert humans for 7 out of 11 tasks. These
+results demonstrate that prompt-based learners like PET excel at true few-shot
+learning and underpin our belief that learning from instructions will play an
+important role on the path towards human-like few-shot learning capabilities.
+"
+Improving In-Context Few-Shot Learning via Self-Supervised Training,Mingda Chen,http://arxiv.org/pdf/2205.01703v2.pdf,2022-05-03,['cs.cl'],2205.01703v2.pdf,"  Self-supervised pretraining has made few-shot learning possible for many NLP
+tasks. But the pretraining objectives are not typically adapted specifically
+for in-context few-shot learning. In this paper, we propose to use
+self-supervision in an intermediate training stage between pretraining and
+downstream few-shot usage with the goal to teach the model to perform
+in-context few shot learning. We propose and evaluate four self-supervised
+objectives on two benchmarks. We find that the intermediate self-supervision
+stage produces models that outperform strong baselines. Ablation study shows
+that several factors affect the downstream performance, such as the amount of
+training data and the diversity of the self-supervised objectives.
+Human-annotated cross-task supervision and self-supervision are complementary.
+Qualitative analysis suggests that the self-supervised-trained models are
+better at following task requirements.
+"
+Prompting ELECTRA: Few-Shot Learning with Discriminative Pre-Trained  Models,Mengzhou Xia,http://arxiv.org/pdf/2205.15223v3.pdf,2022-05-30,"['cs.cl', 'cs.lg']",2205.15223v3.pdf,"  Pre-trained masked language models successfully perform few-shot learning by
+formulating downstream tasks as text infilling. However, as a strong
+alternative in full-shot settings, discriminative pre-trained models like
+ELECTRA do not fit into the paradigm. In this work, we adapt prompt-based
+few-shot learning to ELECTRA and show that it outperforms masked language
+models in a wide range of tasks. ELECTRA is pre-trained to distinguish if a
+token is generated or original. We naturally extend that to prompt-based
+few-shot learning by training to score the originality of the target options
+without introducing new parameters. Our method can be easily adapted to tasks
+involving multi-token predictions without extra computation overhead. Analysis
+shows that ELECTRA learns distributions that align better with downstream
+tasks.
+"
+Revisiting Few-Shot Learning from a Causal Perspective,Guoliang Lin,http://arxiv.org/pdf/2209.13816v1.pdf,2022-09-28,"['cs.lg', 'cs.ai']",2209.13816v1.pdf,"  Few-shot learning with N-way K-shot scheme is an open challenge in machine
+learning. Many approaches have been proposed to tackle this problem, e.g., the
+Matching Networks and CLIP-Adapter. Despite that these approaches have shown
+significant progress, the mechanism of why these methods succeed has not been
+well explored. In this paper, we interpret these few-shot learning methods via
+causal mechanism. We show that the existing approaches can be viewed as
+specific forms of front-door adjustment, which is to remove the effects of
+confounders. Based on this, we introduce a general causal method for few-shot
+learning, which considers not only the relationship between examples but also
+the diversity of representations. Experimental results demonstrate the
+superiority of our proposed method in few-shot classification on various
+benchmark datasets. Code is available in the supplementary material.
+"
+In-context Learning Distillation: Transferring Few-shot Learning Ability  of Pre-trained Language Models,Yukun Huang,http://arxiv.org/pdf/2212.10670v1.pdf,2022-12-20,"['cs.cl', 'cs.lg']",2212.10670v1.pdf,"  Given the success with in-context learning of large pre-trained language
+models, we introduce in-context learning distillation to transfer in-context
+few-shot learning ability from large models to smaller models. We propose to
+combine in-context learning objectives with language modeling objectives to
+distill both the ability to read in-context examples and task knowledge to the
+smaller models. We perform in-context learning distillation under two different
+few-shot learning paradigms: Meta In-context Tuning (Meta-ICT) and Multitask
+In-context Tuning (Multitask-ICT). Multitask-ICT performs better on multitask
+few-shot learning but also requires more computation than Meta-ICT. Our method
+shows consistent improvements for both Meta-ICT and Multitask-ICT on two
+benchmarks: LAMA and CrossFit. Our extensive experiments and analysis reveal
+that in-context learning objectives and language modeling objectives are
+complementary under the Multitask-ICT paradigm. In-context learning objectives
+achieve the best performance when combined with language modeling objectives.
+"
+FILM: How can Few-Shot Image Classification Benefit from Pre-Trained  Language Models?,Zihao Jiang,http://arxiv.org/pdf/2307.04114v1.pdf,2023-07-09,"['cs.lg', 'cs.ai', 'cs.cl', 'cs.cv', 'cs.mm']",2307.04114v1.pdf,"  Few-shot learning aims to train models that can be generalized to novel
+classes with only a few samples. Recently, a line of works are proposed to
+enhance few-shot learning with accessible semantic information from class
+names. However, these works focus on improving existing modules such as visual
+prototypes and feature extractors of the standard few-shot learning framework.
+This limits the full potential use of semantic information. In this paper, we
+propose a novel few-shot learning framework that uses pre-trained language
+models based on contrastive learning. To address the challenge of alignment
+between visual features and textual embeddings obtained from text-based
+pre-trained language model, we carefully design the textual branch of our
+framework and introduce a metric module to generalize the cosine similarity.
+For better transferability, we let the metric module adapt to different
+few-shot tasks and adopt MAML to train the model via bi-level optimization.
+Moreover, we conduct extensive experiments on multiple benchmarks to
+demonstrate the effectiveness of our method.
+"
+Reordering Examples Helps during Priming-based Few-Shot Learning,Sawan Kumar,http://arxiv.org/pdf/2106.01751v1.pdf,2021-06-03,['cs.cl'],2106.01751v1.pdf,"  The ability to learn from limited data, or few-shot learning, is a desirable
+and often critical requirement for NLP systems. While many existing methods do
+poorly at learning from a handful of examples, large pretrained language models
+have recently been shown to be efficient few-shot learners. One approach to
+few-shot learning, which does not require finetuning of model parameters, is to
+augment the language model's input with priming text which is typically
+constructed using task specific descriptions and examples. In this work, we
+further explore priming-based few-shot learning, with focus on using examples
+as prompts. We show that presenting examples in the right order is key for
+generalization. We introduce PERO (Prompting with Examples in the Right Order),
+where we formulate few-shot learning as search over the set of permutations of
+the training examples. We show that PERO can learn to generalize efficiently
+using as few as 10 examples, in contrast to existing approaches. While the
+newline token is a natural choice for separating the examples in the prompt, we
+show that learning a new separator token can potentially provide further gains
+in performance. We demonstrate the effectiveness of the proposed method on the
+tasks of sentiment classification, natural language inference and fact
+retrieval. Finally, we analyze the learned prompts to reveal novel insights,
+including the idea that two training examples in the right order alone can
+provide competitive performance for sentiment classification and natural
+language inference.
+"
+CLUES: Few-Shot Learning Evaluation in Natural Language Understanding,Subhabrata Mukherjee,http://arxiv.org/pdf/2111.02570v1.pdf,2021-11-04,"['cs.cl', 'cs.lg']",2111.02570v1.pdf,"  Most recent progress in natural language understanding (NLU) has been driven,
+in part, by benchmarks such as GLUE, SuperGLUE, SQuAD, etc. In fact, many NLU
+models have now matched or exceeded ""human-level"" performance on many tasks in
+these benchmarks. Most of these benchmarks, however, give models access to
+relatively large amounts of labeled data for training. As such, the models are
+provided far more data than required by humans to achieve strong performance.
+That has motivated a line of work that focuses on improving few-shot learning
+performance of NLU models. However, there is a lack of standardized evaluation
+benchmarks for few-shot NLU resulting in different experimental settings in
+different papers. To help accelerate this line of work, we introduce CLUES
+(Constrained Language Understanding Evaluation Standard), a benchmark for
+evaluating the few-shot learning capabilities of NLU models. We demonstrate
+that while recent models reach human performance when they have access to large
+amounts of labeled data, there is a huge gap in performance in the few-shot
+setting for most tasks. We also demonstrate differences between alternative
+model families and adaptation techniques in the few shot setting. Finally, we
+discuss several principles and choices in designing the experimental settings
+for evaluating the true few-shot learning performance and suggest a unified
+standardized approach to few-shot learning evaluation. We aim to encourage
+research on NLU models that can generalize to new tasks with a small number of
+examples. Code and data for CLUES are available at
+https://github.com/microsoft/CLUES.
+"
+Tuning Language Models as Training Data Generators for  Augmentation-Enhanced Few-Shot Learning,Yu Meng,http://arxiv.org/pdf/2211.03044v2.pdf,2022-11-06,"['cs.cl', 'cs.lg']",2211.03044v2.pdf,"  Recent studies have revealed the intriguing few-shot learning ability of
+pretrained language models (PLMs): They can quickly adapt to a new task when
+fine-tuned on a small amount of labeled data formulated as prompts, without
+requiring abundant task-specific annotations. Despite their promising
+performance, most existing few-shot approaches that only learn from the small
+training set still underperform fully supervised training by nontrivial
+margins. In this work, we study few-shot learning with PLMs from a different
+perspective: We first tune an autoregressive PLM on the few-shot samples and
+then use it as a generator to synthesize a large amount of novel training
+samples which augment the original training set. To encourage the generator to
+produce label-discriminative samples, we train it via weighted maximum
+likelihood where the weight of each token is automatically adjusted based on a
+discriminative meta-learning objective. A classification PLM can then be
+fine-tuned on both the few-shot and the synthetic samples with regularization
+for better generalization and stability. Our approach FewGen achieves an
+overall better result across seven classification tasks of the GLUE benchmark
+than existing few-shot learning methods, improving no-augmentation methods by
+5+ average points, and outperforming augmentation methods by 3+ average points.
+"
+Improving Few-Shot Generalization by Exploring and Exploiting Auxiliary  Data,Alon Albalak,http://arxiv.org/pdf/2302.00674v4.pdf,2023-02-01,"['cs.lg', 'cs.cl']",2302.00674v4.pdf,"  Few-shot learning is valuable in many real-world applications, but learning a
+generalizable model without overfitting to the few labeled datapoints is
+challenging. In this work, we focus on Few-shot Learning with Auxiliary Data
+(FLAD), a training paradigm that assumes access to auxiliary data during
+few-shot learning in hopes of improving generalization. Previous works have
+proposed automated methods for mixing auxiliary and target data, but these
+methods typically scale linearly (or worse) with the number of auxiliary
+datasets, limiting their practicality. In this work we relate FLAD to the
+explore-exploit dilemma that is central to the multi-armed bandit setting and
+derive algorithms whose computational complexity is independent of the number
+of auxiliary datasets, allowing us to scale to 100x more auxiliary datasets
+than prior methods. We propose two algorithms -- EXP3-FLAD and UCB1-FLAD -- and
+compare them with prior FLAD methods that either explore or exploit, finding
+that the combination of exploration and exploitation is crucial. Through
+extensive experimentation we find that our methods outperform all pre-existing
+FLAD methods by 4% and lead to the first 3 billion parameter language models
+that outperform the 175 billion parameter GPT-3. Overall, our work suggests
+that the discovery of better, more efficient mixing strategies for FLAD may
+provide a viable path towards substantially improving generalization in
+few-shot learning.
+"
+Universal Few-shot Learning of Dense Prediction Tasks with Visual Token  Matching,Donggyun Kim,http://arxiv.org/pdf/2303.14969v1.pdf,2023-03-27,"['cs.cv', 'cs.ai']",2303.14969v1.pdf,"  Dense prediction tasks are a fundamental class of problems in computer
+vision. As supervised methods suffer from high pixel-wise labeling cost, a
+few-shot learning solution that can learn any dense task from a few labeled
+images is desired. Yet, current few-shot learning methods target a restricted
+set of tasks such as semantic segmentation, presumably due to challenges in
+designing a general and unified model that is able to flexibly and efficiently
+adapt to arbitrary tasks of unseen semantics. We propose Visual Token Matching
+(VTM), a universal few-shot learner for arbitrary dense prediction tasks. It
+employs non-parametric matching on patch-level embedded tokens of images and
+labels that encapsulates all tasks. Also, VTM flexibly adapts to any task with
+a tiny amount of task-specific parameters that modulate the matching algorithm.
+We implement VTM as a powerful hierarchical encoder-decoder architecture
+involving ViT backbones where token matching is performed at multiple feature
+hierarchies. We experiment VTM on a challenging variant of Taskonomy dataset
+and observe that it robustly few-shot learns various unseen dense prediction
+tasks. Surprisingly, it is competitive with fully supervised baselines using
+only 10 labeled examples of novel tasks (0.004% of full supervision) and
+sometimes outperforms using 0.1% of full supervision. Codes are available at
+https://github.com/GitGyun/visual_token_matching.
+"
+FD-Align: Feature Discrimination Alignment for Fine-tuning Pre-Trained  Models in Few-Shot Learning,Kun Song,http://arxiv.org/pdf/2310.15105v3.pdf,2023-10-23,['cs.cv'],2310.15105v3.pdf,"  Due to the limited availability of data, existing few-shot learning methods
+trained from scratch fail to achieve satisfactory performance. In contrast,
+large-scale pre-trained models such as CLIP demonstrate remarkable few-shot and
+zero-shot capabilities. To enhance the performance of pre-trained models for
+downstream tasks, fine-tuning the model on downstream data is frequently
+necessary. However, fine-tuning the pre-trained model leads to a decrease in
+its generalizability in the presence of distribution shift, while the limited
+number of samples in few-shot learning makes the model highly susceptible to
+overfitting. Consequently, existing methods for fine-tuning few-shot learning
+primarily focus on fine-tuning the model's classification head or introducing
+additional structure. In this paper, we introduce a fine-tuning approach termed
+Feature Discrimination Alignment (FD-Align). Our method aims to bolster the
+model's generalizability by preserving the consistency of spurious features
+across the fine-tuning process. Extensive experimental results validate the
+efficacy of our approach for both ID and OOD tasks. Once fine-tuned, the model
+can seamlessly integrate with existing methods, leading to performance
+improvements. Our code can be found in https://github.com/skingorz/FD-Align.
+"
+Few-Shot Learning with Localization in Realistic Settings,Davis Wertheimer,http://arxiv.org/pdf/1904.08502v2.pdf,2019-04-09,"['cs.cv', 'cs.ai', 'cs.lg', 'stat.ml']",1904.08502v2.pdf,"  Traditional recognition methods typically require large,
+artificially-balanced training classes, while few-shot learning methods are
+tested on artificially small ones. In contrast to both extremes, real world
+recognition problems exhibit heavy-tailed class distributions, with cluttered
+scenes and a mix of coarse and fine-grained class distinctions. We show that
+prior methods designed for few-shot learning do not work out of the box in
+these challenging conditions, based on a new ""meta-iNat"" benchmark. We
+introduce three parameter-free improvements: (a) better training procedures
+based on adapting cross-validation to meta-learning, (b) novel architectures
+that localize objects using limited bounding box annotations before
+classification, and (c) simple parameter-free expansions of the feature space
+based on bilinear pooling. Together, these improvements double the accuracy of
+state-of-the-art models on meta-iNat while generalizing to prior benchmarks,
+complex neural architectures, and settings with substantial domain shift.
+"
+Model-Agnostic Graph Regularization for Few-Shot Learning,Ethan Shen,http://arxiv.org/pdf/2102.07077v1.pdf,2021-02-14,"['cs.lg', 'cs.cv']",2102.07077v1.pdf,"  In many domains, relationships between categories are encoded in the
+knowledge graph. Recently, promising results have been achieved by
+incorporating knowledge graph as side information in hard classification tasks
+with severely limited data. However, prior models consist of highly complex
+architectures with many sub-components that all seem to impact performance. In
+this paper, we present a comprehensive empirical study on graph embedded
+few-shot learning. We introduce a graph regularization approach that allows a
+deeper understanding of the impact of incorporating graph information between
+labels. Our proposed regularization is widely applicable and model-agnostic,
+and boosts the performance of any few-shot learning model, including
+fine-tuning, metric-based, and optimization-based meta-learning. Our approach
+improves the performance of strong base learners by up to 2% on Mini-ImageNet
+and 6.7% on ImageNet-FS, outperforming state-of-the-art graph embedded methods.
+Additional analyses reveal that graph regularizing models result in a lower
+loss for more difficult tasks, such as those with fewer shots and less
+informative support examples.
+"
+Uniform Sampling over Episode Difficulty,Sébastien M. R. Arnold,http://arxiv.org/pdf/2108.01662v2.pdf,2021-08-03,"['cs.lg', 'cs.ai', 'cs.cv']",2108.01662v2.pdf,"  Episodic training is a core ingredient of few-shot learning to train models
+on tasks with limited labelled data. Despite its success, episodic training
+remains largely understudied, prompting us to ask the question: what is the
+best way to sample episodes? In this paper, we first propose a method to
+approximate episode sampling distributions based on their difficulty. Building
+on this method, we perform an extensive analysis and find that sampling
+uniformly over episode difficulty outperforms other sampling schemes, including
+curriculum and easy-/hard-mining. As the proposed sampling method is algorithm
+agnostic, we can leverage these insights to improve few-shot learning
+accuracies across many episodic training algorithms. We demonstrate the
+efficacy of our method across popular few-shot learning datasets, algorithms,
+network architectures, and protocols.
+"
+CINS: Comprehensive Instruction for Few-shot Learning in Task-oriented  Dialog Systems,Fei Mi,http://arxiv.org/pdf/2109.04645v4.pdf,2021-09-10,"['cs.cl', 'cs.lg']",2109.04645v4.pdf,"  As labeling cost for different modules in task-oriented dialog (ToD) systems
+is high, a major challenge in practice is to learn different tasks with the
+least amount of labeled data. Recently, prompting methods over pre-trained
+language models (PLMs) have shown promising results for few-shot learning in
+ToD. To better utilize the power of PLMs, this paper proposes Comprehensive
+Instruction (CINS) that exploits PLMs with extra task-specific instructions. We
+design a schema (definition, constraint, prompt) of instructions and their
+customized realizations for three important downstream tasks in ToD, i.e.
+intent classification, dialog state tracking, and natural language generation.
+A sequence-to-sequence model (T5) is adopted to solve these three tasks in a
+unified framework. Extensive experiments are conducted on these ToD tasks in
+realistic few-shot learning scenarios with small validation data. Empirical
+results demonstrate that the proposed CINS approach consistently improves
+techniques that finetune PLMs with raw input or short prompts.
+"
+Exploring Prompt-based Few-shot Learning for Grounded Dialog Generation,Chujie Zheng,http://arxiv.org/pdf/2109.06513v2.pdf,2021-09-14,['cs.cl'],2109.06513v2.pdf,"  Dialog models can be greatly strengthened through grounding on various
+external information, but grounded dialog corpora are usually not naturally
+accessible. In this work, we focus on the few-shot learning for grounded dialog
+generation (GDG). We first propose a simple prompting method for GDG tasks,
+where different constructs of model input, such as the grounding source and the
+conversation context, are distinguished through continuous or discrete prompts.
+On three typical GDG tasks, we empirically demonstrate and analyze in-depth the
+effectiveness of our method. We then conduct extensive experiments to
+thoroughly investigate how our prompting method works with different
+pre-trained models. We show that prompted language models perform superiorly to
+conversational models, and further analyze various factors that influence the
+effects of prompting. Overall, our work introduces a prompt-based perspective
+to the few-shot learning for GDG tasks, and provides valuable findings and
+insights for future research.
+"
+Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning,Sungyong Baik,http://arxiv.org/pdf/2110.03909v2.pdf,2021-10-08,"['cs.lg', 'cs.cv']",2110.03909v2.pdf,"  In few-shot learning scenarios, the challenge is to generalize and perform
+well on new unseen examples when only very few labeled examples are available
+for each task. Model-agnostic meta-learning (MAML) has gained the popularity as
+one of the representative few-shot learning methods for its flexibility and
+applicability to diverse problems. However, MAML and its variants often resort
+to a simple loss function without any auxiliary loss function or regularization
+terms that can help achieve better generalization. The problem lies in that
+each application and task may require different auxiliary loss function,
+especially when tasks are diverse and distinct. Instead of attempting to
+hand-design an auxiliary loss function for each application and task, we
+introduce a new meta-learning framework with a loss function that adapts to
+each task. Our proposed framework, named Meta-Learning with Task-Adaptive Loss
+Function (MeTAL), demonstrates the effectiveness and the flexibility across
+various domains, such as few-shot classification and few-shot regression.
+"
+Ontology-enhanced Prompt-tuning for Few-shot Learning,Hongbin Ye,http://arxiv.org/pdf/2201.11332v1.pdf,2022-01-27,"['cs.cl', 'cs.ai', 'cs.ir', 'cs.lg']",2201.11332v1.pdf,"  Few-shot Learning (FSL) is aimed to make predictions based on a limited
+number of samples. Structured data such as knowledge graphs and ontology
+libraries has been leveraged to benefit the few-shot setting in various tasks.
+However, the priors adopted by the existing methods suffer from challenging
+knowledge missing, knowledge noise, and knowledge heterogeneity, which hinder
+the performance for few-shot learning. In this study, we explore knowledge
+injection for FSL with pre-trained language models and propose
+ontology-enhanced prompt-tuning (OntoPrompt). Specifically, we develop the
+ontology transformation based on the external knowledge graph to address the
+knowledge missing issue, which fulfills and converts structure knowledge to
+text. We further introduce span-sensitive knowledge injection via a visible
+matrix to select informative knowledge to handle the knowledge noise issue. To
+bridge the gap between knowledge and text, we propose a collective training
+algorithm to optimize representations jointly. We evaluate our proposed
+OntoPrompt in three tasks, including relation extraction, event extraction, and
+knowledge graph completion, with eight datasets. Experimental results
+demonstrate that our approach can obtain better few-shot performance than
+baselines.
+"
+Impossible Triangle: What's Next for Pre-trained Language Models?,Chenguang Zhu,http://arxiv.org/pdf/2204.06130v2.pdf,2022-04-13,['cs.cl'],2204.06130v2.pdf,"  Recent development of large-scale pre-trained language models (PLM) have
+significantly improved the capability of models in various NLP tasks, in terms
+of performance after task-specific fine-tuning and zero-shot / few-shot
+learning. However, many of such models come with a dauntingly huge size that
+few institutions can afford to pre-train, fine-tune or even deploy, while
+moderate-sized models usually lack strong generalized few-shot learning
+capabilities. In this paper, we first elaborate the current obstacles of using
+PLM models in terms of the Impossible Triangle: 1) moderate model size, 2)
+state-of-the-art few-shot learning capability, and 3) state-of-the-art
+fine-tuning capability. We argue that all existing PLM models lack one or more
+properties from the Impossible Triangle. To remedy these missing properties of
+PLMs, various techniques have been proposed, such as knowledge distillation,
+data augmentation and prompt learning, which inevitably brings additional work
+to the application of PLMs in real scenarios. We then offer insights into
+future research directions of PLMs to achieve the Impossible Triangle, and
+break down the task into several key phases.
+"
+A Study on Prompt-based Few-Shot Learning Methods for Belief State  Tracking in Task-oriented Dialog Systems,Debjoy Saha,http://arxiv.org/pdf/2204.08167v1.pdf,2022-04-18,"['cs.cl', 'cs.ai']",2204.08167v1.pdf,"  We tackle the Dialogue Belief State Tracking(DST) problem of task-oriented
+conversational systems. Recent approaches to this problem leveraging
+Transformer-based models have yielded great results. However, training these
+models is expensive, both in terms of computational resources and time.
+Additionally, collecting high quality annotated dialogue datasets remains a
+challenge for researchers because of the extensive annotation required for
+training these models. Driven by the recent success of pre-trained language
+models and prompt-based learning, we explore prompt-based few-shot learning for
+Dialogue Belief State Tracking. We formulate the DST problem as a 2-stage
+prompt-based language modelling task and train language models for both tasks
+and present a comprehensive empirical analysis of their separate and joint
+performance. We demonstrate the potential of prompt-based methods in few-shot
+learning for DST and provide directions for future improvement.
+"
+How to Prompt? Opportunities and Challenges of Zero- and Few-Shot  Learning for Human-AI Interaction in Creative Applications of Generative  Models,Hai Dang,http://arxiv.org/pdf/2209.01390v1.pdf,2022-09-03,"['cs.hc', 'cs.cl', 'h.5.2; i.2.7']",2209.01390v1.pdf,"  Deep generative models have the potential to fundamentally change the way we
+create high-fidelity digital content but are often hard to control. Prompting a
+generative model is a promising recent development that in principle enables
+end-users to creatively leverage zero-shot and few-shot learning to assign new
+tasks to an AI ad-hoc, simply by writing them down. However, for the majority
+of end-users writing effective prompts is currently largely a trial and error
+process. To address this, we discuss the key opportunities and challenges for
+interactive creative applications that use prompting as a new paradigm for
+Human-AI interaction. Based on our analysis, we propose four design goals for
+user interfaces that support prompting. We illustrate these with concrete UI
+design sketches, focusing on the use case of creative writing. The research
+community in HCI and AI can take these as starting points to develop adequate
+user interfaces for models capable of zero- and few-shot learning.
+"
+On Measuring the Intrinsic Few-Shot Hardness of Datasets,Xinran Zhao,http://arxiv.org/pdf/2211.09113v1.pdf,2022-11-16,['cs.cl'],2211.09113v1.pdf,"  While advances in pre-training have led to dramatic improvements in few-shot
+learning of NLP tasks, there is limited understanding of what drives successful
+few-shot adaptation in datasets. In particular, given a new dataset and a
+pre-trained model, what properties of the dataset make it \emph{few-shot
+learnable} and are these properties independent of the specific adaptation
+techniques used? We consider an extensive set of recent few-shot learning
+methods, and show that their performance across a large number of datasets is
+highly correlated, showing that few-shot hardness may be intrinsic to datasets,
+for a given pre-trained model. To estimate intrinsic few-shot hardness, we then
+propose a simple and lightweight metric called ""Spread"" that captures the
+intuition that few-shot learning is made possible by exploiting feature-space
+invariances between training and test samples. Our metric better accounts for
+few-shot hardness compared to existing notions of hardness, and is ~8-100x
+faster to compute.
+"
+Differentiable Entailment for Parameter Efficient Few Shot Learning,Ethan Kim,http://arxiv.org/pdf/2301.13345v1.pdf,2023-01-31,['cs.cl'],2301.13345v1.pdf,"  Few-shot learning allows pre-trained language models to adapt to downstream
+tasks while using a limited number of training examples. However, practical
+applications are limited when all model parameters must be optimized. In this
+work we apply a new technique for parameter efficient few shot learning while
+adopting a strict definition of parameter efficiency. Our training method
+combines 1) intermediate training by reformulating natural language tasks as
+entailment tasks \cite{wang_entailment_2021} and 2) differentiable optimization
+of template and label tokens \cite{zhang_differentiable_2021}. We quantify the
+tradeoff between parameter efficiency and performance in the few-shot regime
+and propose a simple model agnostic approach that can be extended to any task
+By achieving competitive performance while only optimizing 3\% of a model's
+parameters and allowing for batched inference, we allow for more efficient
+practical deployment of models.
+"
+MerA: Merging Pretrained Adapters For Few-Shot Learning,Shwai He,http://arxiv.org/pdf/2308.15982v1.pdf,2023-08-30,['cs.cl'],2308.15982v1.pdf,"  Adapter tuning, which updates only a few parameters, has become a mainstream
+method for fine-tuning pretrained language models to downstream tasks. However,
+it often yields subpar results in few-shot learning. AdapterFusion, which
+assembles pretrained adapters using composition layers tailored to specific
+tasks, is a possible solution but significantly increases trainable parameters
+and deployment costs. Despite this, our preliminary study reveals that even
+single adapters can outperform Adapterfusion in few-shot learning, urging us to
+propose \textbf{\texttt{Merging Pretrained Adapters}} (MerA) that efficiently
+incorporates pretrained adapters to a single model through model fusion.
+Extensive experiments on two PLMs demonstrate that MerA achieves substantial
+improvements compared to both single adapters and AdapterFusion. To further
+enhance the capacity of MerA, we also introduce a simple yet effective
+technique, referred to as the ""\textit{same-track}"" setting, that merges
+adapters from the same track of pretraining tasks. With the implementation of
+the ""\textit{same-track}"" setting, we observe even more impressive gains,
+surpassing the performance of both full fine-tuning and adapter tuning by a
+substantial margin, e.g., 3.5\% in MRPC and 5.0\% in MNLI.
+"
+Meta-Adapter: An Online Few-shot Learner for Vision-Language Model,Cheng Cheng,http://arxiv.org/pdf/2311.03774v1.pdf,2023-11-07,['cs.cv'],2311.03774v1.pdf,"  The contrastive vision-language pre-training, known as CLIP, demonstrates
+remarkable potential in perceiving open-world visual concepts, enabling
+effective zero-shot image recognition. Nevertheless, few-shot learning methods
+based on CLIP typically require offline fine-tuning of the parameters on
+few-shot samples, resulting in longer inference time and the risk of
+over-fitting in certain domains. To tackle these challenges, we propose the
+Meta-Adapter, a lightweight residual-style adapter, to refine the CLIP features
+guided by the few-shot samples in an online manner. With a few training
+samples, our method can enable effective few-shot learning capabilities and
+generalize to unseen data or tasks without additional fine-tuning, achieving
+competitive performance and high efficiency. Without bells and whistles, our
+approach outperforms the state-of-the-art online few-shot learning method by an
+average of 3.6\% on eight image classification datasets with higher inference
+speed. Furthermore, our model is simple and flexible, serving as a
+plug-and-play module directly applicable to downstream tasks. Without further
+fine-tuning, Meta-Adapter obtains notable performance improvements in
+open-vocabulary object detection and segmentation tasks.
+"
+Pushing the Limits of Simple Pipelines for Few-Shot Learning: External  Data and Fine-Tuning Make a Difference,Shell Xu Hu,http://arxiv.org/pdf/2204.07305v1.pdf,2022-04-15,"['cs.cv', 'cs.lg']",2204.07305v1.pdf,"  Few-shot learning (FSL) is an important and topical problem in computer
+vision that has motivated extensive research into numerous methods spanning
+from sophisticated meta-learning methods to simple transfer learning baselines.
+We seek to push the limits of a simple-but-effective pipeline for more
+realistic and practical settings of few-shot image classification. To this end,
+we explore few-shot learning from the perspective of neural network
+architecture, as well as a three stage pipeline of network updates under
+different data supplies, where unsupervised external data is considered for
+pre-training, base categories are used to simulate few-shot tasks for
+meta-training, and the scarcely labelled data of an novel task is taken for
+fine-tuning. We investigate questions such as: (1) How pre-training on external
+data benefits FSL? (2) How state-of-the-art transformer architectures can be
+exploited? and (3) How fine-tuning mitigates domain shift? Ultimately, we show
+that a simple transformer-based pipeline yields surprisingly good performance
+on standard benchmarks such as Mini-ImageNet, CIFAR-FS, CDFSL and Meta-Dataset.
+Our code and demo are available at https://hushell.github.io/pmf.
+"
+"Multi-Level Fine-Tuning, Data Augmentation, and Few-Shot Learning for  Specialized Cyber Threat Intelligence",Markus Bayer,http://arxiv.org/pdf/2207.11076v1.pdf,2022-07-22,"['cs.cr', 'cs.cl']",2207.11076v1.pdf,"  Gathering cyber threat intelligence from open sources is becoming
+increasingly important for maintaining and achieving a high level of security
+as systems become larger and more complex. However, these open sources are
+often subject to information overload. It is therefore useful to apply machine
+learning models that condense the amount of information to what is necessary.
+Yet, previous studies and applications have shown that existing classifiers are
+not able to extract specific information about emerging cybersecurity events
+due to their low generalization ability. Therefore, we propose a system to
+overcome this problem by training a new classifier for each new incident. Since
+this requires a lot of labelled data using standard training methods, we
+combine three different low-data regime techniques - transfer learning, data
+augmentation, and few-shot learning - to train a high-quality classifier from
+very few labelled instances. We evaluated our approach using a novel dataset
+derived from the Microsoft Exchange Server data breach of 2021 which was
+labelled by three experts. Our findings reveal an increase in F1 score of more
+than 21 points compared to standard training methods and more than 18 points
+compared to a state-of-the-art method in few-shot learning. Furthermore, the
+classifier trained with this method and 32 instances is only less than 5 F1
+score points worse than a classifier trained with 1800 instances.
+"
+Multitask Pre-training of Modular Prompt for Chinese Few-Shot Learning,Tianxiang Sun,http://arxiv.org/pdf/2210.07565v3.pdf,2022-10-14,['cs.cl'],2210.07565v3.pdf,"  Prompt tuning is a parameter-efficient approach to adapting pre-trained
+language models to downstream tasks. Although prompt tuning has been shown to
+match the performance of full model tuning when training data is sufficient, it
+tends to struggle in few-shot learning settings. In this paper, we present
+Multi-task Pre-trained Modular Prompt (MP2) to boost prompt tuning for few-shot
+learning. MP2 is a set of combinable prompts pre-trained on 38 Chinese tasks.
+On downstream tasks, the pre-trained prompts are selectively activated and
+combined, leading to strong compositional generalization to unseen tasks. To
+bridge the gap between pre-training and fine-tuning, we formulate upstream and
+downstream tasks into a unified machine reading comprehension task. Extensive
+experiments under two learning paradigms, i.e., gradient descent and black-box
+tuning, show that MP2 significantly outperforms prompt tuning, full model
+tuning, and prior prompt pre-training methods in few-shot settings. In
+addition, we demonstrate that MP2 can achieve surprisingly fast and strong
+adaptation to downstream tasks by merely learning 8 parameters to combine the
+pre-trained modular prompts.
+"
+Few-shot Classification with Hypersphere Modeling of Prototypes,Ning Ding,http://arxiv.org/pdf/2211.05319v1.pdf,2022-11-10,"['cs.lg', 'cs.cl', 'cs.cv']",2211.05319v1.pdf,"  Metric-based meta-learning is one of the de facto standards in few-shot
+learning. It composes of representation learning and metrics calculation
+designs. Previous works construct class representations in different ways,
+varying from mean output embedding to covariance and distributions. However,
+using embeddings in space lacks expressivity and cannot capture class
+information robustly, while statistical complex modeling poses difficulty to
+metric designs. In this work, we use tensor fields (``areas'') to model classes
+from the geometrical perspective for few-shot learning. We present a simple and
+effective method, dubbed hypersphere prototypes (HyperProto), where class
+information is represented by hyperspheres with dynamic sizes with two sets of
+learnable parameters: the hypersphere's center and the radius. Extending from
+points to areas, hyperspheres are much more expressive than embeddings.
+Moreover, it is more convenient to perform metric-based classification with
+hypersphere prototypes than statistical modeling, as we only need to calculate
+the distance from a data point to the surface of the hypersphere. Following
+this idea, we also develop two variants of prototypes under other measurements.
+Extensive experiments and analysis on few-shot learning tasks across NLP and CV
+and comparison with 20+ competitive baselines demonstrate the effectiveness of
+our approach.
+"
+StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot  Learning,Yuqian Fu,http://arxiv.org/pdf/2302.09309v2.pdf,2023-02-18,['cs.cv'],2302.09309v2.pdf,"  Cross-Domain Few-Shot Learning (CD-FSL) is a recently emerging task that
+tackles few-shot learning across different domains. It aims at transferring
+prior knowledge learned on the source dataset to novel target datasets. The
+CD-FSL task is especially challenged by the huge domain gap between different
+datasets. Critically, such a domain gap actually comes from the changes of
+visual styles, and wave-SAN empirically shows that spanning the style
+distribution of the source data helps alleviate this issue. However, wave-SAN
+simply swaps styles of two images. Such a vanilla operation makes the generated
+styles ``real'' and ``easy'', which still fall into the original set of the
+source styles. Thus, inspired by vanilla adversarial learning, a novel
+model-agnostic meta Style Adversarial training (StyleAdv) method together with
+a novel style adversarial attack method is proposed for CD-FSL. Particularly,
+our style attack method synthesizes both ``virtual'' and ``hard'' adversarial
+styles for model training. This is achieved by perturbing the original style
+with the signed style gradients. By continually attacking styles and forcing
+the model to recognize these challenging adversarial styles, our model is
+gradually robust to the visual styles, thus boosting the generalization ability
+for novel target datasets. Besides the typical CNN-based backbone, we also
+employ our StyleAdv method on large-scale pretrained vision transformer.
+Extensive experiments conducted on eight various target datasets show the
+effectiveness of our method. Whether built upon ResNet or ViT, we achieve the
+new state of the art for CD-FSL. Code is available at
+https://github.com/lovelyqian/StyleAdv-CDFSL.
+"
+Few-Shot Learning with Visual Distribution Calibration and Cross-Modal  Distribution Alignment,Runqi Wang,http://arxiv.org/pdf/2305.11439v1.pdf,2023-05-19,['cs.cv'],2305.11439v1.pdf,"  Pre-trained vision-language models have inspired much research on few-shot
+learning. However, with only a few training images, there exist two crucial
+problems: (1) the visual feature distributions are easily distracted by
+class-irrelevant information in images, and (2) the alignment between the
+visual and language feature distributions is difficult. To deal with the
+distraction problem, we propose a Selective Attack module, which consists of
+trainable adapters that generate spatial attention maps of images to guide the
+attacks on class-irrelevant image areas. By messing up these areas, the
+critical features are captured and the visual distributions of image features
+are calibrated. To better align the visual and language feature distributions
+that describe the same object class, we propose a cross-modal distribution
+alignment module, in which we introduce a vision-language prototype for each
+class to align the distributions, and adopt the Earth Mover's Distance (EMD) to
+optimize the prototypes. For efficient computation, the upper bound of EMD is
+derived. In addition, we propose an augmentation strategy to increase the
+diversity of the images and the text prompts, which can reduce overfitting to
+the few-shot training images. Extensive experiments on 11 datasets demonstrate
+that our method consistently outperforms prior arts in few-shot learning. The
+implementation code will be available at https://github.com/bhrqw/SADA.
+"
+Federated Few-shot Learning for Cough Classification with Edge Devices,Ngan Dao Hoang,http://arxiv.org/pdf/2309.01076v1.pdf,2023-09-03,"['cs.lg', 'cs.sd', 'eess.as']",2309.01076v1.pdf,"  Automatically classifying cough sounds is one of the most critical tasks for
+the diagnosis and treatment of respiratory diseases. However, collecting a huge
+amount of labeled cough dataset is challenging mainly due to high laborious
+expenses, data scarcity, and privacy concerns. In this work, our aim is to
+develop a framework that can effectively perform cough classification even in
+situations when enormous cough data is not available, while also addressing
+privacy concerns. Specifically, we formulate a new problem to tackle these
+challenges and adopt few-shot learning and federated learning to design a novel
+framework, termed F2LCough, for solving the newly formulated problem. We
+illustrate the superiority of our method compared with other approaches on
+COVID-19 Thermal Face & Cough dataset, in which F2LCough achieves an average
+F1-Score of 86%. Our results show the feasibility of few-shot learning combined
+with federated learning to build a classification model of cough sounds. This
+new methodology is able to classify cough sounds in data-scarce situations and
+maintain privacy properties. The outcomes of this work can be a fundamental
+framework for building support systems for the detection and diagnosis of
+cough-related diseases.
+"
+Few-Shot Bot: Prompt-Based Learning for Dialogue Systems,Andrea Madotto,http://arxiv.org/pdf/2110.08118v1.pdf,2021-10-15,"['cs.cl', 'cs.ai']",2110.08118v1.pdf,"  Learning to converse using only a few examples is a great challenge in
+conversational AI. The current best conversational models, which are either
+good chit-chatters (e.g., BlenderBot) or goal-oriented systems (e.g., MinTL),
+are language models (LMs) fine-tuned on large conversational datasets. Training
+these models is expensive, both in terms of computational resources and time,
+and it is hard to keep them up to date with new conversational skills. A simple
+yet unexplored solution is prompt-based few-shot learning (Brown et al. 2020)
+which does not require gradient-based fine-tuning but instead uses a few
+examples in the LM context as the only source of learning. In this paper, we
+explore prompt-based few-shot learning in dialogue tasks. We benchmark LMs of
+different sizes in nine response generation tasks, which include four
+knowledge-grounded tasks, a task-oriented generations task, three open-chat
+tasks, and controlled stylistic generation, and five conversational parsing
+tasks, which include dialogue state tracking, graph path generation, persona
+information extraction, document retrieval, and internet query generation. The
+current largest released LM (GPT-J-6B) using prompt-based few-shot learning,
+and thus requiring no training, achieves competitive performance to fully
+trained state-of-the-art models. Moreover, we propose a novel prompt-based
+few-shot classifier, that also does not require any fine-tuning, to select the
+most appropriate prompt given a dialogue history. Finally, by combining the
+power of prompt-based few-shot learning and a Skill Selector, we create an
+end-to-end chatbot named the Few-Shot Bot (FSB), which automatically selects
+the most appropriate conversational skill, queries different knowledge bases or
+the internet, and uses the retrieved knowledge to generate a human-like
+response, all using only few dialogue examples per skill.
+"
+"A Neural Network Solves, Explains, and Generates University Math  Problems by Program Synthesis and Few-Shot Learning at Human Level",Iddo Drori,http://arxiv.org/pdf/2112.15594v4.pdf,2021-12-31,"['cs.lg', 'cs.ai']",2112.15594v4.pdf,"  We demonstrate that a neural network pre-trained on text and fine-tuned on
+code solves mathematics course problems, explains solutions, and generates new
+questions at a human level. We automatically synthesize programs using few-shot
+learning and OpenAI's Codex transformer and execute them to solve course
+problems at 81% automatic accuracy. We curate a new dataset of questions from
+MIT's largest mathematics courses (Single Variable and Multivariable Calculus,
+Differential Equations, Introduction to Probability and Statistics, Linear
+Algebra, and Mathematics for Computer Science) and Columbia University's
+Computational Linear Algebra. We solve questions from a MATH dataset (on
+Prealgebra, Algebra, Counting and Probability, Intermediate Algebra, Number
+Theory, and Precalculus), the latest benchmark of advanced mathematics problems
+designed to assess mathematical reasoning. We randomly sample questions and
+generate solutions with multiple modalities, including numbers, equations, and
+plots. The latest GPT-3 language model pre-trained on text automatically solves
+only 18.8% of these university questions using zero-shot learning and 30.8%
+using few-shot learning and the most recent chain of thought prompting. In
+contrast, program synthesis with few-shot learning using Codex fine-tuned on
+code generates programs that automatically solve 81% of these questions. Our
+approach improves the previous state-of-the-art automatic solution accuracy on
+the benchmark topics from 8.8% to 81.1%. We perform a survey to evaluate the
+quality and difficulty of generated questions. This work is the first to
+automatically solve university-level mathematics course questions at a human
+level and the first work to explain and generate university-level mathematics
+course questions at scale, a milestone for higher education.
+"
+Is Support Set Diversity Necessary for Meta-Learning?,Amrith Setlur,http://arxiv.org/pdf/2011.14048v2.pdf,2020-11-28,"['cs.lg', 'stat.ml']",2011.14048v2.pdf,"  Meta-learning is a popular framework for learning with limited data in which
+an algorithm is produced by training over multiple few-shot learning tasks. For
+classification problems, these tasks are typically constructed by sampling a
+small number of support and query examples from a subset of the classes. While
+conventional wisdom is that task diversity should improve the performance of
+meta-learning, in this work we find evidence to the contrary: we propose a
+modification to traditional meta-learning approaches in which we keep the
+support sets fixed across tasks, thus reducing task diversity. Surprisingly, we
+find that not only does this modification not result in adverse effects, it
+almost always improves the performance for a variety of datasets and
+meta-learning methods. We also provide several initial analyses to understand
+this phenomenon. Our work serves to: (i) more closely investigate the effect of
+support set construction for the problem of meta-learning, and (ii) suggest a
+simple, general, and competitive baseline for few-shot learning.
+"
+Detecting Hate Speech with GPT-3,Ke-Li Chiu,http://arxiv.org/pdf/2103.12407v4.pdf,2021-03-23,['cs.cl'],2103.12407v4.pdf,"  Sophisticated language models such as OpenAI's GPT-3 can generate hateful
+text that targets marginalized groups. Given this capacity, we are interested
+in whether large language models can be used to identify hate speech and
+classify text as sexist or racist. We use GPT-3 to identify sexist and racist
+text passages with zero-, one-, and few-shot learning. We find that with zero-
+and one-shot learning, GPT-3 can identify sexist or racist text with an average
+accuracy between 55 per cent and 67 per cent, depending on the category of text
+and type of learning. With few-shot learning, the model's accuracy can be as
+high as 85 per cent. Large language models have a role to play in hate speech
+detection, and with further development they could eventually be used to
+counter hate speech.
+"
+CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in  NLP,Qinyuan Ye,http://arxiv.org/pdf/2104.08835v2.pdf,2021-04-18,"['cs.cl', 'cs.lg']",2104.08835v2.pdf,"  Humans can learn a new language task efficiently with only few examples, by
+leveraging their knowledge obtained when learning prior tasks. In this paper,
+we explore whether and how such cross-task generalization ability can be
+acquired, and further applied to build better few-shot learners across diverse
+NLP tasks. We introduce CrossFit, a problem setup for studying cross-task
+generalization ability, which standardizes seen/unseen task partitions, data
+access during different learning stages, and the evaluation protocols. To
+instantiate different seen/unseen task partitions in CrossFit and facilitate
+in-depth analysis, we present the NLP Few-shot Gym, a repository of 160 diverse
+few-shot NLP tasks created from open-access NLP datasets and converted to a
+unified text-to-text format. Our analysis reveals that the few-shot learning
+ability on unseen tasks can be improved via an upstream learning stage using a
+set of seen tasks. We also observe that the selection of upstream learning
+tasks can significantly influence few-shot performance on unseen tasks, asking
+further analysis on task similarity and transferability.
+"
+Entailment as Few-Shot Learner,Sinong Wang,http://arxiv.org/pdf/2104.14690v1.pdf,2021-04-29,"['cs.cl', 'cs.ai']",2104.14690v1.pdf,"  Large pre-trained language models (LMs) have demonstrated remarkable ability
+as few-shot learners. However, their success hinges largely on scaling model
+parameters to a degree that makes it challenging to train and serve. In this
+paper, we propose a new approach, named as EFL, that can turn small LMs into
+better few-shot learners. The key idea of this approach is to reformulate
+potential NLP task into an entailment one, and then fine-tune the model with as
+little as 8 examples. We further demonstrate our proposed method can be: (i)
+naturally combined with an unsupervised contrastive learning-based data
+augmentation method; (ii) easily extended to multilingual few-shot learning. A
+systematic evaluation on 18 standard NLP tasks demonstrates that this approach
+improves the various existing SOTA few-shot learning methods by 12\%, and
+yields competitive few-shot performance with 500 times larger models, such as
+GPT-3.
+"
+True Few-Shot Learning with Language Models,Ethan Perez,http://arxiv.org/pdf/2105.11447v1.pdf,2021-05-24,"['cs.cl', 'cs.lg', 'stat.ml']",2105.11447v1.pdf,"  Pretrained language models (LMs) perform well on many tasks even when
+learning from a few examples, but prior work uses many held-out examples to
+tune various aspects of learning, such as hyperparameters, training objectives,
+and natural language templates (""prompts""). Here, we evaluate the few-shot
+ability of LMs when such held-out examples are unavailable, a setting we call
+true few-shot learning. We test two model selection criteria, cross-validation
+and minimum description length, for choosing LM prompts and hyperparameters in
+the true few-shot setting. On average, both marginally outperform random
+selection and greatly underperform selection based on held-out examples.
+Moreover, selection criteria often prefer models that perform significantly
+worse than randomly-selected ones. We find similar results even when taking
+into account our uncertainty in a model's true performance during selection, as
+well as when varying the amount of computation and number of examples used for
+selection. Overall, our findings suggest that prior work significantly
+overestimated the true few-shot ability of LMs given the difficulty of few-shot
+model selection.
+"
+"Generate, Annotate, and Learn: NLP with Synthetic Text",Xuanli He,http://arxiv.org/pdf/2106.06168v3.pdf,2021-06-11,['cs.lg'],2106.06168v3.pdf,"  This paper studies the use of language models as a source of synthetic
+unlabeled text for NLP. We formulate a general framework called ``generate,
+annotate, and learn (GAL)'' to take advantage of synthetic text within
+knowledge distillation, self-training, and few-shot learning applications. To
+generate high-quality task-specific text, we either fine-tune LMs on inputs
+from the task of interest, or prompt large LMs with few examples. We use the
+best available classifier to annotate synthetic text with soft pseudo labels
+for knowledge distillation and self-training, and use LMs to obtain hard labels
+for few-shot learning. We train new supervised models on the combination of
+labeled and pseudo-labeled data, which results in significant gains across
+several applications. We investigate key components of GAL and present
+theoretical and empirical arguments against the use of class-conditional LMs to
+generate synthetic labeled text instead of unlabeled text. GAL achieves new
+state-of-the-art knowledge distillation results for 6-layer transformers on the
+GLUE leaderboard.
+"
+Multimodal Few-Shot Learning with Frozen Language Models,Maria Tsimpoukelli,http://arxiv.org/pdf/2106.13884v2.pdf,2021-06-25,"['cs.cv', 'cs.cl', 'cs.lg']",2106.13884v2.pdf,"  When trained at sufficient scale, auto-regressive language models exhibit the
+notable ability to learn a new language task after being prompted with just a
+few examples. Here, we present a simple, yet effective, approach for
+transferring this few-shot learning ability to a multimodal setting (vision and
+language). Using aligned image and caption data, we train a vision encoder to
+represent each image as a sequence of continuous embeddings, such that a
+pre-trained, frozen language model prompted with this prefix generates the
+appropriate caption. The resulting system is a multimodal few-shot learner,
+with the surprising ability to learn a variety of new tasks when conditioned on
+examples, represented as a sequence of multiple interleaved image and text
+embeddings. We demonstrate that it can rapidly learn words for new objects and
+novel visual categories, do visual question-answering with only a handful of
+examples, and make use of outside knowledge, by measuring a single model on a
+variety of established and new benchmarks.
+"
+Revisiting Self-Training for Few-Shot Learning of Language Model,Yiming Chen,http://arxiv.org/pdf/2110.01256v1.pdf,2021-10-04,['cs.cl'],2110.01256v1.pdf,"  As unlabeled data carry rich task-relevant information, they are proven
+useful for few-shot learning of language model. The question is how to
+effectively make use of such data. In this work, we revisit the self-training
+technique for language model fine-tuning and present a state-of-the-art
+prompt-based few-shot learner, SFLM. Given two views of a text sample via weak
+and strong augmentation techniques, SFLM generates a pseudo label on the weakly
+augmented version. Then, the model predicts the same pseudo label when
+fine-tuned with the strongly augmented version. This simple approach is shown
+to outperform other state-of-the-art supervised and semi-supervised
+counterparts on six sentence classification and six sentence-pair
+classification benchmarking tasks. In addition, SFLM only relies on a few
+in-domain unlabeled data. We conduct a comprehensive analysis to demonstrate
+the robustness of our proposed approach under various settings, including
+augmentation techniques, model scale, and few-shot knowledge transfer across
+tasks.
+"
+In-Context Learning for Few-Shot Dialogue State Tracking,Yushi Hu,http://arxiv.org/pdf/2203.08568v3.pdf,2022-03-16,['cs.cl'],2203.08568v3.pdf,"  Collecting and annotating task-oriented dialogues is time-consuming and
+costly; thus, zero and few shot learning could greatly benefit dialogue state
+tracking (DST). In this work, we propose an in-context learning (ICL) framework
+for zero-shot and few-shot learning DST, where a large pre-trained language
+model (LM) takes a test instance and a few exemplars as input, and directly
+decodes the dialogue state without any parameter updates. To better leverage a
+tabular domain description in the LM prompt, we reformulate DST into a
+text-to-SQL problem. We also propose a novel approach to retrieve annotated
+dialogues as exemplars. Empirical results on MultiWOZ show that our method
+IC-DST substantially outperforms previous fine-tuned state-of-the-art models in
+few-shot settings. In addition, we test IC-DST in zero-shot settings, in which
+the model only takes a fixed task instruction as input, finding that it
+outperforms previous zero-shot methods by a large margin.
+"
+WAVPROMPT: Towards Few-Shot Spoken Language Understanding with Frozen  Language Models,Heting Gao,http://arxiv.org/pdf/2203.15863v2.pdf,2022-03-29,"['eess.as', 'cs.ai', 'cs.cl']",2203.15863v2.pdf,"  Large-scale auto-regressive language models pretrained on massive text have
+demonstrated their impressive ability to perform new natural language tasks
+with only a few text examples, without the need for fine-tuning. Recent studies
+further show that such a few-shot learning ability can be extended to the
+text-image setting by training an encoder to encode the images into embeddings
+functioning like the text embeddings of the language model. Interested in
+exploring the possibility of transferring the few-shot learning ability to the
+audio-text setting, we propose a novel speech understanding framework,
+WavPrompt, where we finetune a wav2vec model to generate a sequence of audio
+embeddings understood by the language model. We show that WavPrompt is a
+few-shot learner that can perform speech understanding tasks better than a
+naive text baseline. We conduct detailed ablation studies on different
+components and hyperparameters to empirically identify the best model
+configuration. In addition, we conduct a non-speech understanding experiment to
+show WavPrompt can extract more information than just the transcriptions. Code
+is available at https://github.com/Hertin/WavPrompt
+"
+Enabling Classifiers to Make Judgements Explicitly Aligned with Human  Values,Yejin Bang,http://arxiv.org/pdf/2210.07652v1.pdf,2022-10-14,"['cs.cl', 'cs.ai']",2210.07652v1.pdf,"  Many NLP classification tasks, such as sexism/racism detection or toxicity
+detection, are based on human values. Yet, human values can vary under diverse
+cultural conditions. Therefore, we introduce a framework for value-aligned
+classification that performs prediction based on explicitly written human
+values in the command. Along with the task, we propose a practical approach
+that distills value-aligned knowledge from large-scale language models (LLMs)
+to construct value-aligned classifiers in two steps. First, we generate
+value-aligned training data from LLMs by prompt-based few-shot learning. Next,
+we fine-tune smaller classification models with the generated data for the
+task. Empirical results show that our VA-Models surpass multiple baselines by
+at least 15.56% on the F1-score, including few-shot learning with OPT-175B and
+existing text augmentation methods. We suggest that using classifiers with
+explicit human value input improves both inclusivity & explainability in AI.
+"
+Aligning MAGMA by Few-Shot Learning and Finetuning,Jean-Charles Layoun,http://arxiv.org/pdf/2210.14161v1.pdf,2022-10-18,"['cs.cv', 'cs.ai']",2210.14161v1.pdf,"  The goal of vision-language modeling is to allow models to tie language
+understanding with visual inputs. The aim of this paper is to evaluate and
+align the Visual Language Model (VLM) called Multimodal Augmentation of
+Generative Models through Adapter-based finetuning (MAGMA) with human values.
+MAGMA is a VLM that is capable of image captioning and visual
+question-answering. We will evaluate its alignment in three different
+scenarios. To begin, we assess MAGMA's out-of-the-box alignment through the
+checkpoint provided by Hugging Face. Then, we measure if few-shot learning
+manages to improve the results. Finally, we finetune the model on aligned
+examples and evaluate its behavior.
+"
+GPS: Genetic Prompt Search for Efficient Few-shot Learning,Hanwei Xu,http://arxiv.org/pdf/2210.17041v1.pdf,2022-10-31,['cs.cl'],2210.17041v1.pdf,"  Prompt-based techniques have demostrated great potential for improving the
+few-shot generalization of pretrained language models. However, their
+performance heavily relies on the manual design of prompts and thus requires a
+lot of human efforts. In this paper, we introduce Genetic Prompt Search (GPS)
+to improve few-shot learning with prompts, which utilizes a genetic algorithm
+to automatically search for high-performing prompts. GPS is gradient-free and
+requires no update of model parameters but only a small validation set.
+Experiments on diverse datasets proved the effectiveness of GPS, which
+outperforms manual prompts by a large margin of 2.6 points. Our method is also
+better than other parameter-efficient tuning methods such as prompt tuning.
+"
+MEAL: Stable and Active Learning for Few-Shot Prompting,Abdullatif Köksal,http://arxiv.org/pdf/2211.08358v2.pdf,2022-11-15,['cs.cl'],2211.08358v2.pdf,"  Few-shot classification has made great strides due to foundation models that,
+through priming and prompting, are highly effective few-shot learners. However,
+this approach has high variance both across different sets of few shots (data
+selection) and across different finetuning runs (run variability). This is
+problematic not only because it impedes the fair comparison of different
+approaches, but especially because it makes few-shot learning too unreliable
+for many real-world applications. To alleviate these issues, we make two
+contributions for more stable and effective few-shot learning: First, we
+propose novel ensembling methods and show that they substantially reduce run
+variability. Second, we introduce a new active learning (AL) criterion for data
+selection and present the first AL-based approach specifically tailored towards
+prompt-based learning. In our experiments, we show that our combined method,
+MEAL (Multiprompt finetuning and prediction Ensembling with Active Learning),
+improves overall performance of prompt-based finetuning by 2.3 points on five
+diverse tasks.
+"
+Few-shot Query-Focused Summarization with Prefix-Merging,Ruifeng Yuan,http://arxiv.org/pdf/2211.16164v1.pdf,2022-11-29,"['cs.cl', 'cs.ai']",2211.16164v1.pdf,"  Query-focused summarization has been considered as an important extension for
+text summarization. It aims to generate a concise highlight for a given query.
+Different from text summarization, query-focused summarization has long been
+plagued by the problem of lacking high-quality large-scale datasets. In this
+paper, we investigate the idea that whether we can integrate and transfer the
+knowledge of text summarization and question answering to assist the few-shot
+learning in query-focused summarization. Here, we propose prefix-merging, a
+prefix-based pretraining strategy for few-shot learning in query-focused
+summarization. Drawn inspiration from prefix-tuning, we are allowed to
+integrate the task knowledge from text summarization and question answering
+into a properly designed prefix and apply the merged prefix to query-focused
+summarization. With only a small amount of trainable parameters, prefix-merging
+outperforms fine-tuning on query-focused summarization. We further discuss the
+influence of different prefix designs and propose a visualized explanation for
+how prefix-merging works.
+"
+JASMINE: Arabic GPT Models for Few-Shot Learning,El Moatez Billah Nagoudi,http://arxiv.org/pdf/2212.10755v2.pdf,2022-12-21,['cs.cl'],2212.10755v2.pdf,"  Scholarship on generative pretraining (GPT) remains acutely Anglocentric,
+leaving serious gaps in our understanding of the whole class of autoregressive
+models. For example, we have little knowledge about the potential of these
+models and their societal impacts in diverse linguistic and cultural settings.
+We alleviate this issue for Arabic, a wide collection of languages and
+dialectal varieties with more than 400 million population, by introducing
+JASMINE. JASMINE is a suite of powerful Arabic autoregressive Transformer
+language models ranging in size between 300 million-6.7 billion parameters
+pretrained on a large and diverse dataset (~ 235 GB of text). We also carefully
+design and release a comprehensive benchmark for both automated and human
+evaluation of Arabic autoregressive models, with coverage of potential social
+biases, harms, and toxicity. Using our novel benchmark, we evaluate JASMINE
+extensively showing powerful performance intrinsically as well as in few-shot
+learning on a wide range of NLP tasks. We aim to responsibly release our models
+and evaluation benchmark with interested researchers, along with code for
+experimenting with them.
+"
+Log Parsing with Prompt-based Few-shot Learning,Van-Hoang Le,http://arxiv.org/pdf/2302.07435v1.pdf,2023-02-15,['cs.se'],2302.07435v1.pdf,"  Logs generated by large-scale software systems provide crucial information
+for engineers to understand the system status and diagnose problems of the
+systems. Log parsing, which converts raw log messages into structured data, is
+the first step to enabling automated log analytics. Existing log parsers
+extract the common part as log templates using statistical features. However,
+these log parsers often fail to identify the correct templates and parameters
+because: 1) they often overlook the semantic meaning of log messages, and 2)
+they require domain-specific knowledge for different log datasets. To address
+the limitations of existing methods, in this paper, we propose LogPPT to
+capture the patterns of templates using prompt-based few-shot learning. LogPPT
+utilises a novel prompt tuning method to recognise keywords and parameters
+based on a few labelled log data. In addition, an adaptive random sampling
+algorithm is designed to select a small yet diverse training set. We have
+conducted extensive experiments on 16 public log datasets. The experimental
+results show that LogPPT is effective and efficient for log parsing.
+"
+Conversation Style Transfer using Few-Shot Learning,Shamik Roy,http://arxiv.org/pdf/2302.08362v2.pdf,2023-02-16,['cs.cl'],2302.08362v2.pdf,"  Conventional text style transfer approaches focus on sentence-level style
+transfer without considering contextual information, and the style is described
+with attributes (e.g., formality). When applying style transfer in
+conversations such as task-oriented dialogues, existing approaches suffer from
+these limitations as context can play an important role and the style
+attributes are often difficult to define in conversations. In this paper, we
+introduce conversation style transfer as a few-shot learning problem, where the
+model learns to perform style transfer by observing only a few example
+dialogues in the target style. We propose a novel in-context learning approach
+to solve the task with style-free dialogues as a pivot. Human evaluation shows
+that by incorporating multi-turn context, the model is able to match the target
+style while having better appropriateness and semantic correctness compared to
+utterance/sentence-level style transfer. Additionally, we show that
+conversation style transfer can also benefit downstream tasks. For example, in
+multi-domain intent classification tasks, the F1 scores improve after
+transferring the style of training data to match the style of the test data.
+"
+STUNT: Few-shot Tabular Learning with Self-generated Tasks from  Unlabeled Tables,Jaehyun Nam,http://arxiv.org/pdf/2303.00918v1.pdf,2023-03-02,"['cs.lg', 'cs.ai']",2303.00918v1.pdf,"  Learning with few labeled tabular samples is often an essential requirement
+for industrial machine learning applications as varieties of tabular data
+suffer from high annotation costs or have difficulties in collecting new
+samples for novel tasks. Despite the utter importance, such a problem is quite
+under-explored in the field of tabular learning, and existing few-shot learning
+schemes from other domains are not straightforward to apply, mainly due to the
+heterogeneous characteristics of tabular data. In this paper, we propose a
+simple yet effective framework for few-shot semi-supervised tabular learning,
+coined Self-generated Tasks from UNlabeled Tables (STUNT). Our key idea is to
+self-generate diverse few-shot tasks by treating randomly chosen columns as a
+target label. We then employ a meta-learning scheme to learn generalizable
+knowledge with the constructed tasks. Moreover, we introduce an unsupervised
+validation scheme for hyperparameter search (and early stopping) by generating
+a pseudo-validation set using STUNT from unlabeled data. Our experimental
+results demonstrate that our simple framework brings significant performance
+gain under various tabular few-shot learning benchmarks, compared to prior
+semi- and self-supervised baselines. Code is available at
+https://github.com/jaehyun513/STUNT.
+"
+CancerGPT: Few-shot Drug Pair Synergy Prediction using Large Pre-trained  Language Models,Tianhao Li,http://arxiv.org/pdf/2304.10946v1.pdf,2023-04-18,"['cs.cl', 'cs.lg', 'q-bio.bm']",2304.10946v1.pdf,"  Large pre-trained language models (LLMs) have been shown to have significant
+potential in few-shot learning across various fields, even with minimal
+training data. However, their ability to generalize to unseen tasks in more
+complex fields, such as biology, has yet to be fully evaluated. LLMs can offer
+a promising alternative approach for biological inference, particularly in
+cases where structured data and sample size are limited, by extracting prior
+knowledge from text corpora. Our proposed few-shot learning approach uses LLMs
+to predict the synergy of drug pairs in rare tissues that lack structured data
+and features. Our experiments, which involved seven rare tissues from different
+cancer types, demonstrated that the LLM-based prediction model achieved
+significant accuracy with very few or zero samples. Our proposed model, the
+CancerGPT (with $\sim$ 124M parameters), was even comparable to the larger
+fine-tuned GPT-3 model (with $\sim$ 175B parameters). Our research is the first
+to tackle drug pair synergy prediction in rare tissues with limited data. We
+are also the first to utilize an LLM-based prediction model for biological
+reaction prediction tasks.
+"
+Automated Few-shot Classification with Instruction-Finetuned Language  Models,Rami Aly,http://arxiv.org/pdf/2305.12576v2.pdf,2023-05-21,['cs.cl'],2305.12576v2.pdf,"  A particularly successful class of approaches for few-shot learning combines
+language models with prompts -- hand-crafted task descriptions that complement
+data samples. However, designing prompts by hand for each task commonly
+requires domain knowledge and substantial guesswork. We observe, in the context
+of classification tasks, that instruction finetuned language models exhibit
+remarkable prompt robustness, and we subsequently propose a simple method to
+eliminate the need for handcrafted prompts, named AuT-Few. This approach
+consists of (i) a prompt retrieval module that selects suitable task
+instructions from the instruction-tuning knowledge base, and (ii) the
+generation of two distinct, semantically meaningful, class descriptions and a
+selection mechanism via cross-validation. Over $12$ datasets, spanning $8$
+classification tasks, we show that AuT-Few outperforms current state-of-the-art
+few-shot learning methods. Moreover, AuT-Few is the best ranking method across
+datasets on the RAFT few-shot benchmark. Notably, these results are achieved
+without task-specific handcrafted prompts on unseen tasks.
+"
+Active Learning Principles for In-Context Learning with Large Language  Models,Katerina Margatina,http://arxiv.org/pdf/2305.14264v1.pdf,2023-05-23,"['cs.cl', 'cs.ai']",2305.14264v1.pdf,"  The remarkable advancements in large language models (LLMs) have
+significantly enhanced the performance in few-shot learning settings. By using
+only a small number of labeled examples, referred to as demonstrations, LLMs
+can effectively grasp the task at hand through in-context learning. However,
+the process of selecting appropriate demonstrations has received limited
+attention in prior work. This paper addresses the issue of identifying the most
+informative demonstrations for few-shot learning by approaching it as a
+pool-based Active Learning (AL) problem over a single iteration. Our objective
+is to investigate how AL algorithms can serve as effective demonstration
+selection methods for in-context learning. We compare various standard AL
+algorithms based on uncertainty, diversity, and similarity, and consistently
+observe that the latter outperforms all other methods, including random
+sampling. Notably, uncertainty sampling, despite its success in conventional
+supervised learning scenarios, performs poorly in this context. Our extensive
+experimentation involving a diverse range of GPT and OPT models across $24$
+classification and multi-choice tasks, coupled with thorough analysis,
+unambiguously demonstrates that in-context example selection through AL
+prioritizes high-quality examples that exhibit low uncertainty and bear
+similarity to the test examples.
+"
+Zero-shot Approach to Overcome Perturbation Sensitivity of Prompts,Mohna Chakraborty,http://arxiv.org/pdf/2305.15689v2.pdf,2023-05-25,"['cs.cl', 'cs.ai']",2305.15689v2.pdf,"  Recent studies have demonstrated that natural-language prompts can help to
+leverage the knowledge learned by pre-trained language models for the binary
+sentence-level sentiment classification task. Specifically, these methods
+utilize few-shot learning settings to fine-tune the sentiment classification
+model using manual or automatically generated prompts. However, the performance
+of these methods is sensitive to the perturbations of the utilized prompts.
+Furthermore, these methods depend on a few labeled instances for automatic
+prompt generation and prompt ranking. This study aims to find high-quality
+prompts for the given task in a zero-shot setting. Given a base prompt, our
+proposed approach automatically generates multiple prompts similar to the base
+prompt employing positional, reasoning, and paraphrasing techniques and then
+ranks the prompts using a novel metric. We empirically demonstrate that the
+top-ranked prompts are high-quality and significantly outperform the base
+prompt and the prompts generated using few-shot learning for the binary
+sentence-level sentiment classification task.
+"
+FLamE: Few-shot Learning from Natural Language Explanations,Yangqiaoyu Zhou,http://arxiv.org/pdf/2306.08042v1.pdf,2023-06-13,"['cs.cl', 'cs.ai']",2306.08042v1.pdf,"  Natural language explanations have the potential to provide rich information
+that in principle guides model reasoning. Yet, recent work by Lampinen et al.
+(2022) has shown limited utility of natural language explanations in improving
+classification. To effectively learn from explanations, we present FLamE, a
+two-stage few-shot learning framework that first generates explanations using
+GPT-3, and then finetunes a smaller model (e.g., RoBERTa) with generated
+explanations. Our experiments on natural language inference demonstrate
+effectiveness over strong baselines, increasing accuracy by 17.6% over GPT-3
+Babbage and 5.7% over GPT-3 Davinci in e-SNLI. Despite improving classification
+performance, human evaluation surprisingly reveals that the majority of
+generated explanations does not adequately justify classification decisions.
+Additional analyses point to the important role of label-specific cues (e.g.,
+""not know"" for the neutral label) in generated explanations.
+"
+Exploiting the Potential of Seq2Seq Models as Robust Few-Shot Learners,Jihyeon Lee,http://arxiv.org/pdf/2307.14856v1.pdf,2023-07-27,"['cs.cl', 'cs.ai']",2307.14856v1.pdf,"  In-context learning, which offers substantial advantages over fine-tuning, is
+predominantly observed in decoder-only models, while encoder-decoder (i.e.,
+seq2seq) models excel in methods that rely on weight updates. Recently, a few
+studies have demonstrated the feasibility of few-shot learning with seq2seq
+models; however, this has been limited to tasks that align well with the
+seq2seq architecture, such as summarization and translation. Inspired by these
+initial studies, we provide a first-ever extensive experiment comparing the
+in-context few-shot learning capabilities of decoder-only and encoder-decoder
+models on a broad range of tasks. Furthermore, we propose two methods to more
+effectively elicit in-context learning ability in seq2seq models:
+objective-aligned prompting and a fusion-based approach. Remarkably, our
+approach outperforms a decoder-only model that is six times larger and exhibits
+significant performance improvements compared to conventional seq2seq models
+across a variety of settings. We posit that, with the right configuration and
+prompt design, seq2seq models can be highly effective few-shot learners for a
+wide spectrum of applications.
+"
+Prototypes-oriented Transductive Few-shot Learning with Conditional  Transport,Long Tian,http://arxiv.org/pdf/2308.03047v1.pdf,2023-08-06,['cs.cv'],2308.03047v1.pdf,"  Transductive Few-Shot Learning (TFSL) has recently attracted increasing
+attention since it typically outperforms its inductive peer by leveraging
+statistics of query samples. However, previous TFSL methods usually encode
+uniform prior that all the classes within query samples are equally likely,
+which is biased in imbalanced TFSL and causes severe performance degradation.
+  Given this pivotal issue, in this work, we propose a novel Conditional
+Transport (CT) based imbalanced TFSL model called {\textbf P}rototypes-oriented
+{\textbf U}nbiased {\textbf T}ransfer {\textbf M}odel (PUTM) to fully exploit
+unbiased statistics of imbalanced query samples, which employs forward and
+backward navigators as transport matrices to balance the prior of query samples
+per class between uniform and adaptive data-driven distributions. For
+efficiently transferring statistics learned by CT, we further derive a closed
+form solution to refine prototypes based on MAP given the learned navigators.
+The above two steps of discovering and transferring unbiased statistics follow
+an iterative manner, formulating our EM-based solver.
+  Experimental results on four standard benchmarks including miniImageNet,
+tieredImageNet, CUB, and CIFAR-FS demonstrate superiority of our model in
+class-imbalanced generalization.
+"
+Approximating Human-Like Few-shot Learning with GPT-based Compression,Cynthia Huang,http://arxiv.org/pdf/2308.06942v1.pdf,2023-08-14,"['cs.ai', 'cs.cl', 'cs.it', 'math.it']",2308.06942v1.pdf,"  In this work, we conceptualize the learning process as information
+compression. We seek to equip generative pre-trained models with human-like
+learning capabilities that enable data compression during inference. We present
+a novel approach that utilizes the Generative Pre-trained Transformer (GPT) to
+approximate Kolmogorov complexity, with the aim of estimating the optimal
+Information Distance for few-shot learning. We first propose using GPT as a
+prior for lossless text compression, achieving a noteworthy compression ratio.
+Experiment with LLAMA2-7B backbone achieves a compression ratio of 15.5 on
+enwik9. We justify the pre-training objective of GPT models by demonstrating
+its equivalence to the compression length, and, consequently, its ability to
+approximate the information distance for texts. Leveraging the approximated
+information distance, our method allows the direct application of GPT models in
+quantitative text similarity measurements. Experiment results show that our
+method overall achieves superior performance compared to embedding and prompt
+baselines on challenging NLP tasks, including semantic similarity, zero and
+one-shot text classification, and zero-shot text ranking.
+"
+COCA: Classifier-Oriented Calibration for Source-Free Universal Domain  Adaptation via Textual Prototype,Xinghong Liu,http://arxiv.org/pdf/2308.10450v1.pdf,2023-08-21,['cs.cv'],2308.10450v1.pdf,"  Universal Domain Adaptation (UniDA) aims to distinguish common and private
+classes between the source and target domains where domain shift exists.
+Recently, due to more stringent data restrictions, researchers have introduced
+Source-Free UniDA (SF-UniDA) in more realistic scenarios. SF-UniDA methods
+eliminate the need for direct access to source samples when performing
+adaptation to the target domain. However, existing SF-UniDA methods still
+require an extensive quantity of labeled source samples to train a source
+model, resulting in significant labeling costs. To tackle this issue, we
+present a novel Classifier-Oriented Calibration (COCA) method. This method,
+which leverages textual prototypes, is formulated for the source model based on
+few-shot learning. Specifically, we propose studying few-shot learning, usually
+explored for closed-set scenarios, to identify common and domain-private
+classes despite a significant domain shift between source and target domains.
+Essentially, we present a novel paradigm based on the vision-language model to
+learn SF-UniDA and hugely reduce the labeling costs on the source domain.
+Experimental results demonstrate that our approach outperforms state-of-the-art
+UniDA and SF-UniDA models.
+"
+Evaluating the Decency and Consistency of Data Validation Tests  Generated by LLMs,Rohan Alexander,http://arxiv.org/pdf/2310.01402v1.pdf,2023-10-02,['stat.me'],2310.01402v1.pdf,"  We investigated the potential of large language models (LLMs) in developing
+dataset validation tests. We carried out 96 experiments each for both GPT-3.5
+and GPT-4, examining different prompt scenarios, learning modes, temperature
+settings, and roles. The prompt scenarios were: 1) Asking for expectations, 2)
+Asking for expectations with a given context, 3) Asking for expectations after
+requesting a simulation, and 4) Asking for expectations with a provided data
+sample. For learning modes, we tested: 1) zero-shot, 2) one-shot, and 3)
+few-shot learning. We also tested four temperature settings: 0, 0.4, 0.6, and
+1. Furthermore, two distinct roles were considered: 1) ""helpful assistant"", 2)
+""expert data scientist"". To gauge consistency, every setup was tested five
+times. The LLM-generated responses were benchmarked against a gold standard
+suite, created by an experienced data scientist knowledgeable about the data in
+question. We find there are considerable returns to the use of few-shot
+learning, and that the more explicit the data setting can be the better. The
+best LLM configurations complement, rather than substitute, the gold standard
+results. This study underscores the value LLMs can bring to the data cleaning
+and preparation stages of the data science workflow.
+"
+Improving generalization in large language models by learning prefix  subspaces,Louis Falissard,http://arxiv.org/pdf/2310.15793v1.pdf,2023-10-24,"['cs.lg', 'cs.ai', 'cs.cl']",2310.15793v1.pdf,"  This article focuses on large language models (LLMs) fine-tuning in the
+scarce data regime (also known as the ""few-shot"" learning setting). We propose
+a method to increase the generalization capabilities of LLMs based on neural
+network subspaces. This optimization method, recently introduced in computer
+vision, aims to improve model generalization by identifying wider local optima
+through the joint optimization of an entire simplex of models in parameter
+space. Its adaptation to massive, pretrained transformers, however, poses some
+challenges. First, their considerable number of parameters makes it difficult
+to train several models jointly, and second, their deterministic parameter
+initialization schemes make them unfit for the subspace method as originally
+proposed. We show in this paper that ""Parameter Efficient Fine-Tuning"" (PEFT)
+methods, however, are perfectly compatible with this original approach, and
+propose to learn entire simplex of continuous prefixes. We test our method on a
+variant of the GLUE benchmark adapted to the few-shot learning setting, and
+show that both our contributions jointly lead to a gain in average performances
+compared to sota methods. The implementation can be found at the following
+link: https://github.com/Liloulou/prefix_subspace
+"
+Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive  Survey,Jiaoyan Chen,http://arxiv.org/pdf/2112.10006v6.pdf,2021-12-18,"['cs.lg', 'cs.ai']",2112.10006v6.pdf,"  Machine learning especially deep neural networks have achieved great success
+but many of them often rely on a number of labeled samples for supervision. As
+sufficient labeled training data are not always ready due to e.g., continuously
+emerging prediction targets and costly sample annotation in real world
+applications, machine learning with sample shortage is now being widely
+investigated. Among all these studies, many prefer to utilize auxiliary
+information including those in the form of Knowledge Graph (KG) to reduce the
+reliance on labeled samples. In this survey, we have comprehensively reviewed
+over 90 papers about KG-aware research for two major sample shortage settings
+-- zero-shot learning (ZSL) where some classes to be predicted have no labeled
+samples, and few-shot learning (FSL) where some classes to be predicted have
+only a small number of labeled samples that are available. We first introduce
+KGs used in ZSL and FSL as well as their construction methods, and then
+systematically categorize and summarize KG-aware ZSL and FSL methods, dividing
+them into different paradigms such as the mapping-based, the data augmentation,
+the propagation-based and the optimization-based. We next present different
+applications, including not only KG augmented prediction tasks such as image
+classification, question answering, text classification and knowledge
+extraction, but also KG completion tasks, and some typical evaluation resources
+for each task. We eventually discuss some challenges and open problems from
+different perspectives.
+"
+Few-shot Learning with Multilingual Language Models,Xi Victoria Lin,http://arxiv.org/pdf/2112.10668v3.pdf,2021-12-20,"['cs.cl', 'cs.ai']",2112.10668v3.pdf,"  Large-scale generative language models such as GPT-3 are competitive few-shot
+learners. While these models are known to be able to jointly represent many
+different languages, their training data is dominated by English, potentially
+limiting their cross-lingual generalization. In this work, we train
+multilingual generative language models on a corpus covering a diverse set of
+languages, and study their few- and zero-shot learning capabilities in a wide
+range of tasks. Our largest model with 7.5 billion parameters sets new state of
+the art in few-shot learning in more than 20 representative languages,
+outperforming GPT-3 of comparable size in multilingual commonsense reasoning
+(with +7.4% absolute accuracy improvement in 0-shot settings and +9.4% in
+4-shot settings) and natural language inference (+5.4% in each of 0-shot and
+4-shot settings). On the FLORES-101 machine translation benchmark, our model
+outperforms GPT-3 on 171 out of 182 directions with 32 training examples, while
+surpassing the official supervised baseline in 45 directions. We conduct an
+in-depth analysis of different multilingual prompting approaches, showing in
+particular that strong few-shot learning performance across languages can be
+achieved via cross-lingual transfer through both templates and demonstration
+examples. Finally, we evaluate our models in social value tasks such as hate
+speech detection in five languages and find it has limitations similar to
+comparable sized GPT-3 models.
+"
+Flamingo: a Visual Language Model for Few-Shot Learning,Jean-Baptiste Alayrac,http://arxiv.org/pdf/2204.14198v2.pdf,2022-04-29,"['cs.cv', 'cs.ai', 'cs.lg']",2204.14198v2.pdf,"  Building models that can be rapidly adapted to novel tasks using only a
+handful of annotated examples is an open challenge for multimodal machine
+learning research. We introduce Flamingo, a family of Visual Language Models
+(VLM) with this ability. We propose key architectural innovations to: (i)
+bridge powerful pretrained vision-only and language-only models, (ii) handle
+sequences of arbitrarily interleaved visual and textual data, and (iii)
+seamlessly ingest images or videos as inputs. Thanks to their flexibility,
+Flamingo models can be trained on large-scale multimodal web corpora containing
+arbitrarily interleaved text and images, which is key to endow them with
+in-context few-shot learning capabilities. We perform a thorough evaluation of
+our models, exploring and measuring their ability to rapidly adapt to a variety
+of image and video tasks. These include open-ended tasks such as visual
+question-answering, where the model is prompted with a question which it has to
+answer; captioning tasks, which evaluate the ability to describe a scene or an
+event; and close-ended tasks such as multiple-choice visual question-answering.
+For tasks lying anywhere on this spectrum, a single Flamingo model can achieve
+a new state of the art with few-shot learning, simply by prompting the model
+with task-specific examples. On numerous benchmarks, Flamingo outperforms
+models fine-tuned on thousands of times more task-specific data.
+"
+"Code Generation Tools (Almost) for Free? A Study of Few-Shot,  Pre-Trained Language Models on Code",Patrick Bareiß,http://arxiv.org/pdf/2206.01335v2.pdf,2022-06-02,"['cs.se', 'cs.lg']",2206.01335v2.pdf,"  Few-shot learning with large-scale, pre-trained language models is a powerful
+way to answer questions about code, e.g., how to complete a given code example,
+or even generate code snippets from scratch. The success of these models raises
+the question whether they could serve as a basis for building a wide range code
+generation tools. Traditionally, such tools are built manually and separately
+for each task. Instead, few-shot learning may allow to obtain different tools
+from a single pre-trained language model by simply providing a few examples or
+a natural language description of the expected tool behavior. This paper
+studies to what extent a state-of-the-art, pre-trained language model of code,
+Codex, may serve this purpose. We consider three code manipulation and code
+generation tasks targeted by a range of traditional tools: (i) code mutation;
+(ii) test oracle generation from natural language documentation; and (iii) test
+case generation. For each task, we compare few-shot learning to a manually
+built tool. Our results show that the model-based tools complement (code
+mutation), are on par (test oracle generation), or even outperform their
+respective traditionally built tool (test case generation), while imposing far
+less effort to develop them. By comparing the effectiveness of different
+variants of the model-based tools, we provide insights on how to design an
+appropriate input (""prompt"") to the model and what influence the size of the
+model has. For example, we find that providing a small natural language
+description of the code generation task is an easy way to improve predictions.
+Overall, we conclude that few-shot language models are surprisingly effective,
+yet there is still more work to be done, such as exploring more diverse ways of
+prompting and tackling even more involved tasks.
+"
+From Human Days to Machine Seconds: Automatically Answering and  Generating Machine Learning Final Exams,Iddo Drori,http://arxiv.org/pdf/2206.05442v7.pdf,2022-06-11,['cs.lg'],2206.05442v7.pdf,"  A final exam in machine learning at a top institution such as MIT, Harvard,
+or Cornell typically takes faculty days to write, and students hours to solve.
+We demonstrate that large language models pass machine learning finals at a
+human level, on finals available online after the models were trained, and
+automatically generate new human-quality final exam questions in seconds.
+Previous work has developed program synthesis and few-shot learning methods to
+solve university-level problem set questions in mathematics and STEM courses.
+In this work, we develop and compare methods that solve final exams, which
+differ from problem sets in several ways: the questions are longer, have
+multiple parts, are more complicated, and span a broader set of topics. We
+curate a dataset and benchmark of questions from machine learning final exams
+available online and code for answering these questions and generating new
+questions. We show how to generate new questions from other questions and
+course notes. For reproducibility and future research on this final exam
+benchmark, we use automatic checkers for multiple-choice, numeric, and
+questions with expression answers. We perform ablation studies comparing
+zero-shot learning with few-shot learning and chain-of-thought prompting using
+GPT-3, OPT, Codex, and ChatGPT across machine learning topics and find that
+few-shot learning methods perform best. We highlight the transformative
+potential of language models to streamline the writing and solution of
+large-scale assessments, significantly reducing the workload from human days to
+mere machine seconds. Our results suggest that rather than banning large
+language models such as ChatGPT in class, instructors should teach students to
+harness them by asking students meta-questions about correctness, completeness,
+and originality of the responses generated, encouraging critical thinking in
+academic studies.
+"
+Model Tuning or Prompt Tuning? A Study of Large Language Models for  Clinical Concept and Relation Extraction,Cheng Peng,http://arxiv.org/pdf/2310.06239v1.pdf,2023-10-10,"['cs.cl', 'cs.ai']",2310.06239v1.pdf,"  Objective To develop soft prompt-based learning algorithms for large language
+models (LLMs), examine the shape of prompts, prompt-tuning using
+frozen/unfrozen LLMs, transfer learning, and few-shot learning abilities.
+Methods We developed a soft prompt-based LLM model and compared 4 training
+strategies including (1) fine-tuning without prompts; (2) hard-prompt with
+unfrozen LLMs; (3) soft-prompt with unfrozen LLMs; and (4) soft-prompt with
+frozen LLMs. We evaluated 7 pretrained LLMs using the 4 training strategies for
+clinical concept and relation extraction on two benchmark datasets. We
+evaluated the transfer learning ability of the prompt-based learning algorithms
+in a cross-institution setting. We also assessed the few-shot learning ability.
+Results and Conclusion When LLMs are unfrozen, GatorTron-3.9B with soft
+prompting achieves the best strict F1-scores of 0.9118 and 0.8604 for concept
+extraction, outperforming the traditional fine-tuning and hard prompt-based
+models by 0.6~3.1% and 1.2~2.9%, respectively; GatorTron-345M with soft
+prompting achieves the best F1-scores of 0.8332 and 0.7488 for end-to-end
+relation extraction, outperforming the other two models by 0.2~2% and
+0.6~11.7%, respectively. When LLMs are frozen, small (i.e., 345 million
+parameters) LLMs have a big gap to be competitive with unfrozen models; scaling
+LLMs up to billions of parameters makes frozen LLMs competitive with unfrozen
+LLMs. For cross-institute evaluation, soft prompting with a frozen
+GatorTron-8.9B model achieved the best performance. This study demonstrates
+that (1) machines can learn soft prompts better than humans, (2) frozen LLMs
+have better few-shot learning ability and transfer learning ability to
+facilitate muti-institution applications, and (3) frozen LLMs require large
+models.
+"
+On Unifying Misinformation Detection,Nayeon Lee,http://arxiv.org/pdf/2104.05243v1.pdf,2021-04-12,"['cs.ai', 'cs.cl']",2104.05243v1.pdf,"  In this paper, we introduce UnifiedM2, a general-purpose misinformation model
+that jointly models multiple domains of misinformation with a single, unified
+setup. The model is trained to handle four tasks: detecting news bias,
+clickbait, fake news, and verifying rumors. By grouping these tasks together,
+UnifiedM2learns a richer representation of misinformation, which leads to
+state-of-the-art or comparable performance across all tasks. Furthermore, we
+demonstrate that UnifiedM2's learned representation is helpful for few-shot
+learning of unseen misinformation tasks/datasets and model's generalizability
+to unseen events.
+"
+Discrete and Soft Prompting for Multilingual Models,Mengjie Zhao,http://arxiv.org/pdf/2109.03630v1.pdf,2021-09-08,['cs.cl'],2109.03630v1.pdf,"  It has been shown for English that discrete and soft prompting perform
+strongly in few-shot learning with pretrained language models (PLMs). In this
+paper, we show that discrete and soft prompting perform better than finetuning
+in multilingual cases: Crosslingual transfer and in-language training of
+multilingual natural language inference. For example, with 48 English training
+examples, finetuning obtains 33.74% accuracy in crosslingual transfer, barely
+surpassing the majority baseline (33.33%). In contrast, discrete and soft
+prompting outperform finetuning, achieving 36.43% and 38.79%. We also
+demonstrate good performance of prompting with training data in multiple
+languages other than English.
+"
+Cedille: A large autoregressive French language model,Martin Müller,http://arxiv.org/pdf/2202.03371v1.pdf,2022-02-07,"['cs.cl', '68t50', 'i.2.7']",2202.03371v1.pdf,"  Scaling up the size and training of autoregressive language models has
+enabled novel ways of solving Natural Language Processing tasks using zero-shot
+and few-shot learning. While extreme-scale language models such as GPT-3 offer
+multilingual capabilities, zero-shot learning for languages other than English
+remain largely unexplored. Here, we introduce Cedille, a large open source
+auto-regressive language model, specifically trained for the French language.
+Our results show that Cedille outperforms existing French language models and
+is competitive with GPT-3 on a range of French zero-shot benchmarks.
+Furthermore, we provide an in-depth comparison of the toxicity exhibited by
+these models, showing that Cedille marks an improvement in language model
+safety thanks to dataset filtering.
+"
+Human in the loop: How to effectively create coherent topics by manually  labeling only a few documents per class,Anton Thielmann,http://arxiv.org/pdf/2212.09422v1.pdf,2022-12-19,['cs.cl'],2212.09422v1.pdf,"  Few-shot methods for accurate modeling under sparse label-settings have
+improved significantly. However, the applications of few-shot modeling in
+natural language processing remain solely in the field of document
+classification. With recent performance improvements, supervised few-shot
+methods, combined with a simple topic extraction method pose a significant
+challenge to unsupervised topic modeling methods. Our research shows that
+supervised few-shot learning, combined with a simple topic extraction method,
+can outperform unsupervised topic modeling techniques in terms of generating
+coherent topics, even when only a few labeled documents per class are used.
+"
+Sentence Simplification via Large Language Models,Yutao Feng,http://arxiv.org/pdf/2302.11957v1.pdf,2023-02-23,"['cs.cl', 'cs.ai']",2302.11957v1.pdf,"  Sentence Simplification aims to rephrase complex sentences into simpler
+sentences while retaining original meaning. Large Language models (LLMs) have
+demonstrated the ability to perform a variety of natural language processing
+tasks. However, it is not yet known whether LLMs can be served as a
+high-quality sentence simplification system. In this work, we empirically
+analyze the zero-/few-shot learning ability of LLMs by evaluating them on a
+number of benchmark test sets. Experimental results show LLMs outperform
+state-of-the-art sentence simplification methods, and are judged to be on a par
+with human annotators.
+"
+NeuroCLIP: Neuromorphic Data Understanding by CLIP and SNN,Yufei Guo,http://arxiv.org/pdf/2306.12073v1.pdf,2023-06-21,['cs.cv'],2306.12073v1.pdf,"  Recently, the neuromorphic vision sensor has received more and more interest.
+However, the neuromorphic data consists of asynchronous event spikes, which is
+not natural and difficult to construct a benchmark, thus limiting the
+neuromorphic data understanding for ""unseen"" objects by deep learning.
+Zero-shot and few-shot learning via Contrastive Vision-Language Pre-training
+(CLIP) have shown inspirational performance in 2D frame image recognition. To
+handle ""unseen"" recognition for the neuromorphic data, in this paper, we
+propose NeuroCLIP, which transfers the CLIP's 2D pre-trained knowledge to event
+spikes. To improve the few-shot performance, we also provide an inter-timestep
+adapter based on a spiking neural network. Our code is open-sourced at
+https://github.com/yfguo91/NeuroCLIP.git.
+"
+Leveraging Few-Shot Data Augmentation and Waterfall Prompting for  Response Generation,Lea Krause,http://arxiv.org/pdf/2308.01080v1.pdf,2023-08-02,['cs.cl'],2308.01080v1.pdf,"  This paper discusses our approaches for task-oriented conversational
+modelling using subjective knowledge, with a particular emphasis on response
+generation. Our methodology was shaped by an extensive data analysis that
+evaluated key factors such as response length, sentiment, and dialogue acts
+present in the provided dataset. We used few-shot learning to augment the data
+with newly generated subjective knowledge items and present three approaches
+for DSTC11: (1) task-specific model exploration, (2) incorporation of the most
+frequent question into all generated responses, and (3) a waterfall prompting
+technique using a combination of both GPT-3 and ChatGPT.
+"
+Making Pre-trained Language Models Better Few-shot Learners,Tianyu Gao,http://arxiv.org/pdf/2012.15723v2.pdf,2020-12-31,"['cs.cl', 'cs.lg']",2012.15723v2.pdf,"  The recent GPT-3 model (Brown et al., 2020) achieves remarkable few-shot
+performance solely by leveraging a natural-language prompt and a few task
+demonstrations as input context. Inspired by their findings, we study few-shot
+learning in a more practical scenario, where we use smaller language models for
+which fine-tuning is computationally efficient. We present LM-BFF--better
+few-shot fine-tuning of language models--a suite of simple and complementary
+techniques for fine-tuning language models on a small number of annotated
+examples. Our approach includes (1) prompt-based fine-tuning together with a
+novel pipeline for automating prompt generation; and (2) a refined strategy for
+dynamically and selectively incorporating demonstrations into each context.
+Finally, we present a systematic evaluation for analyzing few-shot performance
+on a range of NLP tasks, including classification and regression. Our
+experiments demonstrate that our methods combine to dramatically outperform
+standard fine-tuning procedures in this low resource setting, achieving up to
+30% absolute improvement, and 11% on average across all tasks. Our approach
+makes minimal assumptions on task resources and domain expertise, and hence
+constitutes a strong task-agnostic method for few-shot learning.
+"
+GPT-3 Models are Poor Few-Shot Learners in the Biomedical Domain,Milad Moradi,http://arxiv.org/pdf/2109.02555v2.pdf,2021-09-06,"['cs.cl', 'cs.ai', 'cs.lg']",2109.02555v2.pdf,"  Deep neural language models have set new breakthroughs in many tasks of
+Natural Language Processing (NLP). Recent work has shown that deep transformer
+language models (pretrained on large amounts of texts) can achieve high levels
+of task-specific few-shot performance comparable to state-of-the-art models.
+However, the ability of these large language models in few-shot transfer
+learning has not yet been explored in the biomedical domain. We investigated
+the performance of two powerful transformer language models, i.e. GPT-3 and
+BioBERT, in few-shot settings on various biomedical NLP tasks. The experimental
+results showed that, to a great extent, both the models underperform a language
+model fine-tuned on the full training data. Although GPT-3 had already achieved
+near state-of-the-art results in few-shot knowledge transfer on open-domain NLP
+tasks, it could not perform as effectively as BioBERT, which is orders of
+magnitude smaller than GPT-3. Regarding that BioBERT was already pretrained on
+large biomedical text corpora, our study suggests that language models may
+largely benefit from in-domain pretraining in task-specific few-shot learning.
+However, in-domain pretraining seems not to be sufficient; novel pretraining
+and few-shot learning strategies are required in the biomedical NLP domain.
+"
+PPT: Pre-trained Prompt Tuning for Few-shot Learning,Yuxian Gu,http://arxiv.org/pdf/2109.04332v3.pdf,2021-09-09,['cs.cl'],2109.04332v3.pdf,"  Prompts for pre-trained language models (PLMs) have shown remarkable
+performance by bridging the gap between pre-training tasks and various
+downstream tasks. Among these methods, prompt tuning, which freezes PLMs and
+only tunes soft prompts, provides an efficient and effective solution for
+adapting large-scale PLMs to downstream tasks. However, prompt tuning is yet to
+be fully explored. In our pilot experiments, we find that prompt tuning
+performs comparably with conventional full-model fine-tuning when downstream
+data are sufficient, whereas it performs much worse under few-shot learning
+settings, which may hinder the application of prompt tuning in practice. We
+attribute this low performance to the manner of initializing soft prompts.
+Therefore, in this work, we propose to pre-train prompts by adding soft prompts
+into the pre-training stage to obtain a better initialization. We name this
+Pre-trained Prompt Tuning framework ""PPT"". To ensure the generalization of PPT,
+we formulate similar classification tasks into a unified task form and
+pre-train soft prompts for this unified task. Extensive experiments show that
+tuning pre-trained prompts for downstream tasks can reach or even outperform
+full-model fine-tuning under both full-data and few-shot settings. Our approach
+is effective and efficient for using large-scale PLMs in practice.
+"
+Yuan 1.0: Large-Scale Pre-trained Language Model in Zero-Shot and  Few-Shot Learning,Shaohua Wu,http://arxiv.org/pdf/2110.04725v2.pdf,2021-10-10,"['cs.cl', 'cs.ai']",2110.04725v2.pdf,"  Recent work like GPT-3 has demonstrated excellent performance of Zero-Shot
+and Few-Shot learning on many natural language processing (NLP) tasks by
+scaling up model size, dataset size and the amount of computation. However,
+training a model like GPT-3 requires huge amount of computational resources
+which makes it challengeable to researchers. In this work, we propose a method
+that incorporates large-scale distributed training performance into model
+architecture design. With this method, Yuan 1.0, the current largest singleton
+language model with 245B parameters, achieves excellent performance on
+thousands GPUs during training, and the state-of-the-art results on NLP tasks.
+A data processing method is designed to efficiently filter massive amount of
+raw data. The current largest high-quality Chinese corpus with 5TB high quality
+texts is built based on this method. In addition, a calibration and label
+expansion method is proposed to improve the Zero-Shot and Few-Shot performance,
+and steady improvement is observed on the accuracy of various tasks. Yuan 1.0
+presents strong capacity of natural language generation, and the generated
+articles are difficult to distinguish from the human-written ones.
+"
+LiST: Lite Prompted Self-training Makes Parameter-Efficient Few-shot  Learners,Yaqing Wang,http://arxiv.org/pdf/2110.06274v2.pdf,2021-10-12,['cs.cl'],2110.06274v2.pdf,"  We present a new method LiST is short for Lite Prompted Self-Training for
+parameter-efficient fine-tuning of large pre-trained language models (PLMs) for
+few-shot learning. LiST improves over recent methods that adopt prompt-based
+fine-tuning (FN) using two key techniques. The first is the use of
+self-training to leverage large amounts of unlabeled data for prompt-based FN
+in few-shot settings. We use self-training in conjunction with meta-learning
+for re-weighting noisy pseudo-prompt labels. Self-training is expensive as it
+requires updating all the model parameters repetitively. Therefore, we use a
+second technique for light-weight fine-tuning where we introduce a small number
+of task-specific parameters that are fine-tuned during self-training while
+keeping the PLM encoder frozen. Our experiments show that LiST can effectively
+leverage unlabeled data to improve the model performance for few-shot learning.
+Additionally, the fine-tuning is efficient as it only updates a small
+percentage of parameters and the overall model footprint is reduced since
+several tasks can share a common PLM encoder as backbone. A comprehensive study
+on six NLU tasks demonstrate LiST to improve by 35% over classic fine-tuning
+and 6% over prompt-based FN with 96% reduction in number of trainable
+parameters when fine-tuned with no more than 30 labeled examples from each
+task. With only 14M tunable parameters, LiST outperforms GPT-3 in-context
+learning by 33% on few-shot NLU tasks.
+"
+PERFECT: Prompt-free and Efficient Few-shot Learning with Language  Models,Rabeeh Karimi Mahabadi,http://arxiv.org/pdf/2204.01172v2.pdf,2022-04-03,['cs.cl'],2204.01172v2.pdf,"  Current methods for few-shot fine-tuning of pretrained masked language models
+(PLMs) require carefully engineered prompts and verbalizers for each new task
+to convert examples into a cloze-format that the PLM can score. In this work,
+we propose PERFECT, a simple and efficient method for few-shot fine-tuning of
+PLMs without relying on any such handcrafting, which is highly effective given
+as few as 32 data points. PERFECT makes two key design choices: First, we show
+that manually engineered task prompts can be replaced with task-specific
+adapters that enable sample-efficient fine-tuning and reduce memory and storage
+costs by roughly factors of 5 and 100, respectively. Second, instead of using
+handcrafted verbalizers, we learn new multi-token label embeddings during
+fine-tuning, which are not tied to the model vocabulary and which allow us to
+avoid complex auto-regressive decoding. These embeddings are not only learnable
+from limited data but also enable nearly 100x faster training and inference.
+Experiments on a wide range of few-shot NLP tasks demonstrate that PERFECT,
+while being simple and efficient, also outperforms existing state-of-the-art
+few-shot learning methods. Our code is publicly available at
+https://github.com/facebookresearch/perfect.git.
+"
+On the Effect of Pretraining Corpora on In-context Learning by a  Large-scale Language Model,Seongjin Shin,http://arxiv.org/pdf/2204.13509v2.pdf,2022-04-28,['cs.cl'],2204.13509v2.pdf,"  Many recent studies on large-scale language models have reported successful
+in-context zero- and few-shot learning ability. However, the in-depth analysis
+of when in-context learning occurs is still lacking. For example, it is unknown
+how in-context learning performance changes as the training corpus varies.
+Here, we investigate the effects of the source and size of the pretraining
+corpus on in-context learning in HyperCLOVA, a Korean-centric GPT-3 model. From
+our in-depth investigation, we introduce the following observations: (1)
+in-context learning performance heavily depends on the corpus domain source,
+and the size of the pretraining corpus does not necessarily determine the
+emergence of in-context learning, (2) in-context learning ability can emerge
+when a language model is trained on a combination of multiple corpora, even
+when each corpus does not result in in-context learning on its own, (3)
+pretraining with a corpus related to a downstream task does not always
+guarantee the competitive in-context learning performance of the downstream
+task, especially in the few-shot setting, and (4) the relationship between
+language modeling (measured in perplexity) and in-context learning does not
+always correlate: e.g., low perplexity does not always imply high in-context
+few-shot learning performance.
+"
+Few-Shot Stance Detection via Target-Aware Prompt Distillation,Yan Jiang,http://arxiv.org/pdf/2206.13214v1.pdf,2022-06-27,['cs.cl'],2206.13214v1.pdf,"  Stance detection aims to identify whether the author of a text is in favor
+of, against, or neutral to a given target. The main challenge of this task
+comes two-fold: few-shot learning resulting from the varying targets and the
+lack of contextual information of the targets. Existing works mainly focus on
+solving the second issue by designing attention-based models or introducing
+noisy external knowledge, while the first issue remains under-explored. In this
+paper, inspired by the potential capability of pre-trained language models
+(PLMs) serving as knowledge bases and few-shot learners, we propose to
+introduce prompt-based fine-tuning for stance detection. PLMs can provide
+essential contextual information for the targets and enable few-shot learning
+via prompts. Considering the crucial role of the target in stance detection
+task, we design target-aware prompts and propose a novel verbalizer. Instead of
+mapping each label to a concrete word, our verbalizer maps each label to a
+vector and picks the label that best captures the correlation between the
+stance and the target. Moreover, to alleviate the possible defect of dealing
+with varying targets with a single hand-crafted prompt, we propose to distill
+the information learned from multiple prompts. Experimental results show the
+superior performance of our proposed model in both full-data and few-shot
+scenarios.
+"
+Few-Shot Learning for Clinical Natural Language Processing Using Siamese  Neural Networks,David Oniani,http://arxiv.org/pdf/2208.14923v2.pdf,2022-08-31,['cs.cl'],2208.14923v2.pdf,"  Clinical Natural Language Processing (NLP) has become an emerging technology
+in healthcare that leverages a large amount of free-text data in electronic
+health records (EHRs) to improve patient care, support clinical decisions, and
+facilitate clinical and translational science research. Recently, deep learning
+has achieved state-of-the-art performance in many clinical NLP tasks. However,
+training deep learning models usually requires large annotated datasets, which
+are normally not publicly available and can be time-consuming to build in
+clinical domains. Working with smaller annotated datasets is typical in
+clinical NLP and therefore, ensuring that deep learning models perform well is
+crucial for the models to be used in real-world applications. A widely adopted
+approach is fine-tuning existing Pre-trained Language Models (PLMs), but these
+attempts fall short when the training dataset contains only a few annotated
+samples. Few-Shot Learning (FSL) has recently been investigated to tackle this
+problem. Siamese Neural Network (SNN) has been widely utilized as an FSL
+approach in computer vision, but has not been studied well in NLP. Furthermore,
+the literature on its applications in clinical domains is scarce. In this
+paper, we propose two SNN-based FSL approaches for clinical NLP, including
+Pre-Trained SNN (PT-SNN) and SNN with Second-Order Embeddings (SOE-SNN). We
+evaluated the proposed approaches on two clinical tasks, namely clinical text
+classification and clinical named entity recognition. We tested three few-shot
+settings including 4-shot, 8-shot, and 16-shot learning. Both clinical NLP
+tasks were benchmarked using three PLMs, including BERT,BioBERT, and
+BioClinicalBERT. The experimental results verified the effectiveness of the
+proposed SNN-based FSL approaches in both NLP tasks.
+"
+Prompting through Prototype: A Prototype-based Prompt Learning on  Pretrained Vision-Language Models,Yue Zhang,http://arxiv.org/pdf/2210.10841v1.pdf,2022-10-19,"['cs.cl', 'cs.cv']",2210.10841v1.pdf,"  Prompt learning is a new learning paradigm which reformulates downstream
+tasks as similar pretraining tasks on pretrained models by leveraging textual
+prompts. Recent works have demonstrated that prompt learning is particularly
+useful for few-shot learning, where there is limited training data. Depending
+on the granularity of prompts, those methods can be roughly divided into
+task-level prompting and instance-level prompting. Task-level prompting methods
+learn one universal prompt for all input samples, which is efficient but
+ineffective to capture subtle differences among different classes.
+Instance-level prompting methods learn a specific prompt for each input, though
+effective but inefficient. In this work, we develop a novel prototype-based
+prompt learning method to overcome the above limitations. In particular, we
+focus on few-shot image recognition tasks on pretrained vision-language models
+(PVLMs) and develop a method of prompting through prototype (PTP), where we
+define $K$ image prototypes and $K$ prompt prototypes. In PTP, the image
+prototype represents a centroid of a certain image cluster in the latent space
+and a prompt prototype is defined as a soft prompt in the continuous space. The
+similarity between a query image and an image prototype determines how much
+this prediction relies on the corresponding prompt prototype. Hence, in PTP,
+similar images will utilize similar prompting ways. Through extensive
+experiments on seven real-world benchmarks, we show that PTP is an effective
+method to leverage the latent knowledge and adaptive to various PVLMs.
+Moreover, through detailed analysis, we discuss pros and cons for prompt
+learning and parameter-efficient fine-tuning under the context of few-shot
+learning.
+"
+SgVA-CLIP: Semantic-guided Visual Adapting of Vision-Language Models for  Few-shot Image Classification,Fang Peng,http://arxiv.org/pdf/2211.16191v2.pdf,2022-11-28,"['cs.cv', 'cs.mm']",2211.16191v2.pdf,"  Although significant progress has been made in few-shot learning, most of
+existing few-shot image classification methods require supervised pre-training
+on a large amount of samples of base classes, which limits their generalization
+ability in real world application. Recently, large-scale Vision-Language
+Pre-trained models (VLPs) have been gaining increasing attention in few-shot
+learning because they can provide a new paradigm for transferable visual
+representation learning with easily available text on the Web. However, the
+VLPs may neglect detailed visual information that is difficult to describe by
+language sentences, but important for learning an effective classifier to
+distinguish different images. To address the above problem, we propose a new
+framework, named Semantic-guided Visual Adapting (SgVA), which can effectively
+extend vision-language pre-trained models to produce discriminative adapted
+visual features by comprehensively using an implicit knowledge distillation, a
+vision-specific contrastive loss, and a cross-modal contrastive loss. The
+implicit knowledge distillation is designed to transfer the fine-grained
+cross-modal knowledge to guide the updating of the vision adapter.
+State-of-the-art results on 13 datasets demonstrate that the adapted visual
+features can well complement the cross-modal features to improve few-shot image
+classification.
+"
+Finetune like you pretrain: Improved finetuning of zero-shot vision  models,Sachin Goyal,http://arxiv.org/pdf/2212.00638v1.pdf,2022-12-01,"['cs.cv', 'cs.lg']",2212.00638v1.pdf,"  Finetuning image-text models such as CLIP achieves state-of-the-art
+accuracies on a variety of benchmarks. However, recent works like WiseFT
+(Wortsman et al., 2021) and LP-FT (Kumar et al., 2022) have shown that even
+subtle differences in the finetuning process can lead to surprisingly large
+differences in the final performance, both for in-distribution (ID) and
+out-of-distribution (OOD) data. In this work, we show that a natural and simple
+approach of mimicking contrastive pretraining consistently outperforms
+alternative finetuning approaches. Specifically, we cast downstream class
+labels as text prompts and continue optimizing the contrastive loss between
+image embeddings and class-descriptive prompt embeddings (contrastive
+finetuning).
+  Our method consistently outperforms baselines across 7 distribution shifts, 6
+transfer learning, and 3 few-shot learning benchmarks. On WILDS-iWILDCam, our
+proposed approach FLYP outperforms the top of the leaderboard by $2.3\%$ ID and
+$2.7\%$ OOD, giving the highest reported accuracy. Averaged across 7 OOD
+datasets (2 WILDS and 5 ImageNet associated shifts), FLYP gives gains of
+$4.2\%$ OOD over standard finetuning and outperforms the current state of the
+art (LP-FT) by more than $1\%$ both ID and OOD. Similarly, on 3 few-shot
+learning benchmarks, our approach gives gains up to $4.6\%$ over standard
+finetuning and $4.4\%$ over the state of the art. In total, these benchmarks
+establish contrastive finetuning as a simple, intuitive, and state-of-the-art
+approach for supervised finetuning of image-text models like CLIP. Code is
+available at https://github.com/locuslab/FLYP.
+"
+Multimodality Helps Unimodality: Cross-Modal Few-Shot Learning with  Multimodal Models,Zhiqiu Lin,http://arxiv.org/pdf/2301.06267v4.pdf,2023-01-16,"['cs.cv', 'cs.ai', 'cs.lg', 'cs.sd', 'eess.as']",2301.06267v4.pdf,"  The ability to quickly learn a new task with minimal instruction - known as
+few-shot learning - is a central aspect of intelligent agents. Classical
+few-shot benchmarks make use of few-shot samples from a single modality, but
+such samples may not be sufficient to characterize an entire concept class. In
+contrast, humans use cross-modal information to learn new concepts efficiently.
+In this work, we demonstrate that one can indeed build a better ${\bf visual}$
+dog classifier by ${\bf read}$ing about dogs and ${\bf listen}$ing to them
+bark. To do so, we exploit the fact that recent multimodal foundation models
+such as CLIP are inherently cross-modal, mapping different modalities to the
+same representation space. Specifically, we propose a simple cross-modal
+adaptation approach that learns from few-shot examples spanning different
+modalities. By repurposing class names as additional one-shot training samples,
+we achieve SOTA results with an embarrassingly simple linear classifier for
+vision-language adaptation. Furthermore, we show that our approach can benefit
+existing methods such as prefix tuning, adapters, and classifier ensembling.
+Finally, to explore other modalities beyond vision and language, we construct
+the first (to our knowledge) audiovisual few-shot benchmark and use cross-modal
+training to improve the performance of both image and audio classification.
+"
+AugGPT: Leveraging ChatGPT for Text Data Augmentation,Haixing Dai,http://arxiv.org/pdf/2302.13007v3.pdf,2023-02-25,"['cs.cl', 'cs.ai', 'cs.lg']",2302.13007v3.pdf,"  Text data augmentation is an effective strategy for overcoming the challenge
+of limited sample sizes in many natural language processing (NLP) tasks. This
+challenge is especially prominent in the few-shot learning scenario, where the
+data in the target domain is generally much scarcer and of lowered quality. A
+natural and widely-used strategy to mitigate such challenges is to perform data
+augmentation to better capture the data invariance and increase the sample
+size. However, current text data augmentation methods either can't ensure the
+correct labeling of the generated data (lacking faithfulness) or can't ensure
+sufficient diversity in the generated data (lacking compactness), or both.
+Inspired by the recent success of large language models, especially the
+development of ChatGPT, which demonstrated improved language comprehension
+abilities, in this work, we propose a text data augmentation approach based on
+ChatGPT (named AugGPT). AugGPT rephrases each sentence in the training samples
+into multiple conceptually similar but semantically different samples. The
+augmented samples can then be used in downstream model training. Experiment
+results on few-shot learning text classification tasks show the superior
+performance of the proposed AugGPT approach over state-of-the-art text data
+augmentation methods in terms of testing accuracy and distribution of the
+augmented samples.
+"
+Meta Learning to Bridge Vision and Language Models for Multimodal  Few-Shot Learning,Ivona Najdenkoska,http://arxiv.org/pdf/2302.14794v1.pdf,2023-02-28,['cs.cv'],2302.14794v1.pdf,"  Multimodal few-shot learning is challenging due to the large domain gap
+between vision and language modalities. Existing methods are trying to
+communicate visual concepts as prompts to frozen language models, but rely on
+hand-engineered task induction to reduce the hypothesis space. To make the
+whole process learnable, we introduce a multimodal meta-learning approach.
+Specifically, our approach decomposes the training of the model into a set of
+related multimodal few-shot tasks. We define a meta-mapper network, acting as a
+meta-learner, to efficiently bridge frozen large-scale vision and language
+models and leverage their already learned capacity. By updating the learnable
+parameters only of the meta-mapper, it learns to accrue shared meta-knowledge
+among these tasks. Thus, it can rapidly adapt to newly presented samples with
+only a few gradient updates. Importantly, it induces the task in a completely
+data-driven manner, with no need for a hand-engineered task induction. We
+evaluate our approach on recently proposed multimodal few-shot benchmarks,
+measuring how rapidly the model can bind novel visual concepts to words and
+answer visual questions by observing only a limited set of labeled examples.
+The experimental results show that our meta-learning approach outperforms the
+baseline across multiple datasets and various training settings while being
+computationally more efficient.
+"
+Semantic Prompt for Few-Shot Image Recognition,Wentao Chen,http://arxiv.org/pdf/2303.14123v1.pdf,2023-03-24,['cs.cv'],2303.14123v1.pdf,"  Few-shot learning is a challenging problem since only a few examples are
+provided to recognize a new class. Several recent studies exploit additional
+semantic information, e.g. text embeddings of class names, to address the issue
+of rare samples through combining semantic prototypes with visual prototypes.
+However, these methods still suffer from the spurious visual features learned
+from the rare support samples, resulting in limited benefits. In this paper, we
+propose a novel Semantic Prompt (SP) approach for few-shot learning. Instead of
+the naive exploitation of semantic information for remedying classifiers, we
+explore leveraging semantic information as prompts to tune the visual feature
+extraction network adaptively. Specifically, we design two complementary
+mechanisms to insert semantic prompts into the feature extractor: one is to
+enable the interaction between semantic prompts and patch embeddings along the
+spatial dimension via self-attention, another is to supplement visual features
+with the transformed semantic prompts along the channel dimension. By combining
+these two mechanisms, the feature extractor presents a better ability to attend
+to the class-specific features and obtains more generalized image
+representations with merely a few support samples. Through extensive
+experiments on four datasets, the proposed approach achieves promising results,
+improving the 1-shot learning accuracy by 3.67% on average.
+"
+RPLKG: Robust Prompt Learning with Knowledge Graph,Yewon Kim,http://arxiv.org/pdf/2304.10805v1.pdf,2023-04-21,"['cs.ai', 'cs.lg']",2304.10805v1.pdf,"  Large-scale pre-trained models have been known that they are transferable,
+and they generalize well on the unseen dataset. Recently, multimodal
+pre-trained models such as CLIP show significant performance improvement in
+diverse experiments. However, when the labeled dataset is limited, the
+generalization of a new dataset or domain is still challenging. To improve the
+generalization performance on few-shot learning, there have been diverse
+efforts, such as prompt learning and adapter. However, the current few-shot
+adaptation methods are not interpretable, and they require a high computation
+cost for adaptation. In this study, we propose a new method, robust prompt
+learning with knowledge graph (RPLKG). Based on the knowledge graph, we
+automatically design diverse interpretable and meaningful prompt sets. Our
+model obtains cached embeddings of prompt sets after one forwarding from a
+large pre-trained model. After that, model optimizes the prompt selection
+processes with GumbelSoftmax. In this way, our model is trained using
+relatively little memory and learning time. Also, RPLKG selects the optimal
+interpretable prompt automatically, depending on the dataset. In summary, RPLKG
+is i) interpretable, ii) requires small computation resources, and iii) easy to
+incorporate prior human knowledge. To validate the RPLKG, we provide
+comprehensive experimental results on few-shot learning, domain generalization
+and new class generalization setting. RPLKG shows a significant performance
+improvement compared to zero-shot learning and competitive performance against
+several prompt learning methods using much lower resources.
+"
+The CoT Collection: Improving Zero-shot and Few-shot Learning of  Language Models via Chain-of-Thought Fine-Tuning,Seungone Kim,http://arxiv.org/pdf/2305.14045v2.pdf,2023-05-23,"['cs.cl', 'cs.ai', 'cs.lg']",2305.14045v2.pdf,"  Language models (LMs) with less than 100B parameters are known to perform
+poorly on chain-of-thought (CoT) reasoning in contrast to large LMs when
+solving unseen tasks. In this work, we aim to equip smaller LMs with the
+step-by-step reasoning capability by instruction tuning with CoT rationales. In
+order to achieve this goal, we first introduce a new instruction-tuning dataset
+called the CoT Collection, which augments the existing Flan Collection
+(including only 9 CoT tasks) with additional 1.84 million rationales across
+1,060 tasks. We show that CoT fine-tuning Flan-T5 (3B & 11B) with CoT
+Collection enables smaller LMs to have better CoT capabilities on unseen tasks.
+On the BIG-Bench-Hard (BBH) benchmark, we report an average improvement of
++4.34% (Flan-T5 3B) and +2.60% (Flan-T5 11B), in terms of zero-shot task
+accuracy. Furthermore, we show that instruction tuning with CoT Collection
+allows LMs to possess stronger few-shot learning capabilities on 4
+domain-specific tasks, resulting in an improvement of +2.24% (Flan-T5 3B) and
++2.37% (Flan-T5 11B), even outperforming ChatGPT utilizing demonstrations until
+the max length by a +13.98% margin. Our code, the CoT Collection data, and
+model checkpoints are publicly available.
+"
+Adversarial Robustness of Prompt-based Few-Shot Learning for Natural  Language Understanding,Venkata Prabhakara Sarath Nookala,http://arxiv.org/pdf/2306.11066v2.pdf,2023-06-19,"['cs.cl', 'cs.lg']",2306.11066v2.pdf,"  State-of-the-art few-shot learning (FSL) methods leverage prompt-based
+fine-tuning to obtain remarkable results for natural language understanding
+(NLU) tasks. While much of the prior FSL methods focus on improving downstream
+task performance, there is a limited understanding of the adversarial
+robustness of such methods. In this work, we conduct an extensive study of
+several state-of-the-art FSL methods to assess their robustness to adversarial
+perturbations. To better understand the impact of various factors towards
+robustness (or the lack of it), we evaluate prompt-based FSL methods against
+fully fine-tuned models for aspects such as the use of unlabeled data, multiple
+prompts, number of few-shot examples, model size and type. Our results on six
+GLUE tasks indicate that compared to fully fine-tuned models, vanilla FSL
+methods lead to a notable relative drop in task performance (i.e., are less
+robust) in the face of adversarial perturbations. However, using (i) unlabeled
+data for prompt-based FSL and (ii) multiple prompts flip the trend. We further
+demonstrate that increasing the number of few-shot examples and model size lead
+to increased adversarial robustness of vanilla FSL methods. Broadly, our work
+sheds light on the adversarial robustness evaluation of prompt-based FSL
+methods for NLU tasks.
+"
+Few-shot Learning for Inference in Medical Imaging with Subspace Feature  Representations,Jiahui Liu,http://arxiv.org/pdf/2306.11152v1.pdf,2023-06-19,"['math.na', 'cs.na']",2306.11152v1.pdf,"  Unlike the field of visual scene recognition where tremendous advances have
+taken place due to the availability of very large datasets to train deep neural
+networks, inference from medical images is often hampered by the fact that only
+small amounts of data may be available. When working with very small dataset
+problems, of the order of a few hundred items of data, the power of deep
+learning may still be exploited by using a model pre-trained on natural images
+as a feature extractor and carrying out classic pattern recognition techniques
+in this feature space, the so-called few-shot learning problem. In regimes
+where the dimension of this feature space is comparable to or even larger than
+the number of items of data, dimensionality reduction is a necessity and is
+often achieved by principal component analysis, i.e., singular value
+decomposition (SVD). In this paper, noting the inappropriateness of using SVD
+for this setting, we usher in and explore two alternatives based on
+discriminant analysis and non-negative matrix factorization (NMF). Using 14
+different datasets spanning $11$ distinct disease types, we demonstrate that
+discriminant subspaces at low dimensions achieve significant improvements over
+SVD-based subspaces and the original feature space. We also show that NMF at
+modest dimensions is a competitive alternative to SVD in this setting.
+"
+Visually grounded few-shot word learning in low-resource settings,Leanne Nortje,http://arxiv.org/pdf/2306.11371v2.pdf,2023-06-20,"['eess.as', 'cs.cl']",2306.11371v2.pdf,"  We propose a visually grounded speech model that learns new words and their
+visual depictions from just a few word-image example pairs. Given a set of test
+images and a spoken query, we ask the model which image depicts the query word.
+Previous work has simplified this few-shot learning problem by either using an
+artificial setting with digit word-image pairs or by using a large number of
+examples per class. Moreover, all previous studies were performed using English
+speech-image data. We propose an approach that can work on natural word-image
+pairs but with less examples, i.e. fewer shots, and then illustrate how this
+approach can be applied for multimodal few-shot learning in a real low-resource
+language, Yoruba. Our approach involves using the given word-image example
+pairs to mine new unsupervised word-image training pairs from large collections
+of unlabelledspeech and images. Additionally, we use a word-to-image attention
+mechanism to determine word-image similarity. With this new model, we achieve
+better performance with fewer shots than previous approaches on an existing
+English benchmark. Many of the model's mistakes are due to confusion between
+visual concepts co-occurring in similar contexts. The experiments on Yoruba
+show the benefit of transferring knowledge from a multimodal model trained on a
+larger set of English speech-image data.
+"
+Cross-Modal Concept Learning and Inference for Vision-Language Models,Yi Zhang,http://arxiv.org/pdf/2307.15460v1.pdf,2023-07-28,"['cs.cv', 'cs.cl']",2307.15460v1.pdf,"  Large-scale pre-trained Vision-Language Models (VLMs), such as CLIP,
+establish the correlation between texts and images, achieving remarkable
+success on various downstream tasks with fine-tuning. In existing fine-tuning
+methods, the class-specific text description is matched against the whole
+image. We recognize that this whole image matching is not effective since
+images from the same class often contain a set of different semantic objects,
+and an object further consists of a set of semantic parts or concepts.
+Individual semantic parts or concepts may appear in image samples from
+different classes. To address this issue, in this paper, we develop a new
+method called cross-model concept learning and inference (CCLI). Using the
+powerful text-image correlation capability of CLIP, our method automatically
+learns a large set of distinctive visual concepts from images using a set of
+semantic text concepts. Based on these visual concepts, we construct a
+discriminative representation of images and learn a concept inference network
+to perform downstream image classification tasks, such as few-shot learning and
+domain generalization. Extensive experimental results demonstrate that our CCLI
+method is able to improve the performance upon the current state-of-the-art
+methods by large margins, for example, by up to 8.0% improvement on few-shot
+learning and by up to 1.3% for domain generalization.
+"
+Demonstration-based learning for few-shot biomedical named entity  recognition under machine reading comprehension,Leilei Su,http://arxiv.org/pdf/2308.06454v1.pdf,2023-08-12,['cs.cl'],2308.06454v1.pdf,"  Although deep learning techniques have shown significant achievements, they
+frequently depend on extensive amounts of hand-labeled data and tend to perform
+inadequately in few-shot scenarios. The objective of this study is to devise a
+strategy that can improve the model's capability to recognize biomedical
+entities in scenarios of few-shot learning. By redefining biomedical named
+entity recognition (BioNER) as a machine reading comprehension (MRC) problem,
+we propose a demonstration-based learning method to address few-shot BioNER,
+which involves constructing appropriate task demonstrations. In assessing our
+proposed method, we compared the proposed method with existing advanced methods
+using six benchmark datasets, including BC4CHEMD, BC5CDR-Chemical,
+BC5CDR-Disease, NCBI-Disease, BC2GM, and JNLPBA. We examined the models'
+efficacy by reporting F1 scores from both the 25-shot and 50-shot learning
+experiments. In 25-shot learning, we observed 1.1% improvements in the average
+F1 scores compared to the baseline method, reaching 61.7%, 84.1%, 69.1%, 70.1%,
+50.6%, and 59.9% on six datasets, respectively. In 50-shot learning, we further
+improved the average F1 scores by 1.0% compared to the baseline method,
+reaching 73.1%, 86.8%, 76.1%, 75.6%, 61.7%, and 65.4%, respectively. We
+reported that in the realm of few-shot learning BioNER, MRC-based language
+models are much more proficient in recognizing biomedical entities compared to
+the sequence labeling approach. Furthermore, our MRC-language models can
+compete successfully with fully-supervised learning methodologies that rely
+heavily on the availability of abundant annotated data. These results highlight
+possible pathways for future advancements in few-shot BioNER methodologies.
+"
+Robustness Over Time: Understanding Adversarial Examples' Effectiveness  on Longitudinal Versions of Large Language Models,Yugeng Liu,http://arxiv.org/pdf/2308.07847v1.pdf,2023-08-15,['cs.cr'],2308.07847v1.pdf,"  Large Language Models (LLMs) have led to significant improvements in many
+tasks across various domains, such as code interpretation, response generation,
+and ambiguity handling. These LLMs, however, when upgrading, primarily
+prioritize enhancing user experience while neglecting security, privacy, and
+safety implications. Consequently, unintended vulnerabilities or biases can be
+introduced. Previous studies have predominantly focused on specific versions of
+the models and disregard the potential emergence of new attack vectors
+targeting the updated versions. Through the lens of adversarial examples within
+the in-context learning framework, this longitudinal study addresses this gap
+by conducting a comprehensive assessment of the robustness of successive
+versions of LLMs, vis-\`a-vis GPT-3.5. We conduct extensive experiments to
+analyze and understand the impact of the robustness in two distinct learning
+categories: zero-shot learning and few-shot learning. Our findings indicate
+that, in comparison to earlier versions of LLMs, the updated versions do not
+exhibit the anticipated level of robustness against adversarial attacks. In
+addition, our study emphasizes the increased effectiveness of synergized
+adversarial queries in most zero-shot learning and few-shot learning cases. We
+hope that our study can lead to a more refined assessment of the robustness of
+LLMs over time and provide valuable insights of these models for both
+developers and users.
+"
+UniAP: Towards Universal Animal Perception in Vision via Few-shot  Learning,Meiqi Sun,http://arxiv.org/pdf/2308.09953v1.pdf,2023-08-19,['cs.cv'],2308.09953v1.pdf,"  Animal visual perception is an important technique for automatically
+monitoring animal health, understanding animal behaviors, and assisting
+animal-related research. However, it is challenging to design a deep
+learning-based perception model that can freely adapt to different animals
+across various perception tasks, due to the varying poses of a large diversity
+of animals, lacking data on rare species, and the semantic inconsistency of
+different tasks. We introduce UniAP, a novel Universal Animal Perception model
+that leverages few-shot learning to enable cross-species perception among
+various visual tasks. Our proposed model takes support images and labels as
+prompt guidance for a query image. Images and labels are processed through a
+Transformer-based encoder and a lightweight label encoder, respectively. Then a
+matching module is designed for aggregating information between prompt guidance
+and the query image, followed by a multi-head label decoder to generate outputs
+for various tasks. By capitalizing on the shared visual characteristics among
+different animals and tasks, UniAP enables the transfer of knowledge from
+well-studied species to those with limited labeled data or even unseen species.
+We demonstrate the effectiveness of UniAP through comprehensive experiments in
+pose estimation, segmentation, and classification tasks on diverse animal
+species, showcasing its ability to generalize and adapt to new classes with
+minimal labeled examples.
+"
+PaLM: Scaling Language Modeling with Pathways,Aakanksha Chowdhery,http://arxiv.org/pdf/2204.02311v5.pdf,2022-04-05,['cs.cl'],2204.02311v5.pdf,"  Large language models have been shown to achieve remarkable performance
+across a variety of natural language tasks using few-shot learning, which
+drastically reduces the number of task-specific training examples needed to
+adapt the model to a particular application. To further our understanding of
+the impact of scale on few-shot learning, we trained a 540-billion parameter,
+densely activated, Transformer language model, which we call Pathways Language
+Model PaLM. We trained PaLM on 6144 TPU v4 chips using Pathways, a new ML
+system which enables highly efficient training across multiple TPU Pods. We
+demonstrate continued benefits of scaling by achieving state-of-the-art
+few-shot learning results on hundreds of language understanding and generation
+benchmarks. On a number of these tasks, PaLM 540B achieves breakthrough
+performance, outperforming the finetuned state-of-the-art on a suite of
+multi-step reasoning tasks, and outperforming average human performance on the
+recently released BIG-bench benchmark. A significant number of BIG-bench tasks
+showed discontinuous improvements from model scale, meaning that performance
+steeply increased as we scaled to our largest model. PaLM also has strong
+capabilities in multilingual tasks and source code generation, which we
+demonstrate on a wide array of benchmarks. We additionally provide a
+comprehensive analysis on bias and toxicity, and study the extent of training
+data memorization with respect to model scale. Finally, we discuss the ethical
+considerations related to large language models and discuss potential
+mitigation strategies.
+"
+Few-Shot Electronic Health Record Coding through Graph Contrastive  Learning,Shanshan Wang,http://arxiv.org/pdf/2106.15467v1.pdf,2021-06-29,"['cs.ai', 'cs.cl']",2106.15467v1.pdf,"  Electronic health record (EHR) coding is the task of assigning ICD codes to
+each EHR. Most previous studies either only focus on the frequent ICD codes or
+treat rare and frequent ICD codes in the same way. These methods perform well
+on frequent ICD codes but due to the extremely unbalanced distribution of ICD
+codes, the performance on rare ones is far from satisfactory. We seek to
+improve the performance for both frequent and rare ICD codes by using a
+contrastive graph-based EHR coding framework, CoGraph, which re-casts EHR
+coding as a few-shot learning task. First, we construct a heterogeneous EHR
+word-entity (HEWE) graph for each EHR, where the words and entities extracted
+from an EHR serve as nodes and the relations between them serve as edges. Then,
+CoGraph learns similarities and dissimilarities between HEWE graphs from
+different ICD codes so that information can be transferred among them. In a
+few-shot learning scenario, the model only has access to frequent ICD codes
+during training, which might force it to encode features that are useful for
+frequent ICD codes only. To mitigate this risk, CoGraph devises two graph
+contrastive learning schemes, GSCL and GECL, that exploit the HEWE graph
+structures so as to encode transferable features. GSCL utilizes the
+intra-correlation of different sub-graphs sampled from HEWE graphs while GECL
+exploits the inter-correlation among HEWE graphs at different clinical stages.
+Experiments on the MIMIC-III benchmark dataset show that CoGraph significantly
+outperforms state-of-the-art methods on EHR coding, not only on frequent ICD
+codes, but also on rare codes, in terms of several evaluation indicators. On
+frequent ICD codes, GSCL and GECL improve the classification accuracy and F1 by
+1.31% and 0.61%, respectively, and on rare ICD codes CoGraph has more obvious
+improvements by 2.12% and 2.95%.
+"
+ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language  Understanding and Generation,Yu Sun,http://arxiv.org/pdf/2107.02137v1.pdf,2021-07-05,['cs.cl'],2107.02137v1.pdf,"  Pre-trained models have achieved state-of-the-art results in various Natural
+Language Processing (NLP) tasks. Recent works such as T5 and GPT-3 have shown
+that scaling up pre-trained language models can improve their generalization
+abilities. Particularly, the GPT-3 model with 175 billion parameters shows its
+strong task-agnostic zero-shot/few-shot learning capabilities. Despite their
+success, these large-scale models are trained on plain texts without
+introducing knowledge such as linguistic knowledge and world knowledge. In
+addition, most large-scale models are trained in an auto-regressive way. As a
+result, this kind of traditional fine-tuning approach demonstrates relatively
+weak performance when solving downstream language understanding tasks. In order
+to solve the above problems, we propose a unified framework named ERNIE 3.0 for
+pre-training large-scale knowledge enhanced models. It fuses auto-regressive
+network and auto-encoding network, so that the trained model can be easily
+tailored for both natural language understanding and generation tasks with
+zero-shot learning, few-shot learning or fine-tuning. We trained the model with
+10 billion parameters on a 4TB corpus consisting of plain texts and a
+large-scale knowledge graph. Empirical results show that the model outperforms
+the state-of-the-art models on 54 Chinese NLP tasks, and its English version
+achieves the first place on the SuperGLUE benchmark (July 3, 2021), surpassing
+the human performance by +0.8% (90.6% vs. 89.8%).
+"
+UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding  with Text-to-Text Language Models,Tianbao Xie,http://arxiv.org/pdf/2201.05966v3.pdf,2022-01-16,['cs.cl'],2201.05966v3.pdf,"  Structured knowledge grounding (SKG) leverages structured knowledge to
+complete user requests, such as semantic parsing over databases and question
+answering over knowledge bases. Since the inputs and outputs of SKG tasks are
+heterogeneous, they have been studied separately by different communities,
+which limits systematic and compatible research on SKG. In this paper, we
+overcome this limitation by proposing the UnifiedSKG framework, which unifies
+21 SKG tasks into a text-to-text format, aiming to promote systematic SKG
+research, instead of being exclusive to a single task, domain, or dataset. We
+use UnifiedSKG to benchmark T5 with different sizes and show that T5, with
+simple modifications when necessary, achieves state-of-the-art performance on
+almost all of the 21 tasks. We further demonstrate that multi-task
+prefix-tuning improves the performance on most tasks, largely improving the
+overall performance. UnifiedSKG also facilitates the investigation of zero-shot
+and few-shot learning, and we show that T0, GPT-3, and Codex struggle in
+zero-shot and few-shot learning for SKG. We also use UnifiedSKG to conduct a
+series of controlled experiments on structured knowledge encoding variants
+across SKG tasks. UnifiedSKG is easily extensible to more tasks, and it is
+open-sourced at https://github.com/hkunlp/unifiedskg.
+"
+A Prompt-based Few-shot Learning Approach to Software Conflict Detection,Robert K. Helmeczi,http://arxiv.org/pdf/2211.02709v1.pdf,2022-11-04,['cs.se'],2211.02709v1.pdf,"  A software requirement specification (SRS) document is an essential part of
+the software development life cycle which outlines the requirements that a
+software program in development must satisfy. This document is often specified
+by a diverse group of stakeholders and is subject to continual change, making
+the process of maintaining the document and detecting conflicts between
+requirements an essential task in software development. Notably, projects that
+do not address conflicts in the SRS document early on face considerable
+problems later in the development life cycle. These problems incur substantial
+costs in terms of time and money, and these costs often become insurmountable
+barriers that ultimately result in the termination of a software project
+altogether. As a result, early detection of SRS conflicts is critical to
+project sustainability. The conflict detection task is approached in numerous
+ways, many of which require a significant amount of manual intervention from
+developers, or require access to a large amount of labeled, task-specific
+training data. In this work, we propose using a prompt-based learning approach
+to perform few-shot learning for conflict detection. We compare our results to
+supervised learning approaches that use pretrained language models, such as
+BERT and its variants. Our results show that prompting with just 32 labeled
+examples can achieve a similar level of performance in many key metrics to that
+of supervised learning on training sets that are magnitudes larger in size. In
+contrast to many other conflict detection approaches, we make no assumptions
+about the type of underlying requirements, allowing us to analyze pairings of
+both functional and non-functional requirements. This allows us to omit the
+potentially expensive task of filtering out non-functional requirements from
+our dataset.
+"
+"Cross-Lingual Alignment of Contextual Word Embeddings, with Applications  to Zero-shot Dependency Parsing",Tal Schuster,http://arxiv.org/pdf/1902.09492v2.pdf,2019-02-25,"['cs.cl', 'cs.lg']",1902.09492v2.pdf,"  We introduce a novel method for multilingual transfer that utilizes deep
+contextual embeddings, pretrained in an unsupervised fashion. While contextual
+embeddings have been shown to yield richer representations of meaning compared
+to their static counterparts, aligning them poses a challenge due to their
+dynamic nature. To this end, we construct context-independent variants of the
+original monolingual spaces and utilize their mapping to derive an alignment
+for the context-dependent spaces. This mapping readily supports processing of a
+target language, improving transfer by context-aware embeddings. Our
+experimental results demonstrate the effectiveness of this approach for
+zero-shot and few-shot learning of dependency parsing. Specifically, our method
+consistently outperforms the previous state-of-the-art on 6 tested languages,
+yielding an improvement of 6.8 LAS points on average.
+"
+Few-shot Natural Language Generation for Task-Oriented Dialog,Baolin Peng,http://arxiv.org/pdf/2002.12328v1.pdf,2020-02-27,['cs.cl'],2002.12328v1.pdf,"  As a crucial component in task-oriented dialog systems, the Natural Language
+Generation (NLG) module converts a dialog act represented in a semantic form
+into a response in natural language. The success of traditional template-based
+or statistical models typically relies on heavily annotated data, which is
+infeasible for new domains. Therefore, it is pivotal for an NLG system to
+generalize well with limited labelled data in real applications. To this end,
+we present FewShotWoz, the first NLG benchmark to simulate the few-shot
+learning setting in task-oriented dialog systems. Further, we develop the
+SC-GPT model. It is pre-trained on a large set of annotated NLG corpus to
+acquire the controllable generation ability, and fine-tuned with only a few
+domain-specific labels to adapt to new domains. Experiments on FewShotWoz and
+the large Multi-Domain-WOZ datasets show that the proposed SC-GPT significantly
+outperforms existing methods, measured by various automatic metrics and human
+evaluations.
+"
+Alleviating the Incompatibility between Cross Entropy Loss and Episode  Training for Few-shot Skin Disease Classification,Wei Zhu,http://arxiv.org/pdf/2004.09694v1.pdf,2020-04-21,"['eess.iv', 'cs.cv', 'cs.lg']",2004.09694v1.pdf,"  Skin disease classification from images is crucial to dermatological
+diagnosis. However, identifying skin lesions involves a variety of aspects in
+terms of size, color, shape, and texture. To make matters worse, many
+categories only contain very few samples, posing great challenges to
+conventional machine learning algorithms and even human experts. Inspired by
+the recent success of Few-Shot Learning (FSL) in natural image classification,
+we propose to apply FSL to skin disease identification to address the extreme
+scarcity of training sample problem. However, directly applying FSL to this
+task does not work well in practice, and we find that the problem can be
+largely attributed to the incompatibility between Cross Entropy (CE) and
+episode training, which are both commonly used in FSL. Based on a detailed
+analysis, we propose the Query-Relative (QR) loss, which proves superior to CE
+under episode training and is closely related to recently proposed mutual
+information estimation. Moreover, we further strengthen the proposed QR loss
+with a novel adaptive hard margin strategy. Comprehensive experiments validate
+the effectiveness of the proposed FSL scheme and the possibility to diagnosis
+rare skin disease with a few labeled samples.
+"
+Few-shot learning through contextual data augmentation,Farid Arthaud,http://arxiv.org/pdf/2103.16911v1.pdf,2021-03-31,['cs.cl'],2103.16911v1.pdf,"  Machine translation (MT) models used in industries with constantly changing
+topics, such as translation or news agencies, need to adapt to new data to
+maintain their performance over time. Our aim is to teach a pre-trained MT
+model to translate previously unseen words accurately, based on very few
+examples. We propose (i) an experimental setup allowing us to simulate novel
+vocabulary appearing in human-submitted translations, and (ii) corresponding
+evaluation metrics to compare our approaches. We extend a data augmentation
+approach using a pre-trained language model to create training examples with
+similar contexts for novel words. We compare different fine-tuning and data
+augmentation approaches and show that adaptation on the scale of one to five
+examples is possible. Combining data augmentation with randomly selected
+training sentences leads to the highest BLEU score and accuracy improvements.
+Impressively, with only 1 to 5 examples, our model reports better accuracy
+scores than a reference system trained with on average 313 parallel examples.
+"
+Meta-Learning GNN Initializations for Low-Resource Molecular Property  Prediction,Cuong Q. Nguyen,http://arxiv.org/pdf/2003.05996v2.pdf,2020-03-12,"['cs.lg', 'physics.chem-ph', 'stat.ml']",2003.05996v2.pdf,"  Building in silico models to predict chemical properties and activities is a
+crucial step in drug discovery. However, limited labeled data often hinders the
+application of deep learning in this setting. Meanwhile advances in
+meta-learning have enabled state-of-the-art performances in few-shot learning
+benchmarks, naturally prompting the question: Can meta-learning improve deep
+learning performance in low-resource drug discovery projects? In this work, we
+assess the transferability of graph neural networks initializations learned by
+the Model-Agnostic Meta-Learning (MAML) algorithm - and its variants FO-MAML
+and ANIL - for chemical properties and activities tasks. Using the ChEMBL20
+dataset to emulate low-resource settings, our benchmark shows that
+meta-initializations perform comparably to or outperform multi-task
+pre-training baselines on 16 out of 20 in-distribution tasks and on all
+out-of-distribution tasks, providing an average improvement in AUPRC of 11.2%
+and 26.9% respectively. Finally, we observe that meta-initializations
+consistently result in the best performing models across fine-tuning sets with
+$k \in \{16, 32, 64, 128, 256\}$ instances.
+"
+Neural Data Augmentation via Example Extrapolation,Kenton Lee,http://arxiv.org/pdf/2102.01335v1.pdf,2021-02-02,"['cs.cl', 'cs.ai']",2102.01335v1.pdf,"  In many applications of machine learning, certain categories of examples may
+be underrepresented in the training data, causing systems to underperform on
+such ""few-shot"" cases at test time. A common remedy is to perform data
+augmentation, such as by duplicating underrepresented examples, or
+heuristically synthesizing new examples. But these remedies often fail to cover
+the full diversity and complexity of real examples.
+  We propose a data augmentation approach that performs neural Example
+Extrapolation (Ex2). Given a handful of exemplars sampled from some
+distribution, Ex2 synthesizes new examples that also belong to the same
+distribution. The Ex2 model is learned by simulating the example generation
+procedure on data-rich slices of the data, and it is applied to
+underrepresented, few-shot slices.
+  We apply Ex2 to a range of language understanding tasks and significantly
+improve over state-of-the-art methods on multiple few-shot learning benchmarks,
+including for relation extraction (FewRel) and intent classification + slot
+filling (SNIPS).
+"
+One-shot learning for the long term: consolidation with an artificial  hippocampal algorithm,Gideon Kowadlo,http://arxiv.org/pdf/2102.07503v2.pdf,2021-02-15,"['cs.lg', 'cs.ai', 'cs.ne', 'i.2.6; i.5.0; i.5.1']",2102.07503v2.pdf,"  Standard few-shot experiments involve learning to efficiently match
+previously unseen samples by class. We claim that few-shot learning should be
+long term, assimilating knowledge for the future, without forgetting previous
+concepts. In the mammalian brain, the hippocampus is understood to play a
+significant role in this process, by learning rapidly and consolidating
+knowledge to the neocortex incrementally over a short period. In this research
+we tested whether an artificial hippocampal algorithm (AHA), could be used with
+a conventional Machine Learning (ML) model that learns incrementally analogous
+to the neocortex, to achieve one-shot learning both short and long term. The
+results demonstrated that with the addition of AHA, the system could learn in
+one-shot and consolidate the knowledge for the long term without catastrophic
+forgetting. This study is one of the first examples of using a CLS model of
+hippocampus to consolidate memories, and it constitutes a step toward few-shot
+continual learning.
+"
+Calibrate Before Use: Improving Few-Shot Performance of Language Models,Tony Z. Zhao,http://arxiv.org/pdf/2102.09690v2.pdf,2021-02-19,"['cs.cl', 'cs.lg']",2102.09690v2.pdf,"  GPT-3 can perform numerous tasks when provided a natural language prompt that
+contains a few training examples. We show that this type of few-shot learning
+can be unstable: the choice of prompt format, training examples, and even the
+order of the training examples can cause accuracy to vary from near chance to
+near state-of-the-art. We demonstrate that this instability arises from the
+bias of language models towards predicting certain answers, e.g., those that
+are placed near the end of the prompt or are common in the pre-training data.
+To mitigate this, we first estimate the model's bias towards each answer by
+asking for its prediction when given the training prompt and a content-free
+test input such as ""N/A"". We then fit calibration parameters that cause the
+prediction for this input to be uniform across answers. On a diverse set of
+tasks, this contextual calibration procedure substantially improves GPT-3 and
+GPT-2's average accuracy (up to 30.0% absolute) and reduces variance across
+different choices of the prompt.
+"
+The Power of Scale for Parameter-Efficient Prompt Tuning,Brian Lester,http://arxiv.org/pdf/2104.08691v2.pdf,2021-04-18,['cs.cl'],2104.08691v2.pdf,"  In this work, we explore ""prompt tuning"", a simple yet effective mechanism
+for learning ""soft prompts"" to condition frozen language models to perform
+specific downstream tasks. Unlike the discrete text prompts used by GPT-3, soft
+prompts are learned through backpropagation and can be tuned to incorporate
+signal from any number of labeled examples. Our end-to-end learned approach
+outperforms GPT-3's ""few-shot"" learning by a large margin. More remarkably,
+through ablations on model size using T5, we show that prompt tuning becomes
+more competitive with scale: as models exceed billions of parameters, our
+method ""closes the gap"" and matches the strong performance of model tuning
+(where all model weights are tuned). This finding is especially relevant in
+that large models are costly to share and serve, and the ability to reuse one
+frozen model for multiple downstream tasks can ease this burden. Our method can
+be seen as a simplification of the recently proposed ""prefix tuning"" of Li and
+Liang (2021), and we provide a comparison to this and other similar approaches.
+Finally, we show that conditioning a frozen model with soft prompts confers
+benefits in robustness to domain transfer, as compared to full model tuning.
+"
+What's in a Measurement? Using GPT-3 on SemEval 2021 Task 8 -- MeasEval,Curt Kohler,http://arxiv.org/pdf/2106.14720v1.pdf,2021-06-28,['cs.cl'],2106.14720v1.pdf,"  In the summer of 2020 OpenAI released its GPT-3 autoregressive language model
+to much fanfare. While the model has shown promise on tasks in several areas,
+it has not always been clear when the results were cherry-picked or when they
+were the unvarnished output. We were particularly interested in what benefits
+GPT-3 could bring to the SemEval 2021 MeasEval task - identifying measurements
+and their associated attributes in scientific literature. We had already
+experimented with multi-turn questions answering as a solution to this task. We
+wanted to see if we could use GPT-3's few-shot learning capabilities to more
+easily develop a solution that would have better performance than our prior
+work. Unfortunately, we have not been successful in that effort. This paper
+discusses the approach we used, challenges we encountered, and results we
+observed. Some of the problems we encountered were simply due to the state of
+the art. For example, the limits on the size of the prompt and answer limited
+the amount of the training signal that could be offered. Others are more
+fundamental. We are unaware of generative models that excel in retaining
+factual information. Also, the impact of changes in the prompts is
+unpredictable, making it hard to reliably improve performance.
+"
+FLEX: Unifying Evaluation for Few-Shot NLP,Jonathan Bragg,http://arxiv.org/pdf/2107.07170v2.pdf,2021-07-15,"['cs.cl', 'cs.lg', 'i.2.7']",2107.07170v2.pdf,"  Few-shot NLP research is highly active, yet conducted in disjoint research
+threads with evaluation suites that lack challenging-yet-realistic testing
+setups and fail to employ careful experimental design. Consequently, the
+community does not know which techniques perform best or even if they
+outperform simple baselines. In response, we formulate the FLEX Principles, a
+set of requirements and best practices for unified, rigorous, valid, and
+cost-sensitive few-shot NLP evaluation. These principles include Sample Size
+Design, a novel approach to benchmark design that optimizes statistical
+accuracy and precision while keeping evaluation costs manageable. Following the
+principles, we release the FLEX benchmark, which includes four few-shot
+transfer settings, zero-shot evaluation, and a public leaderboard that covers
+diverse NLP tasks. In addition, we present UniFew, a prompt-based model for
+few-shot learning that unifies pretraining and finetuning prompt formats,
+eschewing complex machinery of recent prompt-based approaches in adapting
+downstream task formats to language model pretraining objectives. We
+demonstrate that despite simplicity, UniFew achieves results competitive with
+both popular meta-learning and prompt-based approaches.
+"
+Wordcraft: a Human-AI Collaborative Editor for Story Writing,Andy Coenen,http://arxiv.org/pdf/2107.07430v1.pdf,2021-07-15,['cs.cl'],2107.07430v1.pdf,"  As neural language models grow in effectiveness, they are increasingly being
+applied in real-world settings. However these applications tend to be limited
+in the modes of interaction they support. In this extended abstract, we propose
+Wordcraft, an AI-assisted editor for story writing in which a writer and a
+dialog system collaborate to write a story. Our novel interface uses few-shot
+learning and the natural affordances of conversation to support a variety of
+interactions. Our editor provides a sandbox for writers to probe the boundaries
+of transformer-based language models and paves the way for future
+human-in-the-loop training pipelines and novel evaluation methods.
+"
+Design of a Graphical User Interface for Few-Shot Machine Learning  Classification of Electron Microscopy Data,Christina Doty,http://arxiv.org/pdf/2107.10387v1.pdf,2021-07-21,"['cond-mat.mtrl-sci', 'cs.lg']",2107.10387v1.pdf,"  The recent growth in data volumes produced by modern electron microscopes
+requires rapid, scalable, and flexible approaches to image segmentation and
+analysis. Few-shot machine learning, which can richly classify images from a
+handful of user-provided examples, is a promising route to high-throughput
+analysis. However, current command-line implementations of such approaches can
+be slow and unintuitive to use, lacking the real-time feedback necessary to
+perform effective classification. Here we report on the development of a
+Python-based graphical user interface that enables end users to easily conduct
+and visualize the output of few-shot learning models. This interface is
+lightweight and can be hosted locally or on the web, providing the opportunity
+to reproducibly conduct, share, and crowd-source few-shot analyses.
+"
+Noisy Channel Language Model Prompting for Few-Shot Text Classification,Sewon Min,http://arxiv.org/pdf/2108.04106v3.pdf,2021-08-09,"['cs.cl', 'cs.ai']",2108.04106v3.pdf,"  We introduce a noisy channel approach for language model prompting in
+few-shot text classification. Instead of computing the likelihood of the label
+given the input (referred as direct models), channel models compute the
+conditional probability of the input given the label, and are thereby required
+to explain every word in the input. We use channel models for recently proposed
+few-shot learning methods with no or very limited updates to the language model
+parameters, via either in-context demonstration or prompt tuning. Our
+experiments show that, for both methods, channel models significantly
+outperform their direct counterparts, which we attribute to their stability,
+i.e., lower variance and higher worst-case accuracy. We also present extensive
+ablations that provide recommendations for when to use channel prompt tuning
+instead of other competitive methods (e.g., direct head tuning): channel prompt
+tuning is preferred when the number of training examples is small, labels in
+the training data are imbalanced, or generalization to unseen labels is
+required.
+"
+FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning,Jing Zhou,http://arxiv.org/pdf/2108.06332v2.pdf,2021-08-13,['cs.cl'],2108.06332v2.pdf,"  Most previous methods for text data augmentation are limited to simple tasks
+and weak baselines. We explore data augmentation on hard tasks (i.e., few-shot
+natural language understanding) and strong baselines (i.e., pretrained models
+with over one billion parameters). Under this setting, we reproduced a large
+number of previous augmentation methods and found that these methods bring
+marginal gains at best and sometimes degrade the performance much. To address
+this challenge, we propose a novel data augmentation method FlipDA that jointly
+uses a generative model and a classifier to generate label-flipped data.
+Central to the idea of FlipDA is the discovery that generating label-flipped
+data is more crucial to the performance than generating label-preserved data.
+Experiments show that FlipDA achieves a good tradeoff between effectiveness and
+robustness -- it substantially improves many tasks while not negatively
+affecting the others.
+"
+On the Multilingual Capabilities of Very Large-Scale English Language  Models,Jordi Armengol-Estapé,http://arxiv.org/pdf/2108.13349v1.pdf,2021-08-30,"['cs.cl', 'cs.ai']",2108.13349v1.pdf,"  Generative Pre-trained Transformers (GPTs) have recently been scaled to
+unprecedented sizes in the history of machine learning. These models, solely
+trained on the language modeling objective, have been shown to exhibit
+outstanding few-shot learning capabilities in a number of different tasks.
+Nevertheless, aside from anecdotal experiences, little is known regarding their
+multilingual capabilities, given the fact that the pre-training corpus is
+almost entirely composed of English text. In this work, we investigate the
+multilingual skills of GPT-3, focusing on one language that barely appears in
+the pre-training corpus, Catalan, which makes the results especially
+meaningful; we assume that our results may be relevant for other languages as
+well. We find that the model shows an outstanding performance, particularly in
+generative tasks, with predictable limitations mostly in language understanding
+tasks but still with remarkable results given the zero-shot scenario. We
+investigate its potential and limits in extractive question-answering and
+natural language generation, as well as the effect of scale in terms of model
+size.
+"
+Want To Reduce Labeling Cost? GPT-3 Can Help,Shuohang Wang,http://arxiv.org/pdf/2108.13487v1.pdf,2021-08-30,"['cs.cl', 'cs.ai']",2108.13487v1.pdf,"  Data annotation is a time-consuming and labor-intensive process for many NLP
+tasks. Although there exist various methods to produce pseudo data labels, they
+are often task-specific and require a decent amount of labeled data to start
+with. Recently, the immense language model GPT-3 with 175 billion parameters
+has achieved tremendous improvement across many few-shot learning tasks. In
+this paper, we explore ways to leverage GPT-3 as a low-cost data labeler to
+train other models. We find that, to make the downstream model achieve the same
+performance on a variety of NLU and NLG tasks, it costs 50% to 96% less to use
+labels from GPT-3 than using labels from humans. Furthermore, we propose a
+novel framework of combining pseudo labels from GPT-3 with human labels, which
+leads to even better performance with limited labeling budget. These results
+present a cost-effective data labeling methodology that is generalizable to
+many practical applications.
+"
+ConQX: Semantic Expansion of Spoken Queries for Intent Detection based  on Conditioned Text Generation,Eyup Halit Yilmaz,http://arxiv.org/pdf/2109.00729v1.pdf,2021-09-02,"['cs.cl', 'cs.ai']",2109.00729v1.pdf,"  Intent detection of spoken queries is a challenging task due to their noisy
+structure and short length. To provide additional information regarding the
+query and enhance the performance of intent detection, we propose a method for
+semantic expansion of spoken queries, called ConQX, which utilizes the text
+generation ability of an auto-regressive language model, GPT-2. To avoid
+off-topic text generation, we condition the input query to a structured context
+with prompt mining. We then apply zero-shot, one-shot, and few-shot learning.
+We lastly use the expanded queries to fine-tune BERT and RoBERTa for intent
+detection. The experimental results show that the performance of intent
+detection can be improved by our semantic expansion method.
+"
+Do Prompt-Based Models Really Understand the Meaning of their Prompts?,Albert Webson,http://arxiv.org/pdf/2109.01247v2.pdf,2021-09-02,['cs.cl'],2109.01247v2.pdf,"  Recently, a boom of papers has shown extraordinary progress in zero-shot and
+few-shot learning with various prompt-based models. It is commonly argued that
+prompts help models to learn faster in the same way that humans learn faster
+when provided with task instructions expressed in natural language. In this
+study, we experiment with over 30 prompt templates manually written for natural
+language inference (NLI). We find that models learn just as fast with many
+prompts that are intentionally irrelevant or even pathologically misleading as
+they do with instructively ""good"" prompts. Further, such patterns hold even for
+models as large as 175 billion parameters (Brown et al., 2020) as well as the
+recently proposed instruction-tuned models which are trained on hundreds of
+prompts (Sanh et al., 2022). That is, instruction-tuned models often produce
+good predictions with irrelevant and misleading prompts even at zero shots. In
+sum, notwithstanding prompt-based models' impressive improvement, we find
+evidence of serious limitations that question the degree to which such
+improvement is derived from models understanding task instructions in ways
+analogous to humans' use of task instructions.
+"
+Learning Opinion Summarizers by Selecting Informative Reviews,Arthur Bražinskas,http://arxiv.org/pdf/2109.04325v1.pdf,2021-09-09,"['cs.cl', 'cs.ai', 'cs.lg']",2109.04325v1.pdf,"  Opinion summarization has been traditionally approached with unsupervised,
+weakly-supervised and few-shot learning techniques. In this work, we collect a
+large dataset of summaries paired with user reviews for over 31,000 products,
+enabling supervised training. However, the number of reviews per product is
+large (320 on average), making summarization - and especially training a
+summarizer - impractical. Moreover, the content of many reviews is not
+reflected in the human-written summaries, and, thus, the summarizer trained on
+random review subsets hallucinates. In order to deal with both of these
+challenges, we formulate the task as jointly learning to select informative
+subsets of reviews and summarizing the opinions expressed in these subsets. The
+choice of the review subset is treated as a latent variable, predicted by a
+small and simple selector. The subset is then fed into a more powerful
+summarizer. For joint training, we use amortized variational inference and
+policy gradient methods. Our experiments demonstrate the importance of
+selecting informative reviews resulting in improved quality of summaries and
+reduced hallucinations.
+"
+STraTA: Self-Training with Task Augmentation for Better Few-shot  Learning,Tu Vu,http://arxiv.org/pdf/2109.06270v2.pdf,2021-09-13,['cs.cl'],2109.06270v2.pdf,"  Despite their recent successes in tackling many NLP tasks, large-scale
+pre-trained language models do not perform as well in few-shot settings where
+only a handful of training examples are available. To address this shortcoming,
+we propose STraTA, which stands for Self-Training with Task Augmentation, an
+approach that builds on two key ideas for effective leverage of unlabeled data.
+First, STraTA uses task augmentation, a novel technique that synthesizes a
+large amount of data for auxiliary-task fine-tuning from target-task unlabeled
+texts. Second, STraTA performs self-training by further fine-tuning the strong
+base model created by task augmentation on a broad distribution of
+pseudo-labeled data. Our experiments demonstrate that STraTA can substantially
+improve sample efficiency across 12 few-shot benchmarks. Remarkably, on the
+SST-2 sentiment dataset, STraTA, with only 8 training examples per class,
+achieves comparable results to standard fine-tuning with 67K training examples.
+Our analyses reveal that task augmentation and self-training are both
+complementary and independently effective.
+"
+Few-Shot Emotion Recognition in Conversation with Sequential  Prototypical Networks,Gaël Guibon,http://arxiv.org/pdf/2109.09366v1.pdf,2021-09-20,"['cs.cl', 'cs.lg']",2109.09366v1.pdf,"  Several recent studies on dyadic human-human interactions have been done on
+conversations without specific business objectives. However, many companies
+might benefit from studies dedicated to more precise environments such as after
+sales services or customer satisfaction surveys. In this work, we place
+ourselves in the scope of a live chat customer service in which we want to
+detect emotions and their evolution in the conversation flow. This context
+leads to multiple challenges that range from exploiting restricted, small and
+mostly unlabeled datasets to finding and adapting methods for such context.We
+tackle these challenges by using Few-Shot Learning while making the hypothesis
+it can serve conversational emotion classification for different languages and
+sparse labels. We contribute by proposing a variation of Prototypical Networks
+for sequence labeling in conversation that we name ProtoSeq. We test this
+method on two datasets with different languages: daily conversations in English
+and customer service chat conversations in French. When applied to emotion
+classification in conversations, our method proved to be competitive even when
+compared to other ones.
+"
+UserIdentifier: Implicit User Representations for Simple and Effective  Personalized Sentiment Analysis,Fatemehsadat Mireshghallah,http://arxiv.org/pdf/2110.00135v2.pdf,2021-10-01,"['cs.lg', 'cs.ai', 'cs.cl']",2110.00135v2.pdf,"  Global models are trained to be as generalizable as possible, with user
+invariance considered desirable since the models are shared across multitudes
+of users. As such, these models are often unable to produce personalized
+responses for individual users, based on their data. Contrary to widely-used
+personalization techniques based on few-shot learning, we propose
+UserIdentifier, a novel scheme for training a single shared model for all
+users. Our approach produces personalized responses by adding fixed,
+non-trainable user identifiers to the input data. We empirically demonstrate
+that this proposed method outperforms the prefix-tuning based state-of-the-art
+approach by up to 13%, on a suite of sentiment analysis datasets. We also show
+that, unlike prior work, this method needs neither any additional model
+parameters nor any extra rounds of few-shot fine-tuning.
+"
+Instance-aware Prompt Learning for Language Understanding and Generation,Feihu Jin,http://arxiv.org/pdf/2201.07126v1.pdf,2022-01-18,['cs.cl'],2201.07126v1.pdf,"  Recently, prompt learning has become a new paradigm to utilize pre-trained
+language models (PLMs) and achieves promising results in downstream tasks with
+a negligible increase of parameters. The current usage of discrete and
+continuous prompts assumes that the prompt is fixed for a specific task and all
+samples in the task share the same prompt. However, a task may contain quite
+diverse samples in which some are easy and others are difficult, and diverse
+prompts are desirable. In this paper, we propose an instance-aware prompt
+learning method that learns a different prompt for each instance. Specifically,
+we suppose that each learnable prompt token has a different contribution to
+different instances, and we learn the contribution by calculating the relevance
+score between an instance and each prompt token. The contribution weighted
+prompt would be instance aware. We apply our method to both unidirectional and
+bidirectional PLMs on both language understanding and generation tasks.
+Extensive experiments demonstrate that our method obtains considerable
+improvements compared to strong baselines. Especially, our method achieves the
+state-of-the-art on the SuperGLUE few-shot learning benchmark.
+"
+Generating Training Data with Language Models: Towards Zero-Shot  Language Understanding,Yu Meng,http://arxiv.org/pdf/2202.04538v2.pdf,2022-02-09,"['cs.cl', 'cs.lg']",2202.04538v2.pdf,"  Pretrained language models (PLMs) have demonstrated remarkable performance in
+various natural language processing tasks: Unidirectional PLMs (e.g., GPT) are
+well known for their superior text generation capabilities; bidirectional PLMs
+(e.g., BERT) have been the prominent choice for natural language understanding
+(NLU) tasks. While both types of models have achieved promising few-shot
+learning performance, their potential for zero-shot learning has been
+underexplored. In this paper, we present a simple approach that uses both types
+of PLMs for fully zero-shot learning of NLU tasks without requiring any
+task-specific data: A unidirectional PLM generates class-conditioned texts
+guided by prompts, which are used as the training data for fine-tuning a
+bidirectional PLM. With quality training data selected based on the generation
+probability and regularization techniques (label smoothing and temporal
+ensembling) applied to the fine-tuning stage for better generalization and
+stability, our approach demonstrates strong performance across seven
+classification tasks of the GLUE benchmark (e.g., 72.3/73.8 on MNLI-m/mm and
+92.8 on SST-2), significantly outperforming zero-shot prompting methods and
+achieving even comparable results to strong few-shot approaches using 32
+training samples per class.
+"
+Variational Autoencoder with Disentanglement Priors for Low-Resource  Task-Specific Natural Language Generation,Zhuang Li,http://arxiv.org/pdf/2202.13363v3.pdf,2022-02-27,['cs.cl'],2202.13363v3.pdf,"  In this paper, we propose a variational autoencoder with disentanglement
+priors, VAE-DPRIOR, for task-specific natural language generation with none or
+a handful of task-specific labeled examples. In order to tackle compositional
+generalization across tasks, our model performs disentangled representation
+learning by introducing a conditional prior for the latent content space and
+another conditional prior for the latent label space. Both types of priors
+satisfy a novel property called $\epsilon$-disentangled. We show both
+empirically and theoretically that the novel priors can disentangle
+representations even without specific regularizations as in the prior work. The
+content prior enables directly sampling diverse content representations from
+the content space learned from the seen tasks, and fuse them with the
+representations of novel tasks for generating semantically diverse texts in the
+low-resource settings. Our extensive experiments demonstrate the superior
+performance of our model over competitive baselines in terms of i) data
+augmentation in continuous zero/few-shot learning, and ii) text style transfer
+in the few-shot setting.
+"
+ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer  for Event-Centric Generation and Classification,Yucheng Zhou,http://arxiv.org/pdf/2203.02225v2.pdf,2022-03-04,['cs.cl'],2203.02225v2.pdf,"  Generating new events given context with correlated ones plays a crucial role
+in many event-centric reasoning tasks. Existing works either limit their scope
+to specific scenarios or overlook event-level correlations. In this paper, we
+propose to pre-train a general Correlation-aware context-to-Event Transformer
+(ClarET) for event-centric reasoning. To achieve this, we propose three novel
+event-centric objectives, i.e., whole event recovering, contrastive
+event-correlation encoding and prompt-based event locating, which highlight
+event-level correlations with effective training. The proposed ClarET is
+applicable to a wide range of event-centric reasoning scenarios, considering
+its versatility of (i) event-correlation types (e.g., causal, temporal,
+contrast), (ii) application formulations (i.e., generation and classification),
+and (iii) reasoning types (e.g., abductive, counterfactual and ending
+reasoning). Empirical fine-tuning results, as well as zero- and few-shot
+learning, on 9 benchmarks (5 generation and 4 classification tasks covering 4
+reasoning types with diverse event correlations), verify its effectiveness and
+generalization ability.
+"
+Pre-trained Token-replaced Detection Model as Few-shot Learner,Zicheng Li,http://arxiv.org/pdf/2203.03235v2.pdf,2022-03-07,"['cs.cl', 'cs.ai']",2203.03235v2.pdf,"  Pre-trained masked language models have demonstrated remarkable ability as
+few-shot learners. In this paper, as an alternative, we propose a novel
+approach to few-shot learning with pre-trained token-replaced detection models
+like ELECTRA. In this approach, we reformulate a classification or a regression
+task as a token-replaced detection problem. Specifically, we first define a
+template and label description words for each task and put them into the input
+to form a natural language prompt. Then, we employ the pre-trained
+token-replaced detection model to predict which label description word is the
+most original (i.e., least replaced) among all label description words in the
+prompt. A systematic evaluation on 16 datasets demonstrates that our approach
+outperforms few-shot learners with pre-trained masked language models in both
+one-sentence and two-sentence learning tasks.
+"
+InstructionNER: A Multi-Task Instruction-Based Generative Framework for  Few-shot NER,Liwen Wang,http://arxiv.org/pdf/2203.03903v1.pdf,2022-03-08,['cs.cl'],2203.03903v1.pdf,"  Recently, prompt-based methods have achieved significant performance in
+few-shot learning scenarios by bridging the gap between language model
+pre-training and fine-tuning for downstream tasks. However, existing prompt
+templates are mostly designed for sentence-level tasks and are inappropriate
+for sequence labeling objectives. To address the above issue, we propose a
+multi-task instruction-based generative framework, named InstructionNER, for
+low-resource named entity recognition. Specifically, we reformulate the NER
+task as a generation problem, which enriches source sentences with
+task-specific instructions and answer options, then inferences the entities and
+types in natural language. We further propose two auxiliary tasks, including
+entity extraction and entity typing, which enable the model to capture more
+boundary information of entities and deepen the understanding of entity type
+semantics, respectively. Experimental results show that our method consistently
+outperforms other baselines on five datasets in few-shot settings.
+"
+Prototypical Verbalizer for Prompt-based Few-shot Tuning,Ganqu Cui,http://arxiv.org/pdf/2203.09770v1.pdf,2022-03-18,"['cs.cl', 'cs.lg']",2203.09770v1.pdf,"  Prompt-based tuning for pre-trained language models (PLMs) has shown its
+effectiveness in few-shot learning. Typically, prompt-based tuning wraps the
+input text into a cloze question. To make predictions, the model maps the
+output words to labels via a verbalizer, which is either manually designed or
+automatically built. However, manual verbalizers heavily depend on
+domain-specific prior knowledge and human efforts, while finding appropriate
+label words automatically still remains challenging.In this work, we propose
+the prototypical verbalizer (ProtoVerb) which is built directly from training
+data. Specifically, ProtoVerb learns prototype vectors as verbalizers by
+contrastive learning. In this way, the prototypes summarize training instances
+and are able to enclose rich class-level semantics. We conduct experiments on
+both topic classification and entity typing tasks, and the results demonstrate
+that ProtoVerb significantly outperforms current automatic verbalizers,
+especially when training data is extremely scarce. More surprisingly, ProtoVerb
+consistently boosts prompt-based tuning even on untuned PLMs, indicating an
+elegant non-tuning way to utilize PLMs. Our codes are avaliable at
+https://github.com/thunlp/OpenPrompt.
+"
+Few-Shot Learning with Siamese Networks and Label Tuning,Thomas Müller,http://arxiv.org/pdf/2203.14655v2.pdf,2022-03-28,"['cs.cl', 'cs.lg']",2203.14655v2.pdf,"  We study the problem of building text classifiers with little or no training
+data, commonly known as zero and few-shot text classification. In recent years,
+an approach based on neural textual entailment models has been found to give
+strong results on a diverse range of tasks. In this work, we show that with
+proper pre-training, Siamese Networks that embed texts and labels offer a
+competitive alternative. These models allow for a large reduction in inference
+cost: constant in the number of labels rather than linear. Furthermore, we
+introduce label tuning, a simple and computationally efficient approach that
+allows to adapt the models in a few-shot setup by only changing the label
+embeddings. While giving lower performance than model fine-tuning, this
+approach has the architectural advantage that a single encoder can be shared by
+many different tasks.
+"
+Inverse is Better! Fast and Accurate Prompt for Few-shot Slot Tagging,Yutai Hou,http://arxiv.org/pdf/2204.00885v1.pdf,2022-04-02,"['cs.cl', 'cs.ai']",2204.00885v1.pdf,"  Prompting methods recently achieve impressive success in few-shot learning.
+These methods modify input samples with prompt sentence pieces, and decode
+label tokens to map samples to corresponding labels. However, such a paradigm
+is very inefficient for the task of slot tagging. Since slot tagging samples
+are multiple consecutive words in a sentence, the prompting methods have to
+enumerate all n-grams token spans to find all the possible slots, which greatly
+slows down the prediction. To tackle this, we introduce an inverse paradigm for
+prompting. Different from the classic prompts mapping tokens to labels, we
+reversely predict slot values given slot types. Such inverse prompting only
+requires a one-turn prediction for each slot type and greatly speeds up the
+prediction. Besides, we propose a novel Iterative Prediction Strategy, from
+which the model learns to refine predictions by considering the relations
+between different slot types. We find, somewhat surprisingly, the proposed
+method not only predicts faster but also significantly improves the effect
+(improve over 6.1 F1-scores on 10-shot setting) and achieves new
+state-of-the-art performance.
+"
+Leveraging pre-trained language models for conversational information  seeking from text,Patrizio Bellan,http://arxiv.org/pdf/2204.03542v1.pdf,2022-03-31,"['cs.cl', 'cs.ai']",2204.03542v1.pdf,"  Recent advances in Natural Language Processing, and in particular on the
+construction of very large pre-trained language representation models, is
+opening up new perspectives on the construction of conversational information
+seeking (CIS) systems. In this paper we investigate the usage of in-context
+learning and pre-trained language representation models to address the problem
+of information extraction from process description documents, in an incremental
+question and answering oriented fashion. In particular we investigate the usage
+of the native GPT-3 (Generative Pre-trained Transformer 3) model, together with
+two in-context learning customizations that inject conceptual definitions and a
+limited number of samples in a few shot-learning fashion. The results highlight
+the potential of the approach and the usefulness of the in-context learning
+customizations, which can substantially contribute to address the ""training
+data challenge"" of deep learning based NLP techniques the BPM field. It also
+highlight the challenge posed by control flow relations for which further
+training needs to be devised.
+"
+MGIMN: Multi-Grained Interactive Matching Network for Few-shot Text  Classification,Jianhai Zhang,http://arxiv.org/pdf/2204.04952v3.pdf,2022-04-11,['cs.cl'],2204.04952v3.pdf,"  Text classification struggles to generalize to unseen classes with very few
+labeled text instances per class. In such a few-shot learning (FSL) setting,
+metric-based meta-learning approaches have shown promising results. Previous
+studies mainly aim to derive a prototype representation for each class.
+However, they neglect that it is challenging-yet-unnecessary to construct a
+compact representation which expresses the entire meaning for each class. They
+also ignore the importance to capture the inter-dependency between query and
+the support set for few-shot text classification. To deal with these issues, we
+propose a meta-learning based method MGIMN which performs instance-wise
+comparison followed by aggregation to generate class-wise matching vectors
+instead of prototype learning. The key of instance-wise comparison is the
+interactive matching within the class-specific context and episode-specific
+context. Extensive experiments demonstrate that the proposed method
+significantly outperforms the existing state-of-the-art approaches, under both
+the standard FSL and generalized FSL settings.
+"
+Zero and Few-shot Learning for Author Profiling,Mara Chinea-Rios,http://arxiv.org/pdf/2204.10543v2.pdf,2022-04-22,['cs.cl'],2204.10543v2.pdf,"  Author profiling classifies author characteristics by analyzing how language
+is shared among people. In this work, we study that task from a low-resource
+viewpoint: using little or no training data. We explore different zero and
+few-shot models based on entailment and evaluate our systems on several
+profiling tasks in Spanish and English. In addition, we study the effect of
+both the entailment hypothesis and the size of the few-shot training sample. We
+find that entailment-based models out-perform supervised text classifiers based
+on roberta-XLM and that we can reach 80% of the accuracy of previous approaches
+using less than 50\% of the training data on average.
+"
+Super-Prompting: Utilizing Model-Independent Contextual Data to Reduce  Data Annotation Required in Visual Commonsense Tasks,Navid Rezaei,http://arxiv.org/pdf/2204.11922v1.pdf,2022-04-25,"['cs.cl', 'cs.ai']",2204.11922v1.pdf,"  Pre-trained language models have shown excellent results in few-shot learning
+scenarios using in-context learning. Although it is impressive, the size of
+language models can be prohibitive to make them usable in on-device
+applications, such as sensors or smartphones. With smaller language models,
+task-specific data annotation is needed to fine-tune the language model for a
+specific purpose. However, data annotation can have a substantial financial and
+time burden for small research groups, startups, and even companies. In this
+paper, we analyze different prompt-based fine-tuning techniques to improve
+results on both language and multimodal causal transformer models. To evaluate
+our results, we use a dataset focusing on visual commonsense reasoning in time.
+Our results show that by simple model-agnostic prompt-based fine-tuning,
+comparable results can be reached by only using 35%-40% of the fine-tuning
+training dataset. The proposed approaches result in significant time and
+financial savings. As the proposed methods make minimal architectural
+assumptions, other researchers can use the results in their transformer models
+with minimal adaptations. We plan to release the source code freely to make it
+easier for the community to use and contribute to our work.
+"
+Building a Role Specified Open-Domain Dialogue System Leveraging  Large-Scale Language Models,Sanghwan Bae,http://arxiv.org/pdf/2205.00176v1.pdf,2022-04-30,['cs.cl'],2205.00176v1.pdf,"  Recent open-domain dialogue models have brought numerous breakthroughs.
+However, building a chat system is not scalable since it often requires a
+considerable volume of human-human dialogue data, especially when enforcing
+features such as persona, style, or safety. In this work, we study the
+challenge of imposing roles on open-domain dialogue systems, with the goal of
+making the systems maintain consistent roles while conversing naturally with
+humans. To accomplish this, the system must satisfy a role specification that
+includes certain conditions on the stated features as well as a system policy
+on whether or not certain types of utterances are allowed. For this, we propose
+an efficient data collection framework leveraging in-context few-shot learning
+of large-scale language models for building role-satisfying dialogue dataset
+from scratch. We then compare various architectures for open-domain dialogue
+systems in terms of meeting role specifications while maintaining
+conversational abilities. Automatic and human evaluations show that our models
+return few out-of-bounds utterances, keeping competitive performance on general
+metrics. We release a Korean dialogue dataset we built for further research.
+"
+EasyNLP: A Comprehensive and Easy-to-use Toolkit for Natural Language  Processing,Chengyu Wang,http://arxiv.org/pdf/2205.00258v2.pdf,2022-04-30,['cs.cl'],2205.00258v2.pdf,"  The success of Pre-Trained Models (PTMs) has reshaped the development of
+Natural Language Processing (NLP). Yet, it is not easy to obtain
+high-performing models and deploy them online for industrial practitioners. To
+bridge this gap, EasyNLP is designed to make it easy to build NLP applications,
+which supports a comprehensive suite of NLP algorithms. It further features
+knowledge-enhanced pre-training, knowledge distillation and few-shot learning
+functionalities for large-scale PTMs, and provides a unified framework of model
+training, inference and deployment for real-world applications. Currently,
+EasyNLP has powered over ten business units within Alibaba Group and is
+seamlessly integrated to the Platform of AI (PAI) products on Alibaba Cloud.
+The source code of our EasyNLP toolkit is released at GitHub
+(https://github.com/alibaba/EasyNLP).
+"
+POLITICS: Pretraining with Same-story Article Comparison for Ideology  Prediction and Stance Detection,Yujian Liu,http://arxiv.org/pdf/2205.00619v1.pdf,2022-05-02,['cs.cl'],2205.00619v1.pdf,"  Ideology is at the core of political science research. Yet, there still does
+not exist general-purpose tools to characterize and predict ideology across
+different genres of text. To this end, we study Pretrained Language Models
+using novel ideology-driven pretraining objectives that rely on the comparison
+of articles on the same story written by media of different ideologies. We
+further collect a large-scale dataset, consisting of more than 3.6M political
+news articles, for pretraining. Our model POLITICS outperforms strong baselines
+and the previous state-of-the-art models on ideology prediction and stance
+detection tasks. Further analyses show that POLITICS is especially good at
+understanding long or formally written texts, and is also robust in few-shot
+learning scenarios.
+"
+KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive  Question Answering,Jianing Wang,http://arxiv.org/pdf/2205.03071v1.pdf,2022-05-06,"['cs.cl', 'cs.ai']",2205.03071v1.pdf,"  Extractive Question Answering (EQA) is one of the most important tasks in
+Machine Reading Comprehension (MRC), which can be solved by fine-tuning the
+span selecting heads of Pre-trained Language Models (PLMs). However, most
+existing approaches for MRC may perform poorly in the few-shot learning
+scenario. To solve this issue, we propose a novel framework named Knowledge
+Enhanced Contrastive Prompt-tuning (KECP). Instead of adding pointer heads to
+PLMs, we introduce a seminal paradigm for EQA that transform the task into a
+non-autoregressive Masked Language Modeling (MLM) generation problem.
+Simultaneously, rich semantics from the external knowledge base (KB) and the
+passage context are support for enhancing the representations of the query. In
+addition, to boost the performance of PLMs, we jointly train the model by the
+MLM and contrastive learning objectives. Experiments on multiple benchmarks
+demonstrate that our method consistently outperforms state-of-the-art
+approaches in few-shot settings by a large margin.
+"
+ProQA: Structural Prompt-based Pre-training for Unified Question  Answering,Wanjun Zhong,http://arxiv.org/pdf/2205.04040v2.pdf,2022-05-09,['cs.cl'],2205.04040v2.pdf,"  Question Answering (QA) is a longstanding challenge in natural language
+processing. Existing QA works mostly focus on specific question types,
+knowledge domains, or reasoning skills. The specialty in QA research hinders
+systems from modeling commonalities between tasks and generalization for wider
+applications. To address this issue, we present ProQA, a unified QA paradigm
+that solves various tasks through a single model. ProQA takes a unified
+structural prompt as the bridge and improves the QA-centric ability by
+structural prompt-based pre-training. Through a structurally designed
+prompt-based input schema, ProQA concurrently models the knowledge
+generalization for all QA tasks while keeping the knowledge customization for
+every specific QA task. Furthermore, ProQA is pre-trained with structural
+prompt-formatted large-scale synthesized corpus, which empowers the model with
+the commonly-required QA ability. Experimental results on 11 QA benchmarks
+demonstrate that ProQA consistently boosts performance on both full data
+fine-tuning, few-shot learning, and zero-shot testing scenarios. Furthermore,
+ProQA exhibits strong ability in both continual learning and transfer learning
+by taking the advantages of the structural prompt.
+"
+ALLSH: Active Learning Guided by Local Sensitivity and Hardness,Shujian Zhang,http://arxiv.org/pdf/2205.04980v2.pdf,2022-05-10,"['cs.cl', 'cs.ai', 'cs.lg']",2205.04980v2.pdf,"  Active learning, which effectively collects informative unlabeled data for
+annotation, reduces the demand for labeled data. In this work, we propose to
+retrieve unlabeled samples with a local sensitivity and hardness-aware
+acquisition function. The proposed method generates data copies through local
+perturbations and selects data points whose predictive likelihoods diverge the
+most from their copies. We further empower our acquisition function by
+injecting the select-worst case perturbation. Our method achieves consistent
+gains over the commonly used active learning strategies in various
+classification tasks. Furthermore, we observe consistent improvements over the
+baselines on the study of prompt selection in prompt-based few-shot learning.
+These experiments demonstrate that our acquisition guided by local sensitivity
+and hardness can be effective and beneficial for many NLP tasks.
+"
+Prototypical Calibration for Few-shot Learning of Language Models,Zhixiong Han,http://arxiv.org/pdf/2205.10183v2.pdf,2022-05-20,['cs.cl'],2205.10183v2.pdf,"  In-context learning of GPT-like models has been recognized as fragile across
+different hand-crafted templates, and demonstration permutations. In this work,
+we propose prototypical calibration to adaptively learn a more robust decision
+boundary for zero- and few-shot classification, instead of greedy decoding.
+Concretely, our method first adopts Gaussian mixture distribution to estimate
+the prototypical clusters for all categories. Then we assign each cluster to
+the corresponding label by solving a weighted bipartite matching problem. Given
+an example, its prediction is calibrated by the likelihood of prototypical
+clusters. Experimental results show that prototypical calibration yields a
+substantial improvement on a diverse set of tasks. Extensive analysis across
+different scales also indicates that our method calibrates the decision
+boundary as expected, greatly improving the robustness of GPT to templates,
+permutations, and class imbalance.
+"
+BBTv2: Towards a Gradient-Free Future with Large Language Models,Tianxiang Sun,http://arxiv.org/pdf/2205.11200v2.pdf,2022-05-23,"['cs.cl', 'cs.ai']",2205.11200v2.pdf,"  Most downstream adaptation methods tune all or part of the parameters of
+pre-trained models (PTMs) through gradient descent, where the tuning cost
+increases linearly with the growth of the model size. By contrast,
+gradient-free methods only require the forward computation of the PTM to tune
+the prompt, retaining the benefits of efficient tuning and deployment. Though,
+past work on gradient-free tuning often introduces gradient descent to seek a
+good initialization of prompt and lacks versatility across tasks and PTMs. In
+this paper, we present BBTv2, an improved version of Black-Box Tuning, to drive
+PTMs for few-shot learning. We prepend continuous prompts to every layer of the
+PTM and propose a divide-and-conquer gradient-free algorithm to optimize the
+prompts at different layers alternately. Extensive experiments across various
+tasks and PTMs show that BBTv2 can achieve comparable performance to full model
+tuning and state-of-the-art parameter-efficient methods (e.g., Adapter, LoRA,
+BitFit, etc.) under few-shot settings while maintaining much fewer tunable
+parameters.
+"
+Zero-Shot and Few-Shot Learning for Lung Cancer Multi-Label  Classification using Vision Transformer,Fu-Ming Guo,http://arxiv.org/pdf/2205.15290v2.pdf,2022-05-30,"['cs.cv', 'cs.ai', 'cs.lg']",2205.15290v2.pdf,"  Lung cancer is the leading cause of cancer-related death worldwide. Lung
+adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are the most
+common histologic subtypes of non-small-cell lung cancer (NSCLC). Histology is
+an essential tool for lung cancer diagnosis. Pathologists make classifications
+according to the dominant subtypes. Although morphology remains the standard
+for diagnosis, significant tool needs to be developed to elucidate the
+diagnosis. In our study, we utilize the pre-trained Vision Transformer (ViT)
+model to classify multiple label lung cancer on histologic slices (from dataset
+LC25000), in both Zero-Shot and Few-Shot settings. Then we compare the
+performance of Zero-Shot and Few-Shot ViT on accuracy, precision, recall,
+sensitivity and specificity. Our study show that the pre-trained ViT model has
+a good performance in Zero-Shot setting, a competitive accuracy ($99.87\%$) in
+Few-Shot setting ({epoch = 1}) and an optimal result ($100.00\%$ on both
+validation set and test set) in Few-Shot seeting ({epoch = 5}).
+"
+Neural Prompt Search,Yuanhan Zhang,http://arxiv.org/pdf/2206.04673v2.pdf,2022-06-09,"['cs.cv', 'cs.ai', 'cs.lg']",2206.04673v2.pdf,"  The size of vision models has grown exponentially over the last few years,
+especially after the emergence of Vision Transformer. This has motivated the
+development of parameter-efficient tuning methods, such as learning adapter
+layers or visual prompt tokens, which allow a tiny portion of model parameters
+to be trained whereas the vast majority obtained from pre-training are frozen.
+However, designing a proper tuning method is non-trivial: one might need to try
+out a lengthy list of design choices, not to mention that each downstream
+dataset often requires custom designs. In this paper, we view the existing
+parameter-efficient tuning methods as ""prompt modules"" and propose Neural
+prOmpt seArcH (NOAH), a novel approach that learns, for large vision models,
+the optimal design of prompt modules through a neural architecture search
+algorithm, specifically for each downstream dataset. By conducting extensive
+experiments on over 20 vision datasets, we demonstrate that NOAH (i) is
+superior to individual prompt modules, (ii) has a good few-shot learning
+ability, and (iii) is domain-generalizable. The code and models are available
+at https://github.com/Davidzhangyuanhan/NOAH.
+"
+Low Resource Pipeline for Spoken Language Understanding via Weak  Supervision,Ayush Kumar,http://arxiv.org/pdf/2206.10559v1.pdf,2022-06-21,['cs.cl'],2206.10559v1.pdf,"  In Weak Supervised Learning (WSL), a model is trained over noisy labels
+obtained from semantic rules and task-specific pre-trained models. Rules offer
+limited generalization over tasks and require significant manual efforts while
+pre-trained models are available only for limited tasks. In this work, we
+propose to utilize prompt-based methods as weak sources to obtain the noisy
+labels on unannotated data. We show that task-agnostic prompts are
+generalizable and can be used to obtain noisy labels for different Spoken
+Language Understanding (SLU) tasks such as sentiment classification, disfluency
+detection and emotion classification. These prompts could additionally be
+updated to add task-specific contexts, thus providing flexibility to design
+task-specific prompts. We demonstrate that prompt-based methods generate
+reliable labels for the above SLU tasks and thus can be used as a universal
+weak source to train a weak-supervised model (WSM) in absence of labeled data.
+Our proposed WSL pipeline trained over prompt-based weak source outperforms
+other competitive low-resource benchmarks on zero and few-shot learning by more
+than 4% on Macro-F1 on all of the three benchmark SLU datasets. The proposed
+method also outperforms a conventional rule based WSL pipeline by more than 5%
+on Macro-F1.
+"
+Prompting Decision Transformer for Few-Shot Policy Generalization,Mengdi Xu,http://arxiv.org/pdf/2206.13499v1.pdf,2022-06-27,"['cs.lg', 'cs.ai', 'cs.cv', 'cs.ro']",2206.13499v1.pdf,"  Humans can leverage prior experience and learn novel tasks from a handful of
+demonstrations. In contrast to offline meta-reinforcement learning, which aims
+to achieve quick adaptation through better algorithm design, we investigate the
+effect of architecture inductive bias on the few-shot learning capability. We
+propose a Prompt-based Decision Transformer (Prompt-DT), which leverages the
+sequential modeling ability of the Transformer architecture and the prompt
+framework to achieve few-shot adaptation in offline RL. We design the
+trajectory prompt, which contains segments of the few-shot demonstrations, and
+encodes task-specific information to guide policy generation. Our experiments
+in five MuJoCo control benchmarks show that Prompt-DT is a strong few-shot
+learner without any extra finetuning on unseen target tasks. Prompt-DT
+outperforms its variants and strong meta offline RL baselines by a large margin
+with a trajectory prompt containing only a few timesteps. Prompt-DT is also
+robust to prompt length changes and can generalize to out-of-distribution (OOD)
+environments.
+"
+Few-shot training LLMs for project-specific code-summarization,Toufique Ahmed,http://arxiv.org/pdf/2207.04237v2.pdf,2022-07-09,"['cs.se', 'cs.lg']",2207.04237v2.pdf,"  Very large language models (LLMs), such as GPT-3 and Codex have achieved
+state-of-the-art performance on several natural-language tasks, and show great
+promise also for code. A particularly exciting aspect of LLMs is their knack
+for few-shot and zero-shot learning: they can learn to perform a task with very
+few examples. Few-shotting has particular synergies in software engineering,
+where there are a lot of phenomena (identifier names, APIs, terminology, coding
+patterns) that are known to be highly project-specific. However,
+project-specific data can be quite limited, especially early in the history of
+a project; thus the few-shot learning capacity of LLMs might be very relevant.
+In this paper, we investigate the use few-shot training with the very large GPT
+(Generative Pre-trained Transformer) Codex model, and find evidence suggesting
+that one can significantly surpass state-of-the-art models for
+code-summarization, leveraging project-specific training.
+"
+Convolutional Bypasses Are Better Vision Transformer Adapters,Shibo Jie,http://arxiv.org/pdf/2207.07039v3.pdf,2022-07-14,['cs.cv'],2207.07039v3.pdf,"  The pretrain-then-finetune paradigm has been widely adopted in computer
+vision. But as the size of Vision Transformer (ViT) grows exponentially, the
+full finetuning becomes prohibitive in view of the heavier storage overhead.
+Motivated by parameter-efficient transfer learning (PETL) on language
+transformers, recent studies attempt to insert lightweight adaptation modules
+(e.g., adapter layers or prompt tokens) to pretrained ViT and only finetune
+these modules while the pretrained weights are frozen. However, these modules
+were originally proposed to finetune language models and did not take into
+account the prior knowledge specifically for visual tasks. In this paper, we
+propose to construct Convolutional Bypasses (Convpass) in ViT as adaptation
+modules, introducing only a small amount (less than 0.5% of model parameters)
+of trainable parameters to adapt the large ViT. Different from other PETL
+methods, Convpass benefits from the hard-coded inductive bias of convolutional
+layers and thus is more suitable for visual tasks, especially in the low-data
+regime. Experimental results on VTAB-1K benchmark and few-shot learning
+datasets show that Convpass outperforms current language-oriented adaptation
+modules, demonstrating the necessity to tailor vision-oriented adaptation
+modules for adapting vision models.
+"
+STT: Soft Template Tuning for Few-Shot Adaptation,Ping Yu,http://arxiv.org/pdf/2207.08408v1.pdf,2022-07-18,"['cs.cl', 'cs.ai']",2207.08408v1.pdf,"  Prompt tuning has been an extremely effective tool to adapt a pre-trained
+model to downstream tasks. However, standard prompt-based methods mainly
+consider the case of sufficient data of downstream tasks. It is still unclear
+whether the advantage can be transferred to the few-shot regime, where only
+limited data are available for each downstream task. Although some works have
+demonstrated the potential of prompt-tuning under the few-shot setting, the
+main stream methods via searching discrete prompts or tuning soft prompts with
+limited data are still very challenging. Through extensive empirical studies,
+we find that there is still a gap between prompt tuning and fully fine-tuning
+for few-shot learning. To bridge the gap, we propose a new prompt-tuning
+framework, called Soft Template Tuning (STT). STT combines manual and auto
+prompts, and treats downstream classification tasks as a masked language
+modeling task. Comprehensive evaluation on different settings suggests STT can
+close the gap between fine-tuning and prompt-based methods without introducing
+additional parameters. Significantly, it can even outperform the time- and
+resource-consuming fine-tuning method on sentiment classification tasks.
+"
+Self-Supervision Can Be a Good Few-Shot Learner,Yuning Lu,http://arxiv.org/pdf/2207.09176v1.pdf,2022-07-19,['cs.cv'],2207.09176v1.pdf,"  Existing few-shot learning (FSL) methods rely on training with a large
+labeled dataset, which prevents them from leveraging abundant unlabeled data.
+From an information-theoretic perspective, we propose an effective unsupervised
+FSL method, learning representations with self-supervision. Following the
+InfoMax principle, our method learns comprehensive representations by capturing
+the intrinsic structure of the data. Specifically, we maximize the mutual
+information (MI) of instances and their representations with a low-bias MI
+estimator to perform self-supervised pre-training. Rather than supervised
+pre-training focusing on the discriminable features of the seen classes, our
+self-supervised model has less bias toward the seen classes, resulting in
+better generalization for unseen classes. We explain that supervised
+pre-training and self-supervised pre-training are actually maximizing different
+MI objectives. Extensive experiments are further conducted to analyze their FSL
+performance with various training settings. Surprisingly, the results show that
+self-supervised pre-training can outperform supervised pre-training under the
+appropriate conditions. Compared with state-of-the-art FSL methods, our
+approach achieves comparable performance on widely used FSL benchmarks without
+any labels of the base classes.
+"
+Language Model Cascades,David Dohan,http://arxiv.org/pdf/2207.10342v2.pdf,2022-07-21,"['cs.cl', 'cs.ai']",2207.10342v2.pdf,"  Prompted models have demonstrated impressive few-shot learning abilities.
+Repeated interactions at test-time with a single model, or the composition of
+multiple models together, further expands capabilities. These compositions are
+probabilistic models, and may be expressed in the language of graphical models
+with random variables whose values are complex data types such as strings.
+Cases with control flow and dynamic structure require techniques from
+probabilistic programming, which allow implementing disparate model structures
+and inference strategies in a unified language. We formalize several existing
+techniques from this perspective, including scratchpads / chain of thought,
+verifiers, STaR, selection-inference, and tool use. We refer to the resulting
+programs as language model cascades.
+"
+Few-shot Adaptation Works with UnpredicTable Data,Jun Shern Chan,http://arxiv.org/pdf/2208.01009v2.pdf,2022-08-01,"['cs.cl', 'cs.ai', 'cs.lg']",2208.01009v2.pdf,"  Prior work on language models (LMs) shows that training on a large number of
+diverse tasks improves few-shot learning (FSL) performance on new tasks. We
+take this to the extreme, automatically extracting 413,299 tasks from internet
+tables - orders of magnitude more than the next-largest public datasets.
+Finetuning on the resulting dataset leads to improved FSL performance on
+Natural Language Processing (NLP) tasks, but not proportionally to dataset
+scale. In fact, we find that narrow subsets of our dataset sometimes outperform
+more diverse datasets. For example, finetuning on software documentation from
+support.google.com raises FSL performance by a mean of +7.5% on 52 downstream
+tasks, which beats training on 40 human-curated NLP datasets (+6.7%).
+Finetuning on various narrow datasets leads to similar broad improvements
+across test tasks, suggesting that the gains are not from domain adaptation but
+adapting to FSL in general. We do not observe clear patterns between the
+datasets that lead to FSL gains, leaving open questions about why certain data
+helps with FSL.
+"
+Robotic Interestingness via Human-Informed Few-Shot Object Detection,Seungchan Kim,http://arxiv.org/pdf/2208.01084v1.pdf,2022-08-01,['cs.ro'],2208.01084v1.pdf,"  Interestingness recognition is crucial for decision making in autonomous
+exploration for mobile robots. Previous methods proposed an unsupervised online
+learning approach that can adapt to environments and detect interesting scenes
+quickly, but lack the ability to adapt to human-informed interesting objects.
+To solve this problem, we introduce a human-interactive framework,
+AirInteraction, that can detect human-informed objects via few-shot online
+learning. To reduce the communication bandwidth, we first apply an online
+unsupervised learning algorithm on the unmanned vehicle for interestingness
+recognition and then only send the potential interesting scenes to a
+base-station for human inspection. The human operator is able to draw and
+provide bounding box annotations for particular interesting objects, which are
+sent back to the robot to detect similar objects via few-shot learning. Only
+using few human-labeled examples, the robot can learn novel interesting object
+categories during the mission and detect interesting scenes that contain the
+objects. We evaluate our method on various interesting scene recognition
+datasets. To the best of our knowledge, it is the first human-informed few-shot
+object detection framework for autonomous exploration.
+"
+Atlas: Few-shot Learning with Retrieval Augmented Language Models,Gautier Izacard,http://arxiv.org/pdf/2208.03299v3.pdf,2022-08-05,['cs.cl'],2208.03299v3.pdf,"  Large language models have shown impressive few-shot results on a wide range
+of tasks. However, when knowledge is key for such results, as is the case for
+tasks such as question answering and fact checking, massive parameter counts to
+store knowledge seem to be needed. Retrieval augmented models are known to
+excel at knowledge intensive tasks without the need for as many parameters, but
+it is unclear whether they work in few-shot settings. In this work we present
+Atlas, a carefully designed and pre-trained retrieval augmented language model
+able to learn knowledge intensive tasks with very few training examples. We
+perform evaluations on a wide range of tasks, including MMLU, KILT and
+NaturalQuestions, and study the impact of the content of the document index,
+showing that it can easily be updated. Notably, Atlas reaches over 42% accuracy
+on Natural Questions using only 64 examples, outperforming a 540B parameters
+model by 3% despite having 50x fewer parameters.
+"
+Limits of an AI program for solving college math problems,Ernest Davis,http://arxiv.org/pdf/2208.06906v1.pdf,2022-08-14,['cs.ai'],2208.06906v1.pdf,"  Drori et al. (2022) report that ""A neural network solves, explains, and
+generates university math problems by program synthesis and few-shot learning
+at human level ... [It] automatically answers 81\% of university-level
+mathematics problems."" The system they describe is indeed impressive; however,
+the above description is very much overstated. The work of solving the problems
+is done, not by a neural network, but by the symbolic algebra package Sympy.
+Problems of various formats are excluded from consideration. The so-called
+""explanations"" are just rewordings of lines of code. Answers are marked as
+correct that are not in the form specified in the problem. Most seriously, it
+seems that in many cases the system uses the correct answer given in the test
+corpus to guide its path to solving the problem.
+"
+Efficient Few-Shot Learning Without Prompts,Lewis Tunstall,http://arxiv.org/pdf/2209.11055v1.pdf,2022-09-22,['cs.cl'],2209.11055v1.pdf,"  Recent few-shot methods, such as parameter-efficient fine-tuning (PEFT) and
+pattern exploiting training (PET), have achieved impressive results in
+label-scarce settings. However, they are difficult to employ since they are
+subject to high variability from manually crafted prompts, and typically
+require billion-parameter language models to achieve high accuracy. To address
+these shortcomings, we propose SetFit (Sentence Transformer Fine-tuning), an
+efficient and prompt-free framework for few-shot fine-tuning of Sentence
+Transformers (ST). SetFit works by first fine-tuning a pretrained ST on a small
+number of text pairs, in a contrastive Siamese manner. The resulting model is
+then used to generate rich text embeddings, which are used to train a
+classification head. This simple framework requires no prompts or verbalizers,
+and achieves high accuracy with orders of magnitude less parameters than
+existing techniques. Our experiments show that SetFit obtains comparable
+results with PEFT and PET techniques, while being an order of magnitude faster
+to train. We also show that SetFit can be applied in multilingual settings by
+simply switching the ST body. Our code is available at
+https://github.com/huggingface/setfit and our datasets at
+https://huggingface.co/setfit .
+"
+CORE: A Retrieve-then-Edit Framework for Counterfactual Data Generation,Tanay Dixit,http://arxiv.org/pdf/2210.04873v2.pdf,2022-10-10,['cs.cl'],2210.04873v2.pdf,"  Counterfactual data augmentation (CDA) -- i.e., adding minimally perturbed
+inputs during training -- helps reduce model reliance on spurious correlations
+and improves generalization to out-of-distribution (OOD) data. Prior work on
+generating counterfactuals only considered restricted classes of perturbations,
+limiting their effectiveness. We present COunterfactual Generation via
+Retrieval and Editing (CORE), a retrieval-augmented generation framework for
+creating diverse counterfactual perturbations for CDA. For each training
+example, CORE first performs a dense retrieval over a task-related unlabeled
+text corpus using a learned bi-encoder and extracts relevant counterfactual
+excerpts. CORE then incorporates these into prompts to a large language model
+with few-shot learning capabilities, for counterfactual editing. Conditioning
+language model edits on naturally occurring data results in diverse
+perturbations. Experiments on natural language inference and sentiment analysis
+benchmarks show that CORE counterfactuals are more effective at improving
+generalization to OOD data compared to other DA approaches. We also show that
+the CORE retrieval framework can be used to encourage diversity in manually
+authored perturbations
+"
+Continual Training of Language Models for Few-Shot Learning,Zixuan Ke,http://arxiv.org/pdf/2210.05549v1.pdf,2022-10-11,"['cs.cl', 'cs.ai', 'cs.lg', 'cs.ne']",2210.05549v1.pdf,"  Recent work on applying large language models (LMs) achieves impressive
+performance in many NLP applications. Adapting or posttraining an LM using an
+unlabeled domain corpus can produce even better performance for end-tasks in
+the domain. This paper proposes the problem of continually extending an LM by
+incrementally post-train the LM with a sequence of unlabeled domain corpora to
+expand its knowledge without forgetting its previous skills. The goal is to
+improve the few-shot end-task learning in these domains. The resulting system
+is called CPT (Continual PostTraining), which to our knowledge, is the first
+continual post-training system. Experimental results verify its effectiveness.
+"
+Knowledge-grounded Dialog State Tracking,Dian Yu,http://arxiv.org/pdf/2210.06656v1.pdf,2022-10-13,['cs.cl'],2210.06656v1.pdf,"  Knowledge (including structured knowledge such as schema and ontology, and
+unstructured knowledge such as web corpus) is a critical part of dialog
+understanding, especially for unseen tasks and domains. Traditionally, such
+domain-specific knowledge is encoded implicitly into model parameters for the
+execution of downstream tasks, which makes training inefficient. In addition,
+such models are not easily transferable to new tasks with different schemas. In
+this work, we propose to perform dialog state tracking grounded on knowledge
+encoded externally. We query relevant knowledge of various forms based on the
+dialog context where such information can ground the prediction of dialog
+states. We demonstrate superior performance of our proposed method over strong
+baselines, especially in the few-shot learning setting.
+"
+Unified Vision and Language Prompt Learning,Yuhang Zang,http://arxiv.org/pdf/2210.07225v1.pdf,2022-10-13,"['cs.cv', 'cs.ai']",2210.07225v1.pdf,"  Prompt tuning, a parameter- and data-efficient transfer learning paradigm
+that tunes only a small number of parameters in a model's input space, has
+become a trend in the vision community since the emergence of large
+vision-language models like CLIP. We present a systematic study on two
+representative prompt tuning methods, namely text prompt tuning and visual
+prompt tuning. A major finding is that none of the unimodal prompt tuning
+methods performs consistently well: text prompt tuning fails on data with high
+intra-class visual variances while visual prompt tuning cannot handle low
+inter-class variances. To combine the best from both worlds, we propose a
+simple approach called Unified Prompt Tuning (UPT), which essentially learns a
+tiny neural network to jointly optimize prompts across different modalities.
+Extensive experiments on over 11 vision datasets show that UPT achieves a
+better trade-off than the unimodal counterparts on few-shot learning
+benchmarks, as well as on domain generalization benchmarks. Code and models
+will be released to facilitate future research.
+"
+"Vision-Language Pre-training: Basics, Recent Advances, and Future Trends",Zhe Gan,http://arxiv.org/pdf/2210.09263v1.pdf,2022-10-17,"['cs.cv', 'cs.cl']",2210.09263v1.pdf,"  This paper surveys vision-language pre-training (VLP) methods for multimodal
+intelligence that have been developed in the last few years. We group these
+approaches into three categories: ($i$) VLP for image-text tasks, such as image
+captioning, image-text retrieval, visual question answering, and visual
+grounding; ($ii$) VLP for core computer vision tasks, such as (open-set) image
+classification, object detection, and segmentation; and ($iii$) VLP for
+video-text tasks, such as video captioning, video-text retrieval, and video
+question answering. For each category, we present a comprehensive review of
+state-of-the-art methods, and discuss the progress that has been made and
+challenges still being faced, using specific systems and models as case
+studies. In addition, for each category, we discuss advanced topics being
+actively explored in the research community, such as big foundation models,
+unified modeling, in-context few-shot learning, knowledge, robustness, and
+computer vision in the wild, to name a few.
+"
+Better Few-Shot Relation Extraction with Label Prompt Dropout,Peiyuan Zhang,http://arxiv.org/pdf/2210.13733v1.pdf,2022-10-25,['cs.cl'],2210.13733v1.pdf,"  Few-shot relation extraction aims to learn to identify the relation between
+two entities based on very limited training examples. Recent efforts found that
+textual labels (i.e., relation names and relation descriptions) could be
+extremely useful for learning class representations, which will benefit the
+few-shot learning task. However, what is the best way to leverage such label
+information in the learning process is an important research question. Existing
+works largely assume such textual labels are always present during both
+learning and prediction. In this work, we argue that such approaches may not
+always lead to optimal results. Instead, we present a novel approach called
+label prompt dropout, which randomly removes label descriptions in the learning
+process. Our experiments show that our approach is able to lead to improved
+class representations, yielding significantly better results on the few-shot
+relation extraction task.
+"
+STPrompt: Semantic-guided and Task-driven prompts for Effective Few-shot  Classification,Jinta Weng,http://arxiv.org/pdf/2210.16489v1.pdf,2022-10-29,"['cs.cl', 'cs.ai']",2210.16489v1.pdf,"  The effectiveness of prompt learning has been demonstrated in different
+pre-trained language models. By formulating suitable template and choosing
+representative label mapping, prompt learning can be used as an efficient
+knowledge probe. However, finding suitable prompt in existing methods requires
+multiple experimental attempts or appropriate vector initialization on
+formulating suitable template and choosing representative label mapping, which
+it is more common in few-shot learning tasks. Motivating by PLM working
+process, we try to construct the prompt from task semantic perspective and thus
+propose the STPrompt -Semantic-guided and Task-driven Prompt model.
+Specifically, two novel prompts generated from the semantic dependency tree
+(Dep-prompt) and task-specific metadata description (Meta-prompt), are firstly
+constructed in a prompt augmented pool, and the proposed model would
+automatically select a suitable semantic prompt to motivating the prompt
+learning process. Our results show that the proposed model achieves the
+state-of-the-art performance in five different datasets of few-shot text
+classification tasks, which prove that more semantic and significant prompts
+could assume as a better knowledge proving tool.
+"
+ConsPrompt: Easily Exploiting Contrastive Samples for Few-shot Prompt  Learning,Jinta Weng,http://arxiv.org/pdf/2211.04118v1.pdf,2022-11-08,"['cs.cl', 'cs.ai']",2211.04118v1.pdf,"  Prompt learning recently become an effective linguistic tool to motivate the
+PLMs' knowledge on few-shot-setting tasks. However, studies have shown the lack
+of robustness still exists in prompt learning, since suitable initialization of
+continuous prompt and expert-first manual prompt are essential in fine-tuning
+process. What is more, human also utilize their comparative ability to motivate
+their existing knowledge for distinguishing different examples. Motivated by
+this, we explore how to use contrastive samples to strengthen prompt learning.
+In detail, we first propose our model ConsPrompt combining with prompt encoding
+network, contrastive sampling module, and contrastive scoring module.
+Subsequently, two sampling strategies, similarity-based and label-based
+strategies, are introduced to realize differential contrastive learning. The
+effectiveness of proposed ConsPrompt is demonstrated in five different few-shot
+learning tasks and shown the similarity-based sampling strategy is more
+effective than label-based in combining contrastive learning. Our results also
+exhibits the state-of-the-art performance and robustness in different few-shot
+settings, which proves that the ConsPrompt could be assumed as a better
+knowledge probe to motivate PLMs.
+"
+Retrieval-Augmented Generative Question Answering for Event Argument  Extraction,Xinya Du,http://arxiv.org/pdf/2211.07067v1.pdf,2022-11-14,['cs.cl'],2211.07067v1.pdf,"  Event argument extraction has long been studied as a sequential prediction
+problem with extractive-based methods, tackling each argument in isolation.
+Although recent work proposes generation-based methods to capture
+cross-argument dependency, they require generating and post-processing a
+complicated target sequence (template). Motivated by these observations and
+recent pretrained language models' capabilities of learning from
+demonstrations. We propose a retrieval-augmented generative QA model (R-GQA)
+for event argument extraction. It retrieves the most similar QA pair and
+augments it as prompt to the current example's context, then decodes the
+arguments as answers. Our approach outperforms substantially prior methods
+across various settings (i.e. fully supervised, domain transfer, and fewshot
+learning). Finally, we propose a clustering-based sampling strategy (JointEnc)
+and conduct a thorough analysis of how different strategies influence the
+few-shot learning performance. The implementations are available at https://
+github.com/xinyadu/RGQA
+"
+ProtSi: Prototypical Siamese Network with Data Augmentation for Few-Shot  Subjective Answer Evaluation,Yining Lu,http://arxiv.org/pdf/2211.09855v1.pdf,2022-11-17,['cs.cl'],2211.09855v1.pdf,"  Subjective answer evaluation is a time-consuming and tedious task, and the
+quality of the evaluation is heavily influenced by a variety of subjective
+personal characteristics. Instead, machine evaluation can effectively assist
+educators in saving time while also ensuring that evaluations are fair and
+realistic. However, most existing methods using regular machine learning and
+natural language processing techniques are generally hampered by a lack of
+annotated answers and poor model interpretability, making them unsuitable for
+real-world use. To solve these challenges, we propose ProtSi Network, a unique
+semi-supervised architecture that for the first time uses few-shot learning to
+subjective answer evaluation. To evaluate students' answers by similarity
+prototypes, ProtSi Network simulates the natural process of evaluator scoring
+answers by combining Siamese Network which consists of BERT and encoder layers
+with Prototypical Network. We employed an unsupervised diverse paraphrasing
+model ProtAugment, in order to prevent overfitting for effective few-shot text
+classification. By integrating contrastive learning, the discriminative text
+issue can be mitigated. Experiments on the Kaggle Short Scoring Dataset
+demonstrate that the ProtSi Network outperforms the most recent baseline models
+in terms of accuracy and quadratic weighted kappa.
+"
+TEMPERA: Test-Time Prompting via Reinforcement Learning,Tianjun Zhang,http://arxiv.org/pdf/2211.11890v1.pdf,2022-11-21,"['cs.cl', 'cs.ai']",2211.11890v1.pdf,"  Careful prompt design is critical to the use of large language models in
+zero-shot or few-shot learning. As a consequence, there is a growing interest
+in automated methods to design optimal prompts. In this work, we propose
+Test-time Prompt Editing using Reinforcement learning (TEMPERA). In contrast to
+prior prompt generation methods, TEMPERA can efficiently leverage prior
+knowledge, is adaptive to different queries and provides an interpretable
+prompt for every query. To achieve this, we design a novel action space that
+allows flexible editing of the initial prompts covering a wide set of
+commonly-used components like instructions, few-shot exemplars, and
+verbalizers. The proposed method achieves significant gains compared with
+recent SoTA approaches like prompt tuning, AutoPrompt, and RLPrompt, across a
+variety of tasks including sentiment analysis, topic classification, natural
+language inference, and reading comprehension. Our method achieves 5.33x on
+average improvement in sample efficiency when compared to the traditional
+fine-tuning methods.
+"
+Towards Practical Few-shot Federated NLP,Dongqi Cai,http://arxiv.org/pdf/2212.00192v2.pdf,2022-12-01,"['cs.cl', 'cs.lg']",2212.00192v2.pdf,"  Transformer-based pre-trained models have emerged as the predominant solution
+for natural language processing (NLP). Fine-tuning such pre-trained models for
+downstream tasks often requires a considerable amount of labeled private data.
+In practice, private data is often distributed across heterogeneous mobile
+devices and may be prohibited from being uploaded. Moreover, well-curated
+labeled data is often scarce, presenting an additional challenge. To address
+these challenges, we first introduce a data generator for federated few-shot
+learning tasks, which encompasses the quantity and skewness of scarce labeled
+data in a realistic setting. Subsequently, we propose AUG-FedPrompt, a
+prompt-based federated learning system that exploits abundant unlabeled data
+for data augmentation. Our experiments indicate that AUG-FedPrompt can perform
+on par with full-set fine-tuning with a limited amount of labeled data.
+However, such competitive performance comes at a significant system cost.
+"
+Few-Shot Nested Named Entity Recognition,Hong Ming,http://arxiv.org/pdf/2212.00953v1.pdf,2022-12-02,"['cs.cl', 'cs.ai']",2212.00953v1.pdf,"  While Named Entity Recognition (NER) is a widely studied task, making
+inferences of entities with only a few labeled data has been challenging,
+especially for entities with nested structures. Unlike flat entities, entities
+and their nested entities are more likely to have similar semantic feature
+representations, drastically increasing difficulties in classifying different
+entity categories in the few-shot setting. Although prior work has briefly
+discussed nested structures in the context of few-shot learning, to our best
+knowledge, this paper is the first one specifically dedicated to studying the
+few-shot nested NER task. Leveraging contextual dependency to distinguish
+nested entities, we propose a Biaffine-based Contrastive Learning (BCL)
+framework. We first design a Biaffine span representation module for learning
+the contextual span dependency representation for each entity span rather than
+only learning its semantic representation. We then merge these two
+representations by the residual connection to distinguish nested entities.
+Finally, we build a contrastive learning framework to adjust the representation
+distribution for larger margin boundaries and more generalized domain transfer
+learning ability. We conducted experimental studies on three English, German,
+and Russian nested NER datasets. The results show that the BCL outperformed
+three baseline models on the 1-shot and 5-shot tasks in terms of F1 score.
+"
+Improving Few-Shot Performance of Language Models via Nearest Neighbor  Calibration,Feng Nie,http://arxiv.org/pdf/2212.02216v1.pdf,2022-12-05,['cs.cl'],2212.02216v1.pdf,"  Pre-trained language models (PLMs) have exhibited remarkable few-shot
+learning capabilities when provided a few examples in a natural language prompt
+as demonstrations of test instances, i.e., in-context learning. However, the
+performance of in-context learning is susceptible to the choice of prompt
+format, training examples and the ordering of the training examples. In this
+paper, we propose a novel nearest-neighbor calibration framework for in-context
+learning to ease this issue. It is inspired by a phenomenon that the in-context
+learning paradigm produces incorrect labels when inferring training instances,
+which provides a useful supervised signal to calibrate predictions. Thus, our
+method directly augments the predictions with a $k$-nearest-neighbor ($k$NN)
+classifier over a datastore of cached few-shot instance representations
+obtained by PLMs and their corresponding labels. Then adaptive neighbor
+selection and feature regularization modules are introduced to make full use of
+a few support instances to reduce the $k$NN retrieval noise. Experiments on
+various few-shot text classification tasks demonstrate that our method
+significantly improves in-context learning, while even achieving comparable
+performance with state-of-the-art tuning-based approaches in some sentiment
+analysis tasks.
+"
+JamPatoisNLI: A Jamaican Patois Natural Language Inference Dataset,Ruth-Ann Armstrong,http://arxiv.org/pdf/2212.03419v1.pdf,2022-12-07,"['cs.cl', 'cs.lg', 'i.2.7']",2212.03419v1.pdf,"  JamPatoisNLI provides the first dataset for natural language inference in a
+creole language, Jamaican Patois. Many of the most-spoken low-resource
+languages are creoles. These languages commonly have a lexicon derived from a
+major world language and a distinctive grammar reflecting the languages of the
+original speakers and the process of language birth by creolization. This gives
+them a distinctive place in exploring the effectiveness of transfer from large
+monolingual or multilingual pretrained models. While our work, along with
+previous work, shows that transfer from these models to low-resource languages
+that are unrelated to languages in their training set is not very effective, we
+would expect stronger results from transfer to creoles. Indeed, our experiments
+show considerably better results from few-shot learning of JamPatoisNLI than
+for such unrelated languages, and help us begin to understand how the unique
+relationship between creoles and their high-resource base languages affect
+cross-lingual transfer. JamPatoisNLI, which consists of naturally-occurring
+premises and expert-written hypotheses, is a step towards steering research
+into a traditionally underserved language and a useful benchmark for
+understanding cross-lingual NLP.
+"
+Learn to Explore: on Bootstrapping Interactive Data Exploration with  Meta-learning,Yukun Cao,http://arxiv.org/pdf/2212.03423v4.pdf,2022-12-07,"['cs.db', 'cs.ai']",2212.03423v4.pdf,"  Interactive data exploration (IDE) is an effective way of comprehending big
+data, whose volume and complexity are beyond human abilities. The main goal of
+IDE is to discover user interest regions from a database through multi-rounds
+of user labelling. Existing IDEs adopt active-learning framework, where users
+iteratively discriminate or label the interestingness of selected tuples. The
+process of data exploration can be viewed as the process of training a
+classifier, which determines whether a database tuple is interesting to a user.
+An efficient exploration thus takes very few iterations of user labelling to
+reach the data region of interest. In this work, we consider the data
+exploration as the process of few-shot learning, where the classifier is
+learned with only a few training examples, or exploration iterations. To this
+end, we propose a learning-to-explore framework, based on meta-learning, which
+learns how to learn a classifier with automatically generated meta-tasks, so
+that the exploration process can be much shortened. Extensive experiments on
+real datasets show that our proposal outperforms existing explore-by-example
+solutions in terms of accuracy and efficiency.
+"
+Demystifying Prompts in Language Models via Perplexity Estimation,Hila Gonen,http://arxiv.org/pdf/2212.04037v1.pdf,2022-12-08,['cs.cl'],2212.04037v1.pdf,"  Language models can be prompted to perform a wide variety of zero- and
+few-shot learning problems. However, performance varies significantly with the
+choice of prompt, and we do not yet understand why this happens or how to pick
+the best prompts. In this work, we analyze the factors that contribute to this
+variance and establish a new empirical hypothesis: the performance of a prompt
+is coupled with the extent to which the model is familiar with the language it
+contains. Over a wide range of tasks, we show that the lower the perplexity of
+the prompt is, the better the prompt is able to perform the task. As a result,
+we devise a method for creating prompts: (1) automatically extend a small seed
+set of manually written prompts by paraphrasing using GPT3 and backtranslation
+and (2) choose the lowest perplexity prompts to get significant gains in
+performance.
+"
+Technical Report -- Competition Solution for Prompt Tuning using  Pretrained Language Model,Jiang-Long Song,http://arxiv.org/pdf/2212.06369v3.pdf,2022-12-13,['cs.cl'],2212.06369v3.pdf,"  Prompt tuning recently becomes a hot-spot in the applications of large
+pretrained language models on specific downstream tasks. Regarding the Language
+Model as a Service (LMaaS), black-box tuning using derivative-free optimization
+(DFO) provides a novel approach to expand the practical scenarios of pretrained
+models and enrich the researches of few-shot learning. In this report, we
+present our solution in this competition that is based on the LMaaS scenario.
+Our solution consists of several modifications to BBTv2, including multiple
+label words, selection of P0, rolling update strategy, multi-task loss from MLP
+classifier, and finally using the ensemble method to further improve
+generalization ability. We also shared some strategies that we tried but didn't
+use in the final submission for further discussion. In the end we raised a
+question about the SNLI dataset and the impact on the results, as well as our
+concerns about the competition.
+"
+Localized Latent Updates for Fine-Tuning Vision-Language Models,Moritz Ibing,http://arxiv.org/pdf/2212.06556v1.pdf,2022-12-13,"['cs.cv', 'cs.cl', 'cs.lg']",2212.06556v1.pdf,"  Although massive pre-trained vision-language models like CLIP show impressive
+generalization capabilities for many tasks, still it often remains necessary to
+fine-tune them for improved performance on specific datasets. When doing so, it
+is desirable that updating the model is fast and that the model does not lose
+its capabilities on data outside of the dataset, as is often the case with
+classical fine-tuning approaches. In this work we suggest a lightweight
+adapter, that only updates the models predictions close to seen datapoints. We
+demonstrate the effectiveness and speed of this relatively simple approach in
+the context of few-shot learning, where our results both on classes seen and
+unseen during training are comparable with or improve on the state of the art.
+"
+ALERT: Adapting Language Models to Reasoning Tasks,Ping Yu,http://arxiv.org/pdf/2212.08286v2.pdf,2022-12-16,['cs.cl'],2212.08286v2.pdf,"  Current large language models can perform reasonably well on complex tasks
+that require step-by-step reasoning with few-shot learning. Are these models
+applying reasoning skills they have learnt during pre-training and reason
+outside of their training context, or are they simply memorizing their training
+corpus at finer granularity and have learnt to better understand their context?
+To tease apart these possibilities, we introduce ALERT, a benchmark and suite
+of analyses for assessing language models' reasoning ability comparing
+pre-trained and finetuned models on complex tasks that require reasoning skills
+to solve. ALERT provides a test bed to asses any language model on fine-grained
+reasoning skills, which spans over 20 datasets and covers 10 different
+reasoning skills. We leverage ALERT to further investigate the role of
+finetuning. With extensive empirical analysis we find that language models
+learn more reasoning skills such as textual entailment, abductive reasoning,
+and analogical reasoning during finetuning stage compared to pretraining state.
+We also find that when language models are finetuned they tend to overfit to
+the prompt template, which hurts the robustness of models causing
+generalization problems.
+"
+Learning from Taxonomy: Multi-label Few-Shot Classification for Everyday  Sound Recognition,Jinhua Liang,http://arxiv.org/pdf/2212.08952v1.pdf,2022-12-17,"['cs.sd', 'eess.as']",2212.08952v1.pdf,"  Everyday sound recognition aims to infer types of sound events in audio
+streams. While many works succeeded in training models with high performance in
+a fully-supervised manner, they are still restricted to the demand of large
+quantities of labelled data and the range of predefined classes. To overcome
+these drawbacks, this work firstly curates a new database named FSD-FS for
+multi-label few-shot audio classification. It then explores how to incorporate
+audio taxonomy in few-shot learning. Specifically, this work proposes
+label-dependent prototypical networks (LaD-protonet) to exploit parent-children
+relationships between labels. Plus, it applies taxonomy-aware label smoothing
+techniques to boost model performance. Experiments demonstrate that
+LaD-protonet outperforms original prototypical networks as well as other
+state-of-the-art methods. Moreover, its performance can be further boosted when
+combined with taxonomy-aware label smoothing.
+"
+Z-ICL: Zero-Shot In-Context Learning with Pseudo-Demonstrations,Xinxi Lyu,http://arxiv.org/pdf/2212.09865v2.pdf,2022-12-19,"['cs.cl', 'cs.ai']",2212.09865v2.pdf,"  Although large language models can be prompted for both zero- and few-shot
+learning, performance drops significantly when no demonstrations are available.
+In this paper, we introduce Z-ICL, a new zero-shot method that closes the gap
+by constructing pseudo-demonstrations for a given test input using a raw text
+corpus. Concretely, pseudo-demonstrations are constructed by (1) finding the
+nearest neighbors to the test input from the corpus and pairing them with
+random task labels, and (2) applying a set of techniques to reduce the amount
+of direct copying the model does from the resulting demonstrations. Evaluation
+on nine classification datasets shows that Z-ICL outperforms previous zero-shot
+methods by a significant margin, and is on par with in-context learning with
+labeled training data in the few-shot setting. Overall, Z-ICL provides a
+significantly higher estimate of the zero-shot performance levels of a model,
+and supports future efforts to develop better pseudo-demonstrations that
+further improve zero-shot results.
+"
+A Survey On Few-shot Knowledge Graph Completion with Structural and  Commonsense Knowledge,Haodi Ma,http://arxiv.org/pdf/2301.01172v1.pdf,2023-01-03,"['cs.cl', 'cs.ai', 'cs.lg']",2301.01172v1.pdf,"  Knowledge graphs (KG) have served as the key component of various natural
+language processing applications. Commonsense knowledge graphs (CKG) are a
+special type of KG, where entities and relations are composed of free-form
+text. However, previous works in KG completion and CKG completion suffer from
+long-tail relations and newly-added relations which do not have many know
+triples for training. In light of this, few-shot KG completion (FKGC), which
+requires the strengths of graph representation learning and few-shot learning,
+has been proposed to challenge the problem of limited annotated data. In this
+paper, we comprehensively survey previous attempts on such tasks in the form of
+a series of methods and applications. Specifically, we first introduce FKGC
+challenges, commonly used KGs, and CKGs. Then we systematically categorize and
+summarize existing works in terms of the type of KGs and the methods. Finally,
+we present applications of FKGC models on prediction tasks in different areas
+and share our thoughts on future research directions of FKGC.
+"
+Distillation of encoder-decoder transformers for sequence labelling,Marco Farina,http://arxiv.org/pdf/2302.05454v1.pdf,2023-02-10,"['cs.cl', 'cs.ir']",2302.05454v1.pdf,"  Driven by encouraging results on a wide range of tasks, the field of NLP is
+experiencing an accelerated race to develop bigger language models. This race
+for bigger models has also underscored the need to continue the pursuit of
+practical distillation approaches that can leverage the knowledge acquired by
+these big models in a compute-efficient manner. Having this goal in mind, we
+build on recent work to propose a hallucination-free framework for sequence
+tagging that is especially suited for distillation. We show empirical results
+of new state-of-the-art performance across multiple sequence labelling datasets
+and validate the usefulness of this framework for distilling a large model in a
+few-shot learning scenario.
+"
+Learning to Initialize: Can Meta Learning Improve Cross-task  Generalization in Prompt Tuning?,Chengwei Qin,http://arxiv.org/pdf/2302.08143v2.pdf,2023-02-16,"['cs.cl', 'cs.ai']",2302.08143v2.pdf,"  Prompt tuning (PT) which only tunes the embeddings of an additional sequence
+of tokens per task, keeping the pre-trained language model (PLM) frozen, has
+shown remarkable performance in few-shot learning. Despite this, PT has been
+shown to rely heavily on good initialization of the prompt embeddings. In this
+work, we study meta prompt tuning (MPT) to systematically explore how
+meta-learning can help improve (if it can) cross-task generalization in PT
+through learning to initialize the prompt embeddings from other relevant tasks.
+We empirically analyze a representative set of meta learning algorithms in a
+wide range of adaptation settings with different source/target task
+configurations on a large set of few-shot tasks. With extensive experiments and
+analysis, we demonstrate the effectiveness of MPT. We find the improvement to
+be significant particularly on classification tasks. For other kinds of tasks
+such as question answering, we observe that while MPT can outperform PT in most
+cases, it does not always outperform multi-task learning. We further provide an
+in-depth analysis from the perspective of task similarity.
+"
+Scalable Prompt Generation for Semi-supervised Learning with Language  Models,Yuhang Zhou,http://arxiv.org/pdf/2302.09236v1.pdf,2023-02-18,"['cs.cl', 'cs.ai']",2302.09236v1.pdf,"  Prompt-based learning methods in semi-supervised learning (SSL) settings have
+been shown to be effective on multiple natural language understanding (NLU)
+datasets and tasks in the literature. However, manually designing multiple
+prompts and verbalizers requires domain knowledge and human effort, making it
+difficult and expensive to scale across different datasets. In this paper, we
+propose two methods to automatically design multiple prompts and integrate
+automatic verbalizer in SSL settings without sacrificing performance. The first
+method uses various demonstration examples with learnable continuous prompt
+tokens to create diverse prompt models. The second method uses a varying number
+of soft prompt tokens to encourage language models to learn different prompts.
+For the verbalizer, we use the prototypical verbalizer to replace the manual
+one. In summary, we obtained the best average accuracy of 73.2% (a relative
+improvement of 2.52% over even the previous state-of-the-art SSL method with
+manual prompts and verbalizers) in different few-shot learning settings.
+"
+Language Models are Few-shot Learners for Prognostic Prediction,Zekai Chen,http://arxiv.org/pdf/2302.12692v4.pdf,2023-02-24,"['cs.cl', 'cs.ai', 'cs.lg', 'q-bio.qm']",2302.12692v4.pdf,"  Clinical prediction is an essential task in the healthcare industry. However,
+the recent success of transformers, on which large language models are built,
+has not been extended to this domain. In this research, we explore the use of
+transformers and language models in prognostic prediction for immunotherapy
+using real-world patients' clinical data and molecular profiles. This paper
+investigates the potential of transformers to improve clinical prediction
+compared to conventional machine learning approaches and addresses the
+challenge of few-shot learning in predicting rare disease areas. The study
+benchmarks the efficacy of baselines and language models on prognostic
+prediction across multiple cancer types and investigates the impact of
+different pretrained language models under few-shot regimes. The results
+demonstrate significant improvements in accuracy and highlight the potential of
+NLP in clinical research to improve early detection and intervention for
+different diseases.
+"
+Pre-Finetuning for Few-Shot Emotional Speech Recognition,Maximillian Chen,http://arxiv.org/pdf/2302.12921v2.pdf,2023-02-24,"['cs.cl', 'cs.lg', 'cs.sd', 'eess.as']",2302.12921v2.pdf,"  Speech models have long been known to overfit individual speakers for many
+classification tasks. This leads to poor generalization in settings where the
+speakers are out-of-domain or out-of-distribution, as is common in production
+environments. We view speaker adaptation as a few-shot learning problem and
+propose investigating transfer learning approaches inspired by recent success
+with pre-trained models in natural language tasks. We propose pre-finetuning
+speech models on difficult tasks to distill knowledge into few-shot downstream
+classification objectives. We pre-finetune Wav2Vec2.0 on every permutation of
+four multiclass emotional speech recognition corpora and evaluate our
+pre-finetuned models through 33,600 few-shot fine-tuning trials on the
+Emotional Speech Dataset.
+"
+Mixture of Soft Prompts for Controllable Data Generation,Derek Chen,http://arxiv.org/pdf/2303.01580v2.pdf,2023-03-02,['cs.cl'],2303.01580v2.pdf,"  Large language models (LLMs) effectively generate fluent text when the target
+output follows natural language patterns. However, structured prediction tasks
+confine the output format to a limited ontology, causing even very large models
+to struggle since they were never trained with such restrictions in mind. The
+difficulty of using LLMs for direct prediction is exacerbated in few-shot
+learning scenarios, which commonly arise due to domain shift and resource
+limitations. We flip the problem on its head by leveraging the LLM as a tool
+for data augmentation rather than direct prediction. Our proposed Mixture of
+Soft Prompts (MSP) serves as a parameter-efficient procedure for generating
+data in a controlled manner. Denoising mechanisms are further applied to
+improve the quality of synthesized data. Automatic metrics show our method is
+capable of producing diverse and natural text, while preserving label
+semantics. Moreover, MSP achieves state-of-the-art results on three benchmarks
+when compared against strong baselines. Our method offers an alternate
+data-centric approach for applying LLMs to complex prediction tasks.
+"
+Prismer: A Vision-Language Model with An Ensemble of Experts,Shikun Liu,http://arxiv.org/pdf/2303.02506v2.pdf,2023-03-04,"['cs.lg', 'cs.ai', 'cs.cv']",2303.02506v2.pdf,"  Recent vision-language models have shown impressive multi-modal generation
+capabilities. However, typically they require training huge models on massive
+datasets. As a more scalable alternative, we introduce Prismer, a data- and
+parameter-efficient vision-language model that leverages an ensemble of domain
+experts. Prismer only requires training of a small number of components, with
+the majority of network weights inherited from readily-available, pre-trained
+domain experts, and kept frozen during training. By leveraging experts from a
+wide range of domains, we show that Prismer can efficiently pool this expert
+knowledge and adapt it to various vision-language reasoning tasks. In our
+experiments, we show that Prismer achieves fine-tuned and few-shot learning
+performance which is competitive with current state-of-the-art models, whilst
+requiring up to two orders of magnitude less training data. Code is available
+at https://github.com/NVlabs/prismer.
+"
+Enhancing Activity Prediction Models in Drug Discovery with the Ability  to Understand Human Language,Philipp Seidl,http://arxiv.org/pdf/2303.03363v2.pdf,2023-03-06,"['q-bio.bm', 'cs.cl', 'cs.lg', 'stat.ml']",2303.03363v2.pdf,"  Activity and property prediction models are the central workhorses in drug
+discovery and materials sciences, but currently they have to be trained or
+fine-tuned for new tasks. Without training or fine-tuning, scientific language
+models could be used for such low-data tasks through their announced zero- and
+few-shot capabilities. However, their predictive quality at activity prediction
+is lacking. In this work, we envision a novel type of activity prediction model
+that is able to adapt to new prediction tasks at inference time, via
+understanding textual information describing the task. To this end, we propose
+a new architecture with separate modules for chemical and natural language
+inputs, and a contrastive pre-training objective on data from large biochemical
+databases. In extensive experiments, we show that our method CLAMP yields
+improved predictive performance on few-shot learning benchmarks and zero-shot
+problems in drug discovery. We attribute the advances of our method to the
+modularized architecture and to our pre-training objective.
+"
+MenuCraft: Interactive Menu System Design with Large Language Models,Amir Hossein Kargaran,http://arxiv.org/pdf/2303.04496v2.pdf,2023-03-08,"['cs.cl', 'cs.ai', 'cs.hc']",2303.04496v2.pdf,"  Menu system design is a challenging task involving many design options and
+various human factors. For example, one crucial factor that designers need to
+consider is the semantic and systematic relation of menu commands. However,
+capturing these relations can be challenging due to limited available
+resources. With the advancement of neural language models, large language
+models can utilize their vast pre-existing knowledge in designing and refining
+menu systems. In this paper, we propose MenuCraft, an AI-assisted designer for
+menu design that enables collaboration between the designer and a dialogue
+system to design menus. MenuCraft offers an interactive language-based menu
+design tool that simplifies the menu design process and enables easy
+customization of design options. MenuCraft supports a variety of interactions
+through dialog that allows performing zero/few-shot learning.
+"
+Consistency Analysis of ChatGPT,Myeongjun Erik Jang,http://arxiv.org/pdf/2303.06273v2.pdf,2023-03-11,"['cs.cl', 'cs.ai']",2303.06273v2.pdf,"  ChatGPT has gained a huge popularity since its introduction. Its positive
+aspects have been reported through many media platforms, and some analyses even
+showed that ChatGPT achieved a decent grade in professional exams, adding extra
+support to the claim that AI can now assist and even replace humans in
+industrial fields. Others, however, doubt its reliability and trustworthiness.
+This paper investigates the trustworthiness of ChatGPT and GPT-4 regarding
+logically consistent behaviour, focusing specifically on semantic consistency
+and the properties of negation, symmetric, and transitive consistency. Our
+findings suggest that while both models appear to show an enhanced language
+understanding and reasoning ability, they still frequently fall short of
+generating logically consistent predictions. We also ascertain via experiments
+that prompt designing, few-shot learning and employing larger large language
+models (LLMs) are unlikely to be the ultimate solution to resolve the
+inconsistency issue of LLMs.
+"
+Learning Expressive Prompting With Residuals for Vision Transformers,Rajshekhar Das,http://arxiv.org/pdf/2303.15591v1.pdf,2023-03-27,['cs.cv'],2303.15591v1.pdf,"  Prompt learning is an efficient approach to adapt transformers by inserting
+learnable set of parameters into the input and intermediate representations of
+a pre-trained model. In this work, we present Expressive Prompts with Residuals
+(EXPRES) which modifies the prompt learning paradigm specifically for effective
+adaptation of vision transformers (ViT). Out method constructs downstream
+representations via learnable ``output'' tokens, that are akin to the learned
+class tokens of the ViT. Further for better steering of the downstream
+representation processed by the frozen transformer, we introduce residual
+learnable tokens that are added to the output of various computations. We apply
+EXPRES for image classification, few shot learning, and semantic segmentation,
+and show our method is capable of achieving state of the art prompt tuning on
+3/3 categories of the VTAB benchmark. In addition to strong performance, we
+observe that our approach is an order of magnitude more prompt efficient than
+existing visual prompting baselines. We analytically show the computational
+benefits of our approach over weight space adaptation techniques like
+finetuning. Lastly we systematically corroborate the architectural design of
+our method via a series of ablation experiments.
+"
+Not All Features Matter: Enhancing Few-shot CLIP with Adaptive Prior  Refinement,Xiangyang Zhu,http://arxiv.org/pdf/2304.01195v1.pdf,2023-04-03,"['cs.cv', 'cs.ai', 'cs.mm']",2304.01195v1.pdf,"  The popularity of Contrastive Language-Image Pre-training (CLIP) has
+propelled its application to diverse downstream vision tasks. To improve its
+capacity on downstream tasks, few-shot learning has become a widely-adopted
+technique. However, existing methods either exhibit limited performance or
+suffer from excessive learnable parameters. In this paper, we propose APE, an
+Adaptive Prior rEfinement method for CLIP's pre-trained knowledge, which
+achieves superior accuracy with high computational efficiency. Via a prior
+refinement module, we analyze the inter-class disparity in the downstream data
+and decouple the domain-specific knowledge from the CLIP-extracted cache model.
+On top of that, we introduce two model variants, a training-free APE and a
+training-required APE-T. We explore the trilateral affinities between the test
+image, prior cache model, and textual representations, and only enable a
+lightweight category-residual module to be trained. For the average accuracy
+over 11 benchmarks, both APE and APE-T attain state-of-the-art and respectively
+outperform the second-best by +1.59% and +1.99% under 16 shots with x30 less
+learnable parameters.
+"
+Sociocultural knowledge is needed for selection of shots in hate speech  detection tasks,Antonis Maronikolakis,http://arxiv.org/pdf/2304.01890v4.pdf,2023-04-04,"['cs.cl', 'cs.ai', 'cs.lg']",2304.01890v4.pdf,"  We introduce HATELEXICON, a lexicon of slurs and targets of hate speech for
+the countries of Brazil, Germany, India and Kenya, to aid training and
+interpretability of models. We demonstrate how our lexicon can be used to
+interpret model predictions, showing that models developed to classify extreme
+speech rely heavily on target words when making predictions. Further, we
+propose a method to aid shot selection for training in low-resource settings
+via HATELEXICON. In few-shot learning, the selection of shots is of paramount
+importance to model performance. In our work, we simulate a few-shot setting
+for German and Hindi, using HASOC data for training and the Multilingual
+HateCheck (MHC) as a benchmark. We show that selecting shots based on our
+lexicon leads to models performing better on MHC than models trained on shots
+sampled randomly. Thus, when given only a few training examples, using our
+lexicon to select shots containing more sociocultural information leads to
+better few-shot performance.
+"
+Revisiting Automated Prompting: Are We Actually Doing Better?,Yulin Zhou,http://arxiv.org/pdf/2304.03609v2.pdf,2023-04-07,"['cs.cl', 'cs.lg']",2304.03609v2.pdf,"  Current literature demonstrates that Large Language Models (LLMs) are great
+few-shot learners, and prompting significantly increases their performance on a
+range of downstream tasks in a few-shot learning setting. An attempt to
+automate human-led prompting followed, with some progress achieved. In
+particular, subsequent work demonstrates automation can outperform fine-tuning
+in certain K-shot learning scenarios.
+  In this paper, we revisit techniques for automated prompting on six different
+downstream tasks and a larger range of K-shot learning settings. We find that
+automated prompting does not consistently outperform simple manual prompts. Our
+work suggests that, in addition to fine-tuning, manual prompts should be used
+as a baseline in this line of research.
+"
+MixPro: Simple yet Effective Data Augmentation for Prompt-based Learning,Bohan Li,http://arxiv.org/pdf/2304.09402v1.pdf,2023-04-19,"['cs.cl', 'cs.lg']",2304.09402v1.pdf,"  Prompt-based learning reformulates downstream tasks as cloze problems by
+combining the original input with a template. This technique is particularly
+useful in few-shot learning, where a model is trained on a limited amount of
+data. However, the limited templates and text used in few-shot prompt-based
+learning still leave significant room for performance improvement.
+Additionally, existing methods using model ensembles can constrain the model
+efficiency. To address these issues, we propose an augmentation method called
+MixPro, which augments both the vanilla input text and the templates through
+token-level, sentence-level, and epoch-level Mixup strategies. We conduct
+experiments on five few-shot datasets, and the results show that MixPro
+outperforms other augmentation baselines, improving model performance by an
+average of 5.08% compared to before augmentation.
+"
+Information Extraction from Documents: Question Answering vs Token  Classification in real-world setups,Laurent Lam,http://arxiv.org/pdf/2304.10994v1.pdf,2023-04-21,['cs.cl'],2304.10994v1.pdf,"  Research in Document Intelligence and especially in Document Key Information
+Extraction (DocKIE) has been mainly solved as Token Classification problem.
+Recent breakthroughs in both natural language processing (NLP) and computer
+vision helped building document-focused pre-training methods, leveraging a
+multimodal understanding of the document text, layout and image modalities.
+However, these breakthroughs also led to the emergence of a new DocKIE subtask
+of extractive document Question Answering (DocQA), as part of the Machine
+Reading Comprehension (MRC) research field. In this work, we compare the
+Question Answering approach with the classical token classification approach
+for document key information extraction. We designed experiments to benchmark
+five different experimental setups : raw performances, robustness to noisy
+environment, capacity to extract long entities, fine-tuning speed on Few-Shot
+Learning and finally Zero-Shot Learning. Our research showed that when dealing
+with clean and relatively short entities, it is still best to use token
+classification-based approach, while the QA approach could be a good
+alternative for noisy environment or long entities use-cases.
+"
+Discern and Answer: Mitigating the Impact of Misinformation in  Retrieval-Augmented Models with Discriminators,Giwon Hong,http://arxiv.org/pdf/2305.01579v1.pdf,2023-05-02,"['cs.cl', 'cs.ai']",2305.01579v1.pdf,"  Most existing retrieval-augmented language models (LMs) for question
+answering assume all retrieved information is factually correct. In this work,
+we study a more realistic scenario in which retrieved documents may contain
+misinformation, causing conflicts among them. We observe that the existing
+models are highly brittle to such information in both fine-tuning and
+in-context few-shot learning settings. We propose approaches to make
+retrieval-augmented LMs robust to misinformation by explicitly fine-tuning a
+discriminator or prompting to elicit discrimination capability in GPT-3. Our
+empirical results on open-domain question answering show that these approaches
+significantly improve LMs' robustness to knowledge conflicts. We also provide
+our findings on interleaving the fine-tuned model's decision with the
+in-context learning process, paving a new path to leverage the best of both
+worlds.
+"
+Causal Interventions-based Few-Shot Named Entity Recognition,Zhen Yang,http://arxiv.org/pdf/2305.01914v1.pdf,2023-05-03,['cs.cl'],2305.01914v1.pdf,"  Few-shot named entity recognition (NER) systems aims at recognizing new
+classes of entities based on a few labeled samples. A significant challenge in
+the few-shot regime is prone to overfitting than the tasks with abundant
+samples. The heavy overfitting in few-shot learning is mainly led by spurious
+correlation caused by the few samples selection bias. To alleviate the problem
+of the spurious correlation in the few-shot NER, in this paper, we propose a
+causal intervention-based few-shot NER method. Based on the prototypical
+network, the method intervenes in the context and prototype via backdoor
+adjustment during training. In particular, intervening in the context of the
+one-shot scenario is very difficult, so we intervene in the prototype via
+incremental learning, which can also avoid catastrophic forgetting. Our
+experiments on different benchmarks show that our approach achieves new
+state-of-the-art results (achieving up to 29% absolute improvement and 12% on
+average for all tasks).
+"
+Plug-and-Play Multilingual Few-shot Spoken Words Recognition,Aaqib Saeed,http://arxiv.org/pdf/2305.03058v1.pdf,2023-05-03,"['eess.as', 'cs.lg', 'cs.sd']",2305.03058v1.pdf,"  As technology advances and digital devices become prevalent, seamless
+human-machine communication is increasingly gaining significance. The growing
+adoption of mobile, wearable, and other Internet of Things (IoT) devices has
+changed how we interact with these smart devices, making accurate spoken words
+recognition a crucial component for effective interaction. However, building
+robust spoken words detection system that can handle novel keywords remains
+challenging, especially for low-resource languages with limited training data.
+Here, we propose PLiX, a multilingual and plug-and-play keyword spotting system
+that leverages few-shot learning to harness massive real-world data and enable
+the recognition of unseen spoken words at test-time. Our few-shot deep models
+are learned with millions of one-second audio clips across 20 languages,
+achieving state-of-the-art performance while being highly efficient. Extensive
+evaluations show that PLiX can generalize to novel spoken words given as few as
+just one support example and performs well on unseen languages out of the box.
+We release models and inference code to serve as a foundation for future
+research and voice-enabled user interface development for emerging devices.
+"
+Data Curation for Image Captioning with Text-to-Image Generative Models,Wenyan Li,http://arxiv.org/pdf/2305.03610v1.pdf,2023-05-05,"['cs.cv', 'cs.ai', 'cs.cl']",2305.03610v1.pdf,"  Recent advances in image captioning are mainly driven by large-scale
+vision-language pretraining, relying heavily on computational resources and
+increasingly large multimodal datasets. Instead of scaling up pretraining data,
+we ask whether it is possible to improve performance by improving the quality
+of the samples in existing datasets. We pursue this question through two
+approaches to data curation: one that assumes that some examples should be
+avoided due to mismatches between the image and caption, and one that assumes
+that the mismatch can be addressed by replacing the image, for which we use the
+state-of-the-art Stable Diffusion model. These approaches are evaluated using
+the BLIP model on MS COCO and Flickr30K in both finetuning and few-shot
+learning settings. Our simple yet effective approaches consistently outperform
+baselines, indicating that better image captioning models can be trained by
+curating existing resources. Finally, we conduct a human study to understand
+the errors made by the Stable Diffusion model and highlight directions for
+future work in text-to-image generation.
+"
+Make Prompt-based Black-Box Tuning Colorful: Boosting Model  Generalization from Three Orthogonal Perspectives,Qiushi Sun,http://arxiv.org/pdf/2305.08088v1.pdf,2023-05-14,"['cs.cl', 'cs.ai']",2305.08088v1.pdf,"  Large language models (LLMs) have shown increasing power on various natural
+language processing (NLP) tasks. However, tuning these models for downstream
+tasks usually needs exorbitant costs or is unavailable due to commercial
+considerations. Recently, black-box tuning has been proposed to address this
+problem by optimizing task-specific prompts without accessing the gradients and
+hidden representations. However, most existing works have yet fully exploited
+the potential of gradient-free optimization under the scenario of few-shot
+learning. In this paper, we describe BBT-RGB, a suite of straightforward and
+complementary techniques for enhancing the efficiency and performance of
+black-box optimization. Specifically, our method includes three plug-and-play
+components: (1) Two-stage derivative-free optimization strategy that
+facilitates fast convergence and mitigates overfitting; (2) Automatic
+verbalizer construction with its novel usage under few-shot settings; (3)
+Better prompt initialization policy based on instruction search and
+auto-selected demonstration. Extensive experiments across various tasks on
+natural language understanding and inference demonstrate the effectiveness of
+our method. Our codes are publicly available at
+https://github.com/QiushiSun/BBT-RGB.
+"
+CPL-NoViD: Context-Aware Prompt-based Learning for Norm Violation  Detection in Online Communities,Zihao He,http://arxiv.org/pdf/2305.09846v2.pdf,2023-05-16,"['cs.cl', 'cs.si']",2305.09846v2.pdf,"  Detecting norm violations in online communities is critical to maintaining
+healthy and safe spaces for online discussions. Existing machine learning
+approaches often struggle to adapt to the diverse rules and interpretations
+across different communities due to the inherent challenges of fine-tuning
+models for such context-specific tasks. In this paper, we introduce
+Context-aware Prompt-based Learning for Norm Violation Detection (CPL-NoViD), a
+novel method that employs prompt-based learning to detect norm violations
+across various types of rules. CPL-NoViD outperforms the baseline by
+incorporating context through natural language prompts and demonstrates
+improved performance across different rule types. Significantly, it not only
+excels in cross-rule-type and cross-community norm violation detection but also
+exhibits adaptability in few-shot learning scenarios. Most notably, it
+establishes a new state-of-the-art in norm violation detection, surpassing
+existing benchmarks. Our work highlights the potential of prompt-based learning
+for context-sensitive norm violation detection and paves the way for future
+research on more adaptable, context-aware models to better support online
+community moderators.
+"
+A Weak Supervision Approach for Few-Shot Aspect Based Sentiment,Robert Vacareanu,http://arxiv.org/pdf/2305.11979v1.pdf,2023-05-19,['cs.cl'],2305.11979v1.pdf,"  We explore how weak supervision on abundant unlabeled data can be leveraged
+to improve few-shot performance in aspect-based sentiment analysis (ABSA)
+tasks. We propose a pipeline approach to construct a noisy ABSA dataset, and we
+use it to adapt a pre-trained sequence-to-sequence model to the ABSA tasks. We
+test the resulting model on three widely used ABSA datasets, before and after
+fine-tuning. Our proposed method preserves the full fine-tuning performance
+while showing significant improvements (15.84% absolute F1) in the few-shot
+learning scenario for the harder tasks. In zero-shot (i.e., without
+fine-tuning), our method outperforms the previous state of the art on the
+aspect extraction sentiment classification (AESC) task and is, additionally,
+capable of performing the harder aspect sentiment triplet extraction (ASTE)
+task.
+"
+Efficient Open Domain Multi-Hop Question Answering with Few-Shot Data  Synthesis,Mingda Chen,http://arxiv.org/pdf/2305.13691v1.pdf,2023-05-23,['cs.cl'],2305.13691v1.pdf,"  Few-shot learning for open domain multi-hop question answering typically
+relies on large language models (LLMs). While powerful, LLMs are inefficient at
+the inference time. We propose a data synthesis framework for multi-hop
+question answering that allows for improving smaller language models with less
+than 10 human-annotated question answer pairs. The framework is built upon the
+data generation functions parameterized by LLMs and prompts, which requires
+minimal hand-crafted features. Empirically, we synthesize millions of multi-hop
+questions and claims. After finetuning language models on the synthetic data,
+we evaluate the models on popular benchmarks on multi-hop question answering
+and fact verification. Our experimental results show that finetuning on the
+synthetic data improves model performance significantly, allowing our finetuned
+models to be competitive with prior models while being almost one-third the
+size in terms of parameter counts.
+"
+Images in Language Space: Exploring the Suitability of Large Language  Models for Vision & Language Tasks,Sherzod Hakimov,http://arxiv.org/pdf/2305.13782v1.pdf,2023-05-23,['cs.cl'],2305.13782v1.pdf,"  Large language models have demonstrated robust performance on various
+language tasks using zero-shot or few-shot learning paradigms. While being
+actively researched, multimodal models that can additionally handle images as
+input have yet to catch up in size and generality with language-only models. In
+this work, we ask whether language-only models can be utilised for tasks that
+require visual input -- but also, as we argue, often require a strong reasoning
+component. Similar to some recent related work, we make visual information
+accessible to the language model using separate verbalisation models.
+Specifically, we investigate the performance of open-source, open-access
+language models against GPT-3 on five vision-language tasks when given
+textually-encoded visual information. Our results suggest that language models
+are effective for solving vision-language tasks even with limited samples. This
+approach also enhances the interpretability of a model's output by providing a
+means of tracing the output back through the verbalised image content.
+"
+Improving Factuality and Reasoning in Language Models through Multiagent  Debate,Yilun Du,http://arxiv.org/pdf/2305.14325v1.pdf,2023-05-23,"['cs.cl', 'cs.ai', 'cs.cv', 'cs.lg']",2305.14325v1.pdf,"  Large language models (LLMs) have demonstrated remarkable capabilities in
+language generation, understanding, and few-shot learning in recent years. An
+extensive body of work has explored how their performance may be further
+improved through the tools of prompting, ranging from verification,
+self-consistency, or intermediate scratchpads. In this paper, we present a
+complementary approach to improve language responses where multiple language
+model instances propose and debate their individual responses and reasoning
+processes over multiple rounds to arrive at a common final answer. Our findings
+indicate that this approach significantly enhances mathematical and strategic
+reasoning across a number of tasks. We also demonstrate that our approach
+improves the factual validity of generated content, reducing fallacious answers
+and hallucinations that contemporary models are prone to. Our approach may be
+directly applied to existing black-box models and uses identical procedure and
+prompts for all tasks we investigate. Overall, our findings suggest that such
+""society of minds"" approach has the potential to significantly advance the
+capabilities of LLMs and pave the way for further breakthroughs in language
+generation and understanding.
+"
+Are Large Language Models Robust Zero-shot Coreference Resolvers?,Nghia T. Le,http://arxiv.org/pdf/2305.14489v1.pdf,2023-05-23,['cs.cl'],2305.14489v1.pdf,"  Recent progress in domain adaptation for coreference resolution relies on
+continued training using annotated data from target domains. At the same time,
+pre-trained large language models (LMs) have exhibited strong zero- and
+few-shot learning abilities across a wide range of NLP tasks including pronoun
+resolution. While this demonstrates evidence of coreference ability, previous
+work has mostly studied this ability using simple sentence-level datasets such
+as the Winograd Schema Challenge. In this work, we assess the feasibility of
+zero-shot learning for coreference resolution by evaluating instruction-tuned
+language models on more difficult, linguistically-complex coreference
+benchmarks (e.g., CoNLL-2012). We demonstrate that zero-shot prompting
+outperforms current unsupervised coreference systems. Further investigations
+reveal the robust zero-shot generalization ability of instruction-tuned LMs
+across a wide range of domains, languages, and time periods, as well as a
+strong reliance on high-quality mention detection systems.
+"
+Training on Thin Air: Improve Image Classification with Generated Data,Yongchao Zhou,http://arxiv.org/pdf/2305.15316v1.pdf,2023-05-24,"['cs.cv', 'cs.lg']",2305.15316v1.pdf,"  Acquiring high-quality data for training discriminative models is a crucial
+yet challenging aspect of building effective predictive systems. In this paper,
+we present Diffusion Inversion, a simple yet effective method that leverages
+the pre-trained generative model, Stable Diffusion, to generate diverse,
+high-quality training data for image classification. Our approach captures the
+original data distribution and ensures data coverage by inverting images to the
+latent space of Stable Diffusion, and generates diverse novel training images
+by conditioning the generative model on noisy versions of these vectors. We
+identify three key components that allow our generated images to successfully
+supplant the original dataset, leading to a 2-3x enhancement in sample
+complexity and a 6.5x decrease in sampling time. Moreover, our approach
+consistently outperforms generic prompt-based steering methods and KNN
+retrieval baseline across a wide range of datasets. Additionally, we
+demonstrate the compatibility of our approach with widely-used data
+augmentation techniques, as well as the reliability of the generated data in
+supporting various neural architectures and enhancing few-shot learning.
+"
+ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR  Back-Translation,Kuan-Hao Huang,http://arxiv.org/pdf/2305.16585v1.pdf,2023-05-26,['cs.cl'],2305.16585v1.pdf,"  Paraphrase generation is a long-standing task in natural language processing
+(NLP). Supervised paraphrase generation models, which rely on human-annotated
+paraphrase pairs, are cost-inefficient and hard to scale up. On the other hand,
+automatically annotated paraphrase pairs (e.g., by machine back-translation),
+usually suffer from the lack of syntactic diversity -- the generated paraphrase
+sentences are very similar to the source sentences in terms of syntax. In this
+work, we present ParaAMR, a large-scale syntactically diverse paraphrase
+dataset created by abstract meaning representation back-translation. Our
+quantitative analysis, qualitative examples, and human evaluation demonstrate
+that the paraphrases of ParaAMR are syntactically more diverse compared to
+existing large-scale paraphrase datasets while preserving good semantic
+similarity. In addition, we show that ParaAMR can be used to improve on three
+NLP tasks: learning sentence embeddings, syntactically controlled paraphrase
+generation, and data augmentation for few-shot learning. Our results thus
+showcase the potential of ParaAMR for improving various NLP applications.
+"
+Adapting Language-Audio Models as Few-Shot Audio Learners,Jinhua Liang,http://arxiv.org/pdf/2305.17719v1.pdf,2023-05-28,"['eess.as', 'cs.sd']",2305.17719v1.pdf,"  We presented the Treff adapter, a training-efficient adapter for CLAP, to
+boost zero-shot classification performance by making use of a small set of
+labelled data. Specifically, we designed CALM to retrieve the probability
+distribution of text-audio clips over classes using a set of audio-label pairs
+and combined it with CLAP's zero-shot classification results. Furthermore, we
+designed a training-free version of the Treff adapter by using CALM as a cosine
+similarity measure. Experiments showed that the proposed Treff adapter is
+comparable and even better than fully-supervised methods and adaptation methods
+in low-shot and data-abundant scenarios. While the Treff adapter shows that
+combining large-scale pretraining and rapid learning of domain-specific
+knowledge is non-trivial for obtaining generic representations for few-shot
+learning, it is still limited to audio classification tasks. In the future, we
+will explore how to use audio-language models in diverse audio domains.
+"
+Transfer Learning for Power Outage Detection Task with Limited Training  Data,Olukunle Owolabi,http://arxiv.org/pdf/2305.17817v1.pdf,2023-05-28,"['cs.cl', 'stat.ap']",2305.17817v1.pdf,"  Early detection of power outages is crucial for maintaining a reliable power
+distribution system. This research investigates the use of transfer learning
+and language models in detecting outages with limited labeled data. By
+leveraging pretraining and transfer learning, models can generalize to unseen
+classes.
+  Using a curated balanced dataset of social media tweets related to power
+outages, we conducted experiments using zero-shot and few-shot learning. Our
+hypothesis is that Language Models pretrained with limited data could achieve
+high performance in outage detection tasks over baseline models. Results show
+that while classical models outperform zero-shot Language Models, few-shot
+fine-tuning significantly improves their performance. For example, with 10%
+fine-tuning, BERT achieves 81.3% accuracy (+15.3%), and GPT achieves 74.5%
+accuracy (+8.5%). This has practical implications for analyzing and localizing
+outages in scenarios with limited data availability.
+  Our evaluation provides insights into the potential of few-shot fine-tuning
+with Language Models for power outage detection, highlighting their strengths
+and limitations. This research contributes to the knowledge base of leveraging
+advanced natural language processing techniques for managing critical
+infrastructure.
+"
+Deeply Coupled Cross-Modal Prompt Learning,Xuejing Liu,http://arxiv.org/pdf/2305.17903v2.pdf,2023-05-29,['cs.cv'],2305.17903v2.pdf,"  Recent advancements in multimodal foundation models (e.g., CLIP) have
+excelled in zero-shot generalization. Prompt tuning involved in the knowledge
+transfer from foundation models to downstream tasks has gained significant
+attention recently. Existing prompt-tuning methods in cross-modal learning,
+however, either solely focus on language branch, or learn vision-language
+interaction in a shallow mechanism. In this context, we propose a Deeply
+coupled Cross-modal Prompt learning (DCP) method based on CLIP. DCP flexibly
+accommodates the interplay between vision and language with a Cross-Modal
+Prompt Attention (CMPA) mechanism, which enables the mutual exchange of
+respective representation through a well-connected multi-head attention module
+progressively and strongly. We then conduct comprehensive few-shot learning
+experiments on 11 image classification datasets and analyze the robustness to
+domain shift as well. Thorough experimental analysis evidently demonstrates the
+superb few-shot generalization and compelling domain adaption capacity of a
+well-executed DCP. The code can be found at https://github.com/GingL/CMPA.
+"
+"What does the Failure to Reason with ""Respectively"" in Zero/Few-Shot  Settings Tell Us about Language Models?",Ruixiang Cui,http://arxiv.org/pdf/2305.19597v1.pdf,2023-05-31,"['cs.cl', 'cs.ai']",2305.19597v1.pdf,"  Humans can effortlessly understand the coordinate structure of sentences such
+as ""Niels Bohr and Kurt Cobain were born in Copenhagen and Seattle,
+respectively"". In the context of natural language inference (NLI), we examine
+how language models (LMs) reason with respective readings (Gawron and Kehler,
+2004) from two perspectives: syntactic-semantic and commonsense-world
+knowledge. We propose a controlled synthetic dataset WikiResNLI and a naturally
+occurring dataset NatResNLI to encompass various explicit and implicit
+realizations of ""respectively"". We show that fine-tuned NLI models struggle
+with understanding such readings without explicit supervision. While few-shot
+learning is easy in the presence of explicit cues, longer training is required
+when the reading is evoked implicitly, leaving models to rely on common sense
+inferences. Furthermore, our fine-grained analysis indicates models fail to
+generalize across different constructions. To conclude, we demonstrate that LMs
+still lag behind humans in generalizing to the long tail of linguistic
+constructions.
+"
+Measuring the Robustness of Natural Language Processing Models to Domain  Shifts,Nitay Calderon,http://arxiv.org/pdf/2306.00168v2.pdf,2023-05-31,['cs.cl'],2306.00168v2.pdf,"  Existing research on Domain Robustness (DR) suffers from disparate setups,
+lack of evaluation task variety, and reliance on challenge sets. In this paper,
+we pose a fundamental question: What is the state of affairs of the DR
+challenge in the era of Large Language Models (LLMs)? To this end, we construct
+a DR benchmark comprising diverse NLP tasks, including sentence and token-level
+classification, QA, and generation, each task consists of several domains. We
+explore the DR challenge of fine-tuned and few-shot learning models in natural
+domain shift settings and devise two diagnostic metrics of Out-of-Distribution
+(OOD) performance degradation: The commonly used Source Drop (SD) and the
+overlooked Target Drop (TD). Our findings reveal important insights: First,
+despite their capabilities, zero-to-few shot LLMs and fine-tuning approaches
+still fail to meet satisfactory performance in the OOD context; Second, TD
+approximates better than SD the average OOD degradation; Third, in a
+significant proportion of domain shifts, either SD or TD is positive, but not
+both, and therefore disregarding one can lead to incorrect DR conclusions.
+"
+Human-like Few-Shot Learning via Bayesian Reasoning over Natural  Language,Kevin Ellis,http://arxiv.org/pdf/2306.02797v3.pdf,2023-06-05,"['cs.cl', 'cs.ai', 'cs.lg']",2306.02797v3.pdf,"  A core tension in models of concept learning is that the model must carefully
+balance the tractability of inference against the expressivity of the
+hypothesis class. Humans, however, can efficiently learn a broad range of
+concepts. We introduce a model of inductive learning that seeks to be
+human-like in that sense. It implements a Bayesian reasoning process where a
+language model first proposes candidate hypotheses expressed in natural
+language, which are then re-weighed by a prior and a likelihood. By estimating
+the prior from human data, we can predict human judgments on learning problems
+involving numbers and sets, spanning concepts that are generative,
+discriminative, propositional, and higher-order.
+"
+Few Shot Rationale Generation using Self-Training with Dual Teachers,Aditya Srikanth Veerubhotla,http://arxiv.org/pdf/2306.03315v1.pdf,2023-06-05,"['cs.cl', 'cs.ai']",2306.03315v1.pdf,"  Self-rationalizing models that also generate a free-text explanation for
+their predicted labels are an important tool to build trustworthy AI
+applications. Since generating explanations for annotated labels is a laborious
+and costly pro cess, recent models rely on large pretrained language models
+(PLMs) as their backbone and few-shot learning. In this work we explore a
+self-training approach leveraging both labeled and unlabeled data to further
+improve few-shot models, under the assumption that neither human written
+rationales nor annotated task labels are available at scale. We introduce a
+novel dual-teacher learning framework, which learns two specialized teacher
+models for task prediction and rationalization using self-training and distills
+their knowledge into a multi-tasking student model that can jointly generate
+the task label and rationale. Furthermore, we formulate a new loss function,
+Masked Label Regularization (MLR) which promotes explanations to be strongly
+conditioned on predicted labels. Evaluation on three public datasets
+demonstrate that the proposed methods are effective in modeling task labels and
+generating faithful rationales.
+"
+A New Dataset and Empirical Study for Sentence Simplification in Chinese,Shiping Yang,http://arxiv.org/pdf/2306.04188v1.pdf,2023-06-07,['cs.cl'],2306.04188v1.pdf,"  Sentence Simplification is a valuable technique that can benefit language
+learners and children a lot. However, current research focuses more on English
+sentence simplification. The development of Chinese sentence simplification is
+relatively slow due to the lack of data. To alleviate this limitation, this
+paper introduces CSS, a new dataset for assessing sentence simplification in
+Chinese. We collect manual simplifications from human annotators and perform
+data analysis to show the difference between English and Chinese sentence
+simplifications. Furthermore, we test several unsupervised and zero/few-shot
+learning methods on CSS and analyze the automatic evaluation and human
+evaluation results. In the end, we explore whether Large Language Models can
+serve as high-quality Chinese sentence simplification systems by evaluating
+them on CSS.
+"
+Can AI Moderate Online Communities?,Henrik Axelsen,http://arxiv.org/pdf/2306.05122v1.pdf,2023-06-08,['cs.cy'],2306.05122v1.pdf,"  The task of cultivating healthy communication in online communities becomes
+increasingly urgent, as gaming and social media experiences become
+progressively more immersive and life-like. We approach the challenge of
+moderating online communities by training student models using a large language
+model (LLM). We use zero-shot learning models to distill and expand datasets
+followed by a few-shot learning and a fine-tuning approach, leveraging
+open-access generative pre-trained transformer models (GPT) from OpenAI. Our
+preliminary findings suggest, that when properly trained, LLMs can excel in
+identifying actor intentions, moderating toxic comments, and rewarding positive
+contributions. The student models perform above-expectation in non-contextual
+assignments such as identifying classically toxic behavior and perform
+sufficiently on contextual assignments such as identifying positive
+contributions to online discourse. Further, using open-access models like
+OpenAI's GPT we experience a step-change in the development process for what
+has historically been a complex modeling task. We contribute to the information
+system (IS) discourse with a rapid development framework on the application of
+generative AI in content online moderation and management of culture in
+decentralized, pseudonymous communities by providing a sample model suite of
+industrial-ready generative AI models based on open-access LLMs.
+"
+The ADAIO System at the BEA-2023 Shared Task on Generating AI Teacher  Responses in Educational Dialogues,Adaeze Adigwe,http://arxiv.org/pdf/2306.05360v1.pdf,2023-06-08,"['cs.cl', 'cs.ai', 'cs.cy']",2306.05360v1.pdf,"  This paper presents the ADAIO team's system entry in the Building Educational
+Applications (BEA) 2023 Shared Task on Generating AI Teacher Responses in
+Educational Dialogues. The task aims to assess the performance of
+state-of-the-art generative models as AI teachers in producing suitable
+responses within a student-teacher dialogue. Our system comprises evaluating
+various baseline models using OpenAI GPT-3 and designing diverse prompts to
+prompt the OpenAI models for teacher response generation. After the challenge,
+our system achieved second place by employing a few-shot prompt-based approach
+with the OpenAI text-davinci-003 model. The results highlight the few-shot
+learning capabilities of large-language models, particularly OpenAI's GPT-3, in
+the role of AI teachers.
+"
+Prompt-based Extraction of Social Determinants of Health Using Few-shot  Learning,Giridhar Kaushik Ramachandran,http://arxiv.org/pdf/2306.07170v1.pdf,2023-06-12,['cs.cl'],2306.07170v1.pdf,"  Social determinants of health (SDOH) documented in the electronic health
+record through unstructured text are increasingly being studied to understand
+how SDOH impacts patient health outcomes. In this work, we utilize the Social
+History Annotation Corpus (SHAC), a multi-institutional corpus of de-identified
+social history sections annotated for SDOH, including substance use,
+employment, and living status information. We explore the automatic extraction
+of SDOH information with SHAC in both standoff and inline annotation formats
+using GPT-4 in a one-shot prompting setting. We compare GPT-4 extraction
+performance with a high-performing supervised approach and perform thorough
+error analyses. Our prompt-based GPT-4 method achieved an overall 0.652 F1 on
+the SHAC test set, similar to the 7th best-performing system among all teams in
+the n2c2 challenge with SHAC.
+"
+Rethink the Effectiveness of Text Data Augmentation: An Empirical  Analysis,Zhengxiang Shi,http://arxiv.org/pdf/2306.07664v1.pdf,2023-06-13,"['cs.cl', 'cs.ai', 'cs.lg']",2306.07664v1.pdf,"  In recent years, language models (LMs) have made remarkable progress in
+advancing the field of natural language processing (NLP). However, the impact
+of data augmentation (DA) techniques on the fine-tuning (FT) performance of
+these LMs has been a topic of ongoing debate. In this study, we evaluate the
+effectiveness of three different FT methods in conjugation with
+back-translation across an array of 7 diverse NLP tasks, including
+classification and regression types, covering single-sentence and sentence-pair
+tasks. Contrary to prior assumptions that DA does not contribute to the
+enhancement of LMs' FT performance, our findings reveal that continued
+pre-training on augmented data can effectively improve the FT performance of
+the downstream tasks. In the most favourable case, continued pre-training
+improves the performance of FT by more than 10% in the few-shot learning
+setting. Our finding highlights the potential of DA as a powerful tool for
+bolstering LMs' performance.
+"
+Neural Fine-Tuning Search for Few-Shot Learning,Panagiotis Eustratiadis,http://arxiv.org/pdf/2306.09295v1.pdf,2023-06-15,"['cs.cv', 'cs.lg']",2306.09295v1.pdf,"  In few-shot recognition, a classifier that has been trained on one set of
+classes is required to rapidly adapt and generalize to a disjoint, novel set of
+classes. To that end, recent studies have shown the efficacy of fine-tuning
+with carefully crafted adaptation architectures. However this raises the
+question of: How can one design the optimal adaptation strategy? In this paper,
+we study this question through the lens of neural architecture search (NAS).
+Given a pre-trained neural network, our algorithm discovers the optimal
+arrangement of adapters, which layers to keep frozen and which to fine-tune. We
+demonstrate the generality of our NAS method by applying it to both residual
+networks and vision transformers and report state-of-the-art performance on
+Meta-Dataset and Meta-Album.
+"
+Multilingual Few-Shot Learning via Language Model Retrieval,Genta Indra Winata,http://arxiv.org/pdf/2306.10964v1.pdf,2023-06-19,['cs.cl'],2306.10964v1.pdf,"  Transformer-based language models have achieved remarkable success in
+few-shot in-context learning and drawn a lot of research interest. However,
+these models' performance greatly depends on the choice of the example prompts
+and also has high variability depending on how samples are chosen. In this
+paper, we conduct a comprehensive study of retrieving semantically similar
+few-shot samples and using them as the context, as it helps the model decide
+the correct label without any gradient update in the multilingual and
+cross-lingual settings. We evaluate the proposed method on five natural
+language understanding datasets related to intent detection, question
+classification, sentiment analysis, and topic classification. The proposed
+method consistently outperforms random sampling in monolingual and
+cross-lingual tasks in non-English languages.
+"
+Language models are weak learners,Hariharan Manikandan,http://arxiv.org/pdf/2306.14101v1.pdf,2023-06-25,"['cs.lg', 'cs.ai']",2306.14101v1.pdf,"  A central notion in practical and theoretical machine learning is that of a
+$\textit{weak learner}$, classifiers that achieve better-than-random
+performance (on any given distribution over data), even by a small margin. Such
+weak learners form the practical basis for canonical machine learning methods
+such as boosting. In this work, we illustrate that prompt-based large language
+models can operate effectively as said weak learners. Specifically, we
+illustrate the use of a large language model (LLM) as a weak learner in a
+boosting algorithm applied to tabular data. We show that by providing (properly
+sampled according to the distribution of interest) text descriptions of tabular
+data samples, LLMs can produce a summary of the samples that serves as a
+template for classification and achieves the aim of acting as a weak learner on
+this task. We incorporate these models into a boosting approach, which in some
+settings can leverage the knowledge within the LLM to outperform traditional
+tree-based boosting. The model outperforms both few-shot learning and
+occasionally even more involved fine-tuning procedures, particularly for tasks
+involving small numbers of data points. The results illustrate the potential
+for prompt-based LLMs to function not just as few-shot learners themselves, but
+as components of larger machine learning pipelines.
+"
+RobuT: A Systematic Study of Table QA Robustness Against Human-Annotated  Adversarial Perturbations,Yilun Zhao,http://arxiv.org/pdf/2306.14321v1.pdf,2023-06-25,"['cs.cl', 'cs.ai']",2306.14321v1.pdf,"  Despite significant progress having been made in question answering on
+tabular data (Table QA), it's unclear whether, and to what extent existing
+Table QA models are robust to task-specific perturbations, e.g., replacing key
+question entities or shuffling table columns. To systematically study the
+robustness of Table QA models, we propose a benchmark called RobuT, which
+builds upon existing Table QA datasets (WTQ, WikiSQL-Weak, and SQA) and
+includes human-annotated adversarial perturbations in terms of table header,
+table content, and question. Our results indicate that both state-of-the-art
+Table QA models and large language models (e.g., GPT-3) with few-shot learning
+falter in these adversarial sets. We propose to address this problem by using
+large language models to generate adversarial examples to enhance training,
+which significantly improves the robustness of Table QA models. Our data and
+code is publicly available at https://github.com/yilunzhao/RobuT.
+"
+Benchmarking Large Language Model Capabilities for Conditional  Generation,Joshua Maynez,http://arxiv.org/pdf/2306.16793v1.pdf,2023-06-29,['cs.cl'],2306.16793v1.pdf,"  Pre-trained large language models (PLMs) underlie most new developments in
+natural language processing. They have shifted the field from
+application-specific model pipelines to a single model that is adapted to a
+wide range of tasks. Autoregressive PLMs like GPT-3 or PaLM, alongside
+techniques like few-shot learning, have additionally shifted the output
+modality to generation instead of classification or regression. Despite their
+ubiquitous use, the generation quality of language models is rarely evaluated
+when these models are introduced. Additionally, it is unclear how existing
+generation tasks--while they can be used to compare systems at a high
+level--relate to the real world use cases for which people have been adopting
+them. In this work, we discuss how to adapt existing application-specific
+generation benchmarks to PLMs and provide an in-depth, empirical study of the
+limitations and capabilities of PLMs in natural language generation tasks along
+dimensions such as scale, architecture, input and output language. Our results
+show that PLMs differ in their applicability to different data regimes and
+their generalization to multiple languages and inform which PLMs to use for a
+given generation task setup. We share best practices to be taken into
+consideration when benchmarking generation capabilities during the development
+of upcoming PLMs.
+"
+On Conditional and Compositional Language Model Differentiable Prompting,Jonathan Pilault,http://arxiv.org/pdf/2307.01446v1.pdf,2023-07-04,"['cs.cl', 'cs.lg']",2307.01446v1.pdf,"  Prompts have been shown to be an effective method to adapt a frozen
+Pretrained Language Model (PLM) to perform well on downstream tasks. Prompts
+can be represented by a human-engineered word sequence or by a learned
+continuous embedding. In this work, we investigate conditional and
+compositional differentiable prompting. We propose a new model, Prompt
+Production System (PRopS), which learns to transform task instructions or input
+metadata, into continuous prompts that elicit task-specific outputs from the
+PLM. Our model uses a modular network structure based on our neural formulation
+of Production Systems, which allows the model to learn discrete rules -- neural
+functions that learn to specialize in transforming particular prompt input
+patterns, making it suitable for compositional transfer learning and few-shot
+learning. We present extensive empirical and theoretical analysis and show that
+PRopS consistently surpasses other PLM adaptation techniques, and often
+improves upon fully fine-tuned models, on compositional generalization tasks,
+controllable summarization and multilingual translation, while needing fewer
+trainable parameters.
+"
+Diverse Retrieval-Augmented In-Context Learning for Dialogue State  Tracking,Brendan King,http://arxiv.org/pdf/2307.01453v1.pdf,2023-07-04,['cs.cl'],2307.01453v1.pdf,"  There has been significant interest in zero and few-shot learning for
+dialogue state tracking (DST) due to the high cost of collecting and annotating
+task-oriented dialogues. Recent work has demonstrated that in-context learning
+requires very little data and zero parameter updates, and even outperforms
+trained methods in the few-shot setting (Hu et al. 2022). We propose RefPyDST,
+which advances the state of the art with three advancements to in-context
+learning for DST. First, we formulate DST as a Python programming task,
+explicitly modeling language coreference as variable reference in Python.
+Second, since in-context learning depends highly on the context examples, we
+propose a method to retrieve a diverse set of relevant examples to improve
+performance. Finally, we introduce a novel re-weighting method during decoding
+that takes into account probabilities of competing surface forms, and produces
+a more accurate dialogue state prediction. We evaluate our approach using
+MultiWOZ and achieve state-of-the-art multi-domain joint-goal accuracy in zero
+and few-shot settings.
+"
+Generating Efficient Training Data via LLM-based Attribute Manipulation,Letian Peng,http://arxiv.org/pdf/2307.07099v1.pdf,2023-07-14,['cs.cl'],2307.07099v1.pdf,"  In this paper, we propose a novel method, Chain-of-Thoughts Attribute
+Manipulation (CoTAM), to guide few-shot learning by carefully crafted data from
+Large Language Models (LLMs). The main idea is to create data with changes only
+in the attribute targeted by the task. Inspired by facial attribute
+manipulation, our approach generates label-switched data by leveraging LLMs to
+manipulate task-specific attributes and reconstruct new sentences in a
+controlled manner. Instead of conventional latent representation controlling,
+we implement chain-of-thoughts decomposition and reconstruction to adapt the
+procedure to LLMs. Extensive results on text classification and other tasks
+verify the advantage of CoTAM over other LLM-based text generation methods with
+the same number of training examples. Analysis visualizes the attribute
+manipulation effectiveness of CoTAM and presents the potential of LLM-guided
+learning with even less supervision.
+"
+Overthinking the Truth: Understanding how Language Models Process False  Demonstrations,Danny Halawi,http://arxiv.org/pdf/2307.09476v1.pdf,2023-07-18,"['cs.lg', 'cs.ai', 'cs.cl']",2307.09476v1.pdf,"  Modern language models can imitate complex patterns through few-shot
+learning, enabling them to complete challenging tasks without fine-tuning.
+However, imitation can also lead models to reproduce inaccuracies or harmful
+content if present in the context. We study harmful imitation through the lens
+of a model's internal representations, and identify two related phenomena:
+overthinking and false induction heads. The first phenomenon, overthinking,
+appears when we decode predictions from intermediate layers, given correct vs.
+incorrect few-shot demonstrations. At early layers, both demonstrations induce
+similar model behavior, but the behavior diverges sharply at some ""critical
+layer"", after which the accuracy given incorrect demonstrations progressively
+decreases. The second phenomenon, false induction heads, are a possible
+mechanistic cause of overthinking: these are heads in late layers that attend
+to and copy false information from previous demonstrations, and whose ablation
+reduces overthinking. Beyond scientific understanding, our results suggest that
+studying intermediate model computations could be a promising avenue for
+understanding and guarding against harmful model behaviors.
+"
+Does Correction Remain A Problem For Large Language Models?,Xiaowu Zhang,http://arxiv.org/pdf/2308.01776v2.pdf,2023-08-03,['cs.cl'],2308.01776v2.pdf,"  As large language models, such as GPT, continue to advance the capabilities
+of natural language processing (NLP), the question arises: does the problem of
+correction still persist? This paper investigates the role of correction in the
+context of large language models by conducting two experiments. The first
+experiment focuses on correction as a standalone task, employing few-shot
+learning techniques with GPT-like models for error correction. The second
+experiment explores the notion of correction as a preparatory task for other
+NLP tasks, examining whether large language models can tolerate and perform
+adequately on texts containing certain levels of noise or errors. By addressing
+these experiments, we aim to shed light on the significance of correction in
+the era of large language models and its implications for various NLP
+applications.
+"
+Thespian: Multi-Character Text Role-Playing Game Agents,Christopher Cui,http://arxiv.org/pdf/2308.01872v1.pdf,2023-08-03,"['cs.ai', 'cs.cl']",2308.01872v1.pdf,"  Text-adventure games and text role-playing games are grand challenges for
+reinforcement learning game playing agents. Text role-playing games are
+open-ended environments where an agent must faithfully play a particular
+character. We consider the distinction between characters and actors, where an
+actor agent has the ability to play multiple characters. We present a framework
+we call a thespian agent that can learn to emulate multiple characters along
+with a soft prompt that can be used to direct it as to which character to play
+at any time. We further describe an attention mechanism that allows the agent
+to learn new characters that are based on previously learned characters in a
+few-shot fashion. We show that our agent outperforms the state of the art agent
+framework in multi-character learning and few-shot learning.
+"
+Meta-learning in healthcare: A survey,Alireza Rafiei,http://arxiv.org/pdf/2308.02877v1.pdf,2023-08-05,"['cs.lg', 'cs.ai']",2308.02877v1.pdf,"  As a subset of machine learning, meta-learning, or learning to learn, aims at
+improving the model's capabilities by employing prior knowledge and experience.
+A meta-learning paradigm can appropriately tackle the conventional challenges
+of traditional learning approaches, such as insufficient number of samples,
+domain shifts, and generalization. These unique characteristics position
+meta-learning as a suitable choice for developing influential solutions in
+various healthcare contexts, where the available data is often insufficient,
+and the data collection methodologies are different. This survey discusses
+meta-learning broad applications in the healthcare domain to provide insight
+into how and where it can address critical healthcare challenges. We first
+describe the theoretical foundations and pivotal methods of meta-learning. We
+then divide the employed meta-learning approaches in the healthcare domain into
+two main categories of multi/single-task learning and many/few-shot learning
+and survey the studies. Finally, we highlight the current challenges in
+meta-learning research, discuss the potential solutions and provide future
+perspectives on meta-learning in healthcare.
+"
+AutoConv: Automatically Generating Information-seeking Conversations  with Large Language Models,Siheng Li,http://arxiv.org/pdf/2308.06507v1.pdf,2023-08-12,['cs.cl'],2308.06507v1.pdf,"  Information-seeking conversation, which aims to help users gather information
+through conversation, has achieved great progress in recent years. However, the
+research is still stymied by the scarcity of training data. To alleviate this
+problem, we propose AutoConv for synthetic conversation generation, which takes
+advantage of the few-shot learning ability and generation capacity of large
+language models (LLM). Specifically, we formulate the conversation generation
+problem as a language modeling task, then finetune an LLM with a few human
+conversations to capture the characteristics of the information-seeking process
+and use it for generating synthetic conversations with high quality.
+Experimental results on two frequently-used datasets verify that AutoConv has
+substantial improvements over strong baselines and alleviates the dependence on
+human annotation. In addition, we also provide several analysis studies to
+promote future research.
+"
+Few-shot Class-incremental Learning: A Survey,Jinghua Zhang,http://arxiv.org/pdf/2308.06764v1.pdf,2023-08-13,"['cs.lg', 'cs.ai']",2308.06764v1.pdf,"  Few-shot Class-Incremental Learning (FSCIL) presents a unique challenge in
+machine learning, as it necessitates the continuous learning of new classes
+from sparse labeled training samples without forgetting previous knowledge.
+While this field has seen recent progress, it remains an active area of
+exploration. This paper aims to provide a comprehensive and systematic review
+of FSCIL. In our in-depth examination, we delve into various facets of FSCIL,
+encompassing the problem definition, the discussion of primary challenges of
+unreliable empirical risk minimization and the stability-plasticity dilemma,
+general schemes, and relevant problems of incremental learning and few-shot
+learning. Besides, we offer an overview of benchmark datasets and evaluation
+metrics. Furthermore, we introduce the classification methods in FSCIL from
+data-based, structure-based, and optimization-based approaches and the object
+detection methods in FSCIL from anchor-free and anchor-based approaches. Beyond
+these, we illuminate several promising research directions within FSCIL that
+merit further investigation.
+"
+Distilled Feature Fields Enable Few-Shot Language-Guided Manipulation,William Shen,http://arxiv.org/pdf/2308.07931v1.pdf,2023-07-27,"['cs.cv', 'cs.ai', 'cs.cl', 'cs.lg', 'cs.ro']",2308.07931v1.pdf,"  Self-supervised and language-supervised image models contain rich knowledge
+of the world that is important for generalization. Many robotic tasks, however,
+require a detailed understanding of 3D geometry, which is often lacking in 2D
+image features. This work bridges this 2D-to-3D gap for robotic manipulation by
+leveraging distilled feature fields to combine accurate 3D geometry with rich
+semantics from 2D foundation models. We present a few-shot learning method for
+6-DOF grasping and placing that harnesses these strong spatial and semantic
+priors to achieve in-the-wild generalization to unseen objects. Using features
+distilled from a vision-language model, CLIP, we present a way to designate
+novel objects for manipulation via free-text natural language, and demonstrate
+its ability to generalize to unseen expressions and novel categories of
+objects.
+"
+Refashioning Emotion Recognition Modelling: The Advent of Generalised  Large Models,Zixing Zhang,http://arxiv.org/pdf/2308.11578v1.pdf,2023-08-21,"['cs.cl', 'cs.ai', 'cs.lg']",2308.11578v1.pdf,"  After the inception of emotion recognition or affective computing, it has
+increasingly become an active research topic due to its broad applications.
+Over the past couple of decades, emotion recognition models have gradually
+migrated from statistically shallow models to neural network-based deep models,
+which can significantly boost the performance of emotion recognition models and
+consistently achieve the best results on different benchmarks. Therefore, in
+recent years, deep models have always been considered the first option for
+emotion recognition. However, the debut of large language models (LLMs), such
+as ChatGPT, has remarkably astonished the world due to their emerged
+capabilities of zero/few-shot learning, in-context learning, chain-of-thought,
+and others that are never shown in previous deep models. In the present paper,
+we comprehensively investigate how the LLMs perform in emotion recognition in
+terms of diverse aspects, including in-context learning, few-short learning,
+accuracy, generalisation, and explanation. Moreover, we offer some insights and
+pose other potential challenges, hoping to ignite broader discussions about
+enhancing emotion recognition in the new era of advanced and generalised large
+models.
+"
+Gpachov at CheckThat! 2023: A Diverse Multi-Approach Ensemble for  Subjectivity Detection in News Articles,Georgi Pachov,http://arxiv.org/pdf/2309.06844v1.pdf,2023-09-13,"['cs.cl', 'cs.ai', 'cs.mm']",2309.06844v1.pdf,"  The wide-spread use of social networks has given rise to subjective,
+misleading, and even false information on the Internet. Thus, subjectivity
+detection can play an important role in ensuring the objectiveness and the
+quality of a piece of information. This paper presents the solution built by
+the Gpachov team for the CLEF-2023 CheckThat! lab Task~2 on subjectivity
+detection. Three different research directions are explored. The first one is
+based on fine-tuning a sentence embeddings encoder model and dimensionality
+reduction. The second one explores a sample-efficient few-shot learning model.
+The third one evaluates fine-tuning a multilingual transformer on an altered
+dataset, using data from multiple languages. Finally, the three approaches are
+combined in a simple majority voting ensemble, resulting in 0.77 macro F1 on
+the test set and achieving 2nd place on the English subtask.
+"
+"An Empathy-Based Sandbox Approach to Bridge Attitudes, Goals, Knowledge,  and Behaviors in the Privacy Paradox",Chaoran Chen,http://arxiv.org/pdf/2309.14510v1.pdf,2023-09-25,['cs.hc'],2309.14510v1.pdf,"  The ""privacy paradox"" describes the discrepancy between users' privacy
+attitudes and their actual behaviors. Mitigating this discrepancy requires
+solutions that account for both system opaqueness and users' hesitations in
+testing different privacy settings due to fears of unintended data exposure. We
+introduce an empathy-based approach that allows users to experience how privacy
+behaviors may alter system outcomes in a risk-free sandbox environment from the
+perspective of artificially generated personas. To generate realistic personas,
+we introduce a novel pipeline that augments the outputs of large language
+models using few-shot learning, contextualization, and chain of thoughts. Our
+empirical studies demonstrated the adequate quality of generated personas and
+highlighted the changes in privacy-related applications (e.g., online
+advertising) caused by different personas. Furthermore, users demonstrated
+cognitive and emotional empathy towards the personas when interacting with our
+sandbox. We offered design implications for downstream applications in
+improving user privacy literacy and promoting behavior changes.
+"
+Boosting In-Context Learning with Factual Knowledge,Jianing Wang,http://arxiv.org/pdf/2309.14771v1.pdf,2023-09-26,"['cs.cl', 'cs.ai']",2309.14771v1.pdf,"  In-Context Learning (ICL) over Large language models (LLMs) aims at solving
+previously unseen tasks by conditioning on a few training examples, eliminating
+the need for parameter updates and achieving competitive performance. In this
+paper, we demonstrate that factual knowledge is imperative for the performance
+of ICL in three core facets, i.e., the inherent knowledge learned in LLMs, the
+factual knowledge derived from the selected in-context examples, and the
+knowledge biases in LLMs for output generation. To unleash the power of LLMs in
+few-shot learning scenarios, we introduce a novel Knowledgeable In-Context
+Tuning (KICT) framework to further improve the performance of ICL: 1) injecting
+factual knowledge to LLMs during continual self-supervised pre-training, 2)
+judiciously selecting the examples with high knowledge relevance, and 3)
+calibrating the prediction results based on prior knowledge. We evaluate the
+proposed approaches on auto-regressive LLMs (e.g., GPT-style models) over
+multiple text classification and question answering tasks. Experimental results
+demonstrate that KICT substantially outperforms strong baselines, and improves
+by more than 13% and 7% of accuracy on text classification and question
+answering tasks, respectively.
+"
+Small Visual Language Models can also be Open-Ended Few-Shot Learners,Mohammad Mahdi Derakhshani,http://arxiv.org/pdf/2310.00500v1.pdf,2023-09-30,['cs.cv'],2310.00500v1.pdf,"  We present Self-Context Adaptation (SeCAt), a self-supervised approach that
+unlocks open-ended few-shot abilities of small visual language models. Our
+proposed adaptation algorithm explicitly learns from symbolic, yet
+self-supervised training tasks. Specifically, our approach imitates image
+captions in a self-supervised way based on clustering a large pool of images
+followed by assigning semantically-unrelated names to clusters. By doing so, we
+construct the `self-context', a training signal consisting of interleaved
+sequences of image and pseudo-caption pairs and a query image for which the
+model is trained to produce the right pseudo-caption. We demonstrate the
+performance and flexibility of SeCAt on several multimodal few-shot datasets,
+spanning various granularities. By using models with approximately 1B
+parameters we outperform the few-shot abilities of much larger models, such as
+Frozen and FROMAGe. SeCAt opens new possibilities for research in open-ended
+few-shot learning that otherwise requires access to large or proprietary
+models.
+"
+Injecting a Structural Inductive Bias into a Seq2Seq Model by Simulation,Matthias Lindemann,http://arxiv.org/pdf/2310.00796v1.pdf,2023-10-01,['cs.cl'],2310.00796v1.pdf,"  Strong inductive biases enable learning from little data and help
+generalization outside of the training distribution. Popular neural
+architectures such as Transformers lack strong structural inductive biases for
+seq2seq NLP tasks on their own. Consequently, they struggle with systematic
+generalization beyond the training distribution, e.g. with extrapolating to
+longer inputs, even when pre-trained on large amounts of text. We show how a
+structural inductive bias can be injected into a seq2seq model by pre-training
+it to simulate structural transformations on synthetic data. Specifically, we
+inject an inductive bias towards Finite State Transducers (FSTs) into a
+Transformer by pre-training it to simulate FSTs given their descriptions. Our
+experiments show that our method imparts the desired inductive bias, resulting
+in improved systematic generalization and better few-shot learning for FST-like
+tasks.
+"
+TRAM: Benchmarking Temporal Reasoning for Large Language Models,Yuqing Wang,http://arxiv.org/pdf/2310.00835v2.pdf,2023-10-02,['cs.cl'],2310.00835v2.pdf,"  Reasoning about time is essential for understanding the nuances of events
+described in natural language. Previous research on this topic has been limited
+in scope, characterized by a lack of standardized benchmarks that would allow
+for consistent evaluations across different studies. In this paper, we
+introduce TRAM, a temporal reasoning benchmark composed of ten datasets,
+encompassing various temporal aspects of events such as order, arithmetic,
+frequency, and duration, designed to facilitate a comprehensive evaluation of
+the temporal reasoning capabilities of large language models (LLMs). We conduct
+an extensive evaluation using popular LLMs, such as GPT-4 and Llama2, in both
+zero-shot and few-shot learning scenarios. Additionally, we employ BERT-based
+models to establish the baseline evaluations. Our findings indicate that these
+models still trail human performance in temporal reasoning tasks. It is our
+aspiration that TRAM will spur further progress in enhancing the temporal
+reasoning abilities of LLMs.
+"
+Procedural Text Mining with Large Language Models,Anisa Rula,http://arxiv.org/pdf/2310.03376v1.pdf,2023-10-05,"['cs.cl', 'cs.ai', 'cs.it', 'math.it']",2310.03376v1.pdf,"  Recent advancements in the field of Natural Language Processing, particularly
+the development of large-scale language models that are pretrained on vast
+amounts of knowledge, are creating novel opportunities within the realm of
+Knowledge Engineering. In this paper, we investigate the usage of large
+language models (LLMs) in both zero-shot and in-context learning settings to
+tackle the problem of extracting procedures from unstructured PDF text in an
+incremental question-answering fashion. In particular, we leverage the current
+state-of-the-art GPT-4 (Generative Pre-trained Transformer 4) model,
+accompanied by two variations of in-context learning that involve an ontology
+with definitions of procedures and steps and a limited number of samples of
+few-shot learning. The findings highlight both the promise of this approach and
+the value of the in-context learning customisations. These modifications have
+the potential to significantly address the challenge of obtaining sufficient
+training data, a hurdle often encountered in deep learning-based Natural
+Language Processing techniques for procedure extraction.
+"
+PrototypeFormer: Learning to Explore Prototype Relationships for  Few-shot Image Classification,Feihong He,http://arxiv.org/pdf/2310.03517v1.pdf,2023-10-05,['cs.cv'],2310.03517v1.pdf,"  Few-shot image classification has received considerable attention for
+addressing the challenge of poor classification performance with limited
+samples in novel classes. However, numerous studies have employed sophisticated
+learning strategies and diversified feature extraction methods to address this
+issue. In this paper, we propose our method called PrototypeFormer, which aims
+to significantly advance traditional few-shot image classification approaches
+by exploring prototype relationships. Specifically, we utilize a transformer
+architecture to build a prototype extraction module, aiming to extract class
+representations that are more discriminative for few-shot classification.
+Additionally, during the model training process, we propose a contrastive
+learning-based optimization approach to optimize prototype features in few-shot
+learning scenarios. Despite its simplicity, the method performs remarkably
+well, with no bells and whistles. We have experimented with our approach on
+several popular few-shot image classification benchmark datasets, which shows
+that our method outperforms all current state-of-the-art methods. In
+particular, our method achieves 97.07% and 90.88% on 5-way 5-shot and 5-way
+1-shot tasks of miniImageNet, which surpasses the state-of-the-art results with
+accuracy of 7.27% and 8.72%, respectively. The code will be released later.
+"
+A Holistic Evaluation of Piano Sound Quality,Monan Zhou,http://arxiv.org/pdf/2310.04722v1.pdf,2023-10-07,"['cs.sd', 'cs.ai', 'eess.as']",2310.04722v1.pdf,"  This paper aims to develop a holistic evaluation method for piano sound
+quality to assist in purchasing decisions. Unlike previous studies that focused
+on the effect of piano performance techniques on sound quality, this study
+evaluates the inherent sound quality of different pianos. To derive quality
+evaluation systems, the study uses subjective questionnaires based on a piano
+sound quality dataset. The method selects the optimal piano classification
+models by comparing the fine-tuning results of different pre-training models of
+Convolutional Neural Networks (CNN). To improve the interpretability of the
+models, the study applies Equivalent Rectangular Bandwidth (ERB) analysis. The
+results reveal that musically trained individuals are better able to
+distinguish between the sound quality differences of different pianos. The best
+fine-tuned CNN pre-trained backbone achieves a high accuracy of 98.3\% as the
+piano classifier. However, the dataset is limited, and the audio is sliced to
+increase its quantity, resulting in a lack of diversity and balance, so we use
+focal loss to reduce the impact of data imbalance. To optimize the method, the
+dataset will be expanded, or few-shot learning techniques will be employed in
+future research.
+"
+Argumentative Stance Prediction: An Exploratory Study on Multimodality  and Few-Shot Learning,Arushi Sharma,http://arxiv.org/pdf/2310.07093v1.pdf,2023-10-11,['cs.cl'],2310.07093v1.pdf,"  To advance argumentative stance prediction as a multimodal problem, the First
+Shared Task in Multimodal Argument Mining hosted stance prediction in crucial
+social topics of gun control and abortion. Our exploratory study attempts to
+evaluate the necessity of images for stance prediction in tweets and compare
+out-of-the-box text-based large-language models (LLM) in few-shot settings
+against fine-tuned unimodal and multimodal models. Our work suggests an
+ensemble of fine-tuned text-based language models (0.817 F1-score) outperforms
+both the multimodal (0.677 F1-score) and text-based few-shot prediction using a
+recent state-of-the-art LLM (0.550 F1-score). In addition to the differences in
+performance, our findings suggest that the multimodal models tend to perform
+better when image content is summarized as natural language over their native
+pixel structure and, using in-context examples improves few-shot performance of
+LLMs.
+"
+LLM-augmented Preference Learning from Natural Language,Inwon Kang,http://arxiv.org/pdf/2310.08523v1.pdf,2023-10-12,['cs.cl'],2310.08523v1.pdf,"  Finding preferences expressed in natural language is an important but
+challenging task. State-of-the-art(SotA) methods leverage transformer-based
+models such as BERT, RoBERTa, etc. and graph neural architectures such as graph
+attention networks. Since Large Language Models (LLMs) are equipped to deal
+with larger context lengths and have much larger model sizes than the
+transformer-based model, we investigate their ability to classify comparative
+text directly. This work aims to serve as a first step towards using LLMs for
+the CPC task. We design and conduct a set of experiments that format the
+classification task into an input prompt for the LLM and a methodology to get a
+fixed-format response that can be automatically evaluated. Comparing
+performances with existing methods, we see that pre-trained LLMs are able to
+outperform the previous SotA models with no fine-tuning involved. Our results
+show that the LLMs can consistently outperform the SotA when the target text is
+large -- i.e. composed of multiple sentences --, and are still comparable to
+the SotA performance in shorter text. We also find that few-shot learning
+yields better performance than zero-shot learning.
+"
+In-Context Learning for Few-Shot Molecular Property Prediction,Christopher Fifty,http://arxiv.org/pdf/2310.08863v1.pdf,2023-10-13,['cs.lg'],2310.08863v1.pdf,"  In-context learning has become an important approach for few-shot learning in
+Large Language Models because of its ability to rapidly adapt to new tasks
+without fine-tuning model parameters. However, it is restricted to applications
+in natural language and inapplicable to other domains. In this paper, we adapt
+the concepts underpinning in-context learning to develop a new algorithm for
+few-shot molecular property prediction. Our approach learns to predict
+molecular properties from a context of (molecule, property measurement) pairs
+and rapidly adapts to new properties without fine-tuning. On the FS-Mol and
+BACE molecular property prediction benchmarks, we find this method surpasses
+the performance of recent meta-learning algorithms at small support sizes and
+is competitive with the best methods at large support sizes.
+"
+In-Context Few-Shot Relation Extraction via Pre-Trained Language Models,Yilmazcan Ozyurt,http://arxiv.org/pdf/2310.11085v1.pdf,2023-10-17,"['cs.cl', 'cs.ai', 'cs.lg']",2310.11085v1.pdf,"  Relation extraction aims at inferring structured human knowledge from textual
+documents. State-of-the-art methods based on language models commonly have two
+limitations: (1) they require named entities to be either given as input or
+infer them, which introduces additional noise, and (2) they require human
+annotations of documents. As a remedy, we present a novel framework for
+in-context few-shot relation extraction via pre-trained language models. To the
+best of our knowledge, we are the first to reformulate the relation extraction
+task as a tailored in-context few-shot learning paradigm. Thereby, we achieve
+crucial benefits in that we eliminate the need for both named entity
+recognition and human annotation of documents. Unlike existing methods based on
+fine-tuning, our framework is flexible in that it can be easily updated for a
+new set of relations without re-training. We evaluate our framework using
+DocRED, the largest publicly available dataset for document-level relation
+extraction, and demonstrate that our framework achieves state-of-the-art
+performance. Finally, our framework allows us to identify missing annotations,
+and we thus show that our framework actually performs much better than the
+original labels from the development set of DocRED.
+"
+Group Preference Optimization: Few-Shot Alignment of Large Language  Models,Siyan Zhao,http://arxiv.org/pdf/2310.11523v1.pdf,2023-10-17,"['cs.lg', 'cs.ai', 'cs.cl']",2310.11523v1.pdf,"  Many applications of large language models (LLMs), ranging from chatbots to
+creative writing, require nuanced subjective judgments that can differ
+significantly across different groups. Existing alignment algorithms can be
+expensive to align for each group, requiring prohibitive amounts of
+group-specific preference data and computation for real-world use cases. We
+introduce Group Preference Optimization (GPO), an alignment framework that
+steers language models to preferences of individual groups in a few-shot
+manner. In GPO, we augment the base LLM with an independent transformer module
+trained to predict the preferences of a group for the LLM generations. For
+few-shot learning, we parameterize this module as an in-context autoregressive
+transformer and train it via meta-learning on several groups. We empirically
+validate the efficacy of GPO through rigorous evaluations using LLMs with
+varied sizes on three human opinion adaptation tasks. These tasks involve
+adapting to the preferences of US demographic groups, global countries, and
+individual users. Our results demonstrate that GPO not only aligns models more
+accurately but also requires fewer group-specific preferences, and less
+training and inference computing resources, outperforming existing strategies
+such as in-context steering and fine-tuning methods.
+"
+CLARA: Multilingual Contrastive Learning for Audio Representation  Acquisition,Kari A Noriy,http://arxiv.org/pdf/2310.11830v2.pdf,2023-10-18,"['cs.sd', 'cs.lg', 'cs.mm', 'eess.as']",2310.11830v2.pdf,"  Multilingual speech processing requires understanding emotions, a task made
+difficult by limited labelled data. CLARA, minimizes reliance on labelled data,
+enhancing generalization across languages. It excels at fostering shared
+representations, aiding cross-lingual transfer of speech and emotions, even
+with little data. Our approach adeptly captures emotional nuances in speech,
+overcoming subjective assessment issues. Using a large multilingual audio
+corpus and self-supervised learning, CLARA develops speech representations
+enriched with emotions, advancing emotion-aware multilingual speech processing.
+  Our method expands the data range using data augmentation, textual embedding
+for visual understanding, and transfers knowledge from high- to low-resource
+languages. CLARA demonstrates excellent performance in emotion recognition,
+language comprehension, and audio benchmarks, excelling in zero-shot and
+few-shot learning. It adapts to low-resource languages, marking progress in
+multilingual speech representation learning.
+"
+A Tale of Pronouns: Interpretability Informs Gender Bias Mitigation for  Fairer Instruction-Tuned Machine Translation,Giuseppe Attanasio,http://arxiv.org/pdf/2310.12127v2.pdf,2023-10-18,"['cs.cl', 'cs.lg']",2310.12127v2.pdf,"  Recent instruction fine-tuned models can solve multiple NLP tasks when
+prompted to do so, with machine translation (MT) being a prominent use case.
+However, current research often focuses on standard performance benchmarks,
+leaving compelling fairness and ethical considerations behind. In MT, this
+might lead to misgendered translations, resulting, among other harms, in the
+perpetuation of stereotypes and prejudices. In this work, we address this gap
+by investigating whether and to what extent such models exhibit gender bias in
+machine translation and how we can mitigate it. Concretely, we compute
+established gender bias metrics on the WinoMT corpus from English to German and
+Spanish. We discover that IFT models default to male-inflected translations,
+even disregarding female occupational stereotypes. Next, using interpretability
+methods, we unveil that models systematically overlook the pronoun indicating
+the gender of a target occupation in misgendered translations. Finally, based
+on this finding, we propose an easy-to-implement and effective bias mitigation
+solution based on few-shot learning that leads to significantly fairer
+translations.
+"
+An Exploration of In-Context Learning for Speech Language Model,Ming-Hao Hsu,http://arxiv.org/pdf/2310.12477v1.pdf,2023-10-19,"['eess.as', 'cs.ai', 'cs.cl']",2310.12477v1.pdf,"  Ever since the development of GPT-3 in the natural language processing (NLP)
+field, in-context learning (ICL) has played an important role in utilizing
+large language models (LLMs). By presenting the LM utterance-label
+demonstrations at the input, the LM can accomplish few-shot learning without
+relying on gradient descent or requiring explicit modification of its
+parameters. This enables the LM to learn and adapt in a black-box manner.
+Despite the success of ICL in NLP, little work is exploring the possibility of
+ICL in speech processing. This study proposes the first exploration of ICL with
+a speech LM without text supervision. We first show that the current speech LM
+does not have the ICL capability. With the proposed warmup training, the speech
+LM can, therefore, perform ICL on unseen tasks. In this work, we verify the
+feasibility of ICL for speech LM on speech classification tasks.
+"
+Large Language Models are biased to overestimate profoundness,Eugenio Herrera-Berg,http://arxiv.org/pdf/2310.14422v1.pdf,2023-10-22,['cs.cl'],2310.14422v1.pdf,"  Recent advancements in natural language processing by large language models
+(LLMs), such as GPT-4, have been suggested to approach Artificial General
+Intelligence. And yet, it is still under dispute whether LLMs possess similar
+reasoning abilities to humans. This study evaluates GPT-4 and various other
+LLMs in judging the profoundness of mundane, motivational, and pseudo-profound
+statements. We found a significant statement-to-statement correlation between
+the LLMs and humans, irrespective of the type of statements and the prompting
+technique used. However, LLMs systematically overestimate the profoundness of
+nonsensical statements, with the exception of Tk-instruct, which uniquely
+underestimates the profoundness of statements. Only few-shot learning prompts,
+as opposed to chain-of-thought prompting, draw LLMs ratings closer to humans.
+Furthermore, this work provides insights into the potential biases induced by
+Reinforcement Learning from Human Feedback (RLHF), inducing an increase in the
+bias to overestimate the profoundness of statements.
+"
+Improving Few-shot Generalization of Safety Classifiers via Data  Augmented Parameter-Efficient Fine-Tuning,Ananth Balashankar,http://arxiv.org/pdf/2310.16959v1.pdf,2023-10-25,['cs.lg'],2310.16959v1.pdf,"  As large language models (LLMs) are widely adopted, new safety issues and
+policies emerge, to which existing safety classifiers do not generalize well.
+If we have only observed a few examples of violations of a new safety rule, how
+can we build a classifier to detect violations? In this paper, we study the
+novel setting of domain-generalized few-shot learning for LLM-based text safety
+classifiers. Unlike prior few-shot work, these new safety issues can be hard to
+uncover and we do not get to choose the few examples. We demonstrate that
+existing few-shot techniques do not perform well in this setting, and rather we
+propose to do parameter-efficient fine-tuning (PEFT) combined with augmenting
+training data based on similar examples in prior existing rules. We empirically
+show that our approach of similarity-based data-augmentation + prompt-tuning
+(DAPT) consistently outperforms baselines that either do not rely on data
+augmentation or on PEFT by 7-17% F1 score in the Social Chemistry moral
+judgement and 9-13% AUC in the Toxicity detection tasks, even when the new rule
+is loosely correlated with existing ones.
+"
+Retrofitting Light-weight Language Models for Emotions using Supervised  Contrastive Learning,Sapan Shah,http://arxiv.org/pdf/2310.18930v1.pdf,2023-10-29,['cs.cl'],2310.18930v1.pdf,"  We present a novel retrofitting method to induce emotion aspects into
+pre-trained language models (PLMs) such as BERT and RoBERTa. Our method updates
+pre-trained network weights using contrastive learning so that the text
+fragments exhibiting similar emotions are encoded nearby in the representation
+space, and the fragments with different emotion content are pushed apart. While
+doing so, it also ensures that the linguistic knowledge already present in PLMs
+is not inadvertently perturbed. The language models retrofitted by our method,
+i.e., BERTEmo and RoBERTaEmo, produce emotion-aware text representations, as
+evaluated through different clustering and retrieval metrics. For the
+downstream tasks on sentiment analysis and sarcasm detection, they perform
+better than their pre-trained counterparts (about 1% improvement in F1-score)
+and other existing approaches. Additionally, a more significant boost in
+performance is observed for the retrofitted models over pre-trained ones in
+few-shot learning setting.
+"
+Nexus at ArAIEval Shared Task: Fine-Tuning Arabic Language Models for  Propaganda and Disinformation Detection,Yunze Xiao,http://arxiv.org/pdf/2311.03184v1.pdf,2023-11-06,"['cs.cl', 'cs.ai', 'cs.si', '68t50', 'f.2.2; i.2.7']",2311.03184v1.pdf,"  The spread of disinformation and propagandistic content poses a threat to
+societal harmony, undermining informed decision-making and trust in reliable
+sources. Online platforms often serve as breeding grounds for such content, and
+malicious actors exploit the vulnerabilities of audiences to shape public
+opinion. Although there have been research efforts aimed at the automatic
+identification of disinformation and propaganda in social media content, there
+remain challenges in terms of performance. The ArAIEval shared task aims to
+further research on these particular issues within the context of the Arabic
+language. In this paper, we discuss our participation in these shared tasks. We
+competed in subtasks 1A and 2A, where our submitted system secured positions
+9th and 10th, respectively. Our experiments consist of fine-tuning transformer
+models and using zero- and few-shot learning with GPT-4.
+"
+Multilingual Mathematical Autoformalization,Albert Q. Jiang,http://arxiv.org/pdf/2311.03755v1.pdf,2023-11-07,"['cs.cl', 'cs.lg']",2311.03755v1.pdf,"  Autoformalization is the task of translating natural language materials into
+machine-verifiable formalisations. Progress in autoformalization research is
+hindered by the lack of a sizeable dataset consisting of informal-formal pairs
+expressing the same essence. Existing methods tend to circumvent this challenge
+by manually curating small corpora or using few-shot learning with large
+language models. But these methods suffer from data scarcity and formal
+language acquisition difficulty. In this work, we create $\texttt{MMA}$, a
+large, flexible, multilingual, and multi-domain dataset of informal-formal
+pairs, by using a language model to translate in the reverse direction, that
+is, from formal mathematical statements into corresponding informal ones.
+Experiments show that language models fine-tuned on $\texttt{MMA}$ produce
+$16-18\%$ of statements acceptable with minimal corrections on the
+$\texttt{miniF2F}$ and $\texttt{ProofNet}$ benchmarks, up from $0\%$ with the
+base model. We demonstrate that fine-tuning on multilingual formal data results
+in more capable autoformalization models even when deployed on monolingual
+tasks.
+"
+Data-Efficient Goal-Oriented Conversation with Dialogue Knowledge  Transfer Networks,Igor Shalyminov,http://arxiv.org/pdf/1910.01302v1.pdf,2019-10-03,"['cs.cl', 'i.2.7']",1910.01302v1.pdf,"  Goal-oriented dialogue systems are now being widely adopted in industry where
+it is of key importance to maintain a rapid prototyping cycle for new products
+and domains. Data-driven dialogue system development has to be adapted to meet
+this requirement --- therefore, reducing the amount of data and annotations
+necessary for training such systems is a central research problem.
+  In this paper, we present the Dialogue Knowledge Transfer Network (DiKTNet),
+a state-of-the-art approach to goal-oriented dialogue generation which only
+uses a few example dialogues (i.e. few-shot learning), none of which has to be
+annotated. We achieve this by performing a 2-stage training. Firstly, we
+perform unsupervised dialogue representation pre-training on a large source of
+goal-oriented dialogues in multiple domains, the MetaLWOz corpus. Secondly, at
+the transfer stage, we train DiKTNet using this representation together with 2
+other textual knowledge sources with different levels of generality: ELMo
+encoder and the main dataset's source domains.
+  Our main dataset is the Stanford Multi-Domain dialogue corpus. We evaluate
+our model on it in terms of BLEU and Entity F1 scores, and show that our
+approach significantly and consistently improves upon a series of baseline
+models as well as over the previous state-of-the-art dialogue generation model,
+ZSDG. The improvement upon the latter --- up to 10% in Entity F1 and the
+average of 3% in BLEU score --- is achieved using only the equivalent of 10% of
+ZSDG's in-domain training data.
+"
+Meta-Learning with Dynamic-Memory-Based Prototypical Network for  Few-Shot Event Detection,Shumin Deng,http://arxiv.org/pdf/1910.11621v2.pdf,2019-10-25,"['cs.cl', 'cs.ai', 'cs.ir', 'cs.lg']",1910.11621v2.pdf,"  Event detection (ED), a sub-task of event extraction, involves identifying
+triggers and categorizing event mentions. Existing methods primarily rely upon
+supervised learning and require large-scale labeled event datasets which are
+unfortunately not readily available in many real-life applications. In this
+paper, we consider and reformulate the ED task with limited labeled data as a
+Few-Shot Learning problem. We propose a Dynamic-Memory-Based Prototypical
+Network (DMB-PN), which exploits Dynamic Memory Network (DMN) to not only learn
+better prototypes for event types, but also produce more robust sentence
+encodings for event mentions. Differing from vanilla prototypical networks
+simply computing event prototypes by averaging, which only consume event
+mentions once, our model is more robust and is capable of distilling contextual
+information from event mentions for multiple times due to the multi-hop
+mechanism of DMNs. The experiments show that DMB-PN not only deals with sample
+scarcity better than a series of baseline models but also performs more
+robustly when the variety of event types is relatively large and the instance
+quantity is extremely small.
+"
+Spirit Distillation: Precise Real-time Semantic Segmentation of Road  Scenes with Insufficient Data,Zhiyuan Wu,http://arxiv.org/pdf/2103.13733v2.pdf,2021-03-25,"['cs.cv', 'cs.ai', 'cs.lg']",2103.13733v2.pdf,"  Semantic segmentation of road scenes is one of the key technologies for
+realizing autonomous driving scene perception, and the effectiveness of deep
+Convolutional Neural Networks(CNNs) for this task has been demonstrated.
+State-of-art CNNs for semantic segmentation suffer from excessive computations
+as well as large-scale training data requirement. Inspired by the ideas of
+Fine-tuning-based Transfer Learning (FTT) and feature-based knowledge
+distillation, we propose a new knowledge distillation method for cross-domain
+knowledge transference and efficient data-insufficient network training, named
+Spirit Distillation(SD), which allow the student network to mimic the teacher
+network to extract general features, so that a compact and accurate student
+network can be trained for real-time semantic segmentation of road scenes.
+Then, in order to further alleviate the trouble of insufficient data and
+improve the robustness of the student, an Enhanced Spirit Distillation (ESD)
+method is proposed, which commits to exploit a more comprehensive general
+features extraction capability by considering images from both the target and
+the proximity domains as input. To our knowledge, this paper is a pioneering
+work on the application of knowledge distillation to few-shot learning.
+Persuasive experiments conducted on Cityscapes semantic segmentation with the
+prior knowledge transferred from COCO2017 and KITTI demonstrate that our
+methods can train a better student network (mIOU and high-precision accuracy
+boost by 1.4% and 8.2% respectively, with 78.2% segmentation variance) with
+only 41.8% FLOPs (see Fig. 1).
+"
+AMP0: Species-Specific Prediction of Anti-microbial Peptides using Zero  and Few Shot Learning,Sadaf Gull,http://arxiv.org/pdf/1911.06106v1.pdf,2019-10-28,"['q-bio.bm', 'cs.lg', 'stat.ml']",1911.06106v1.pdf,"  The evolution of drug-resistant microbial species is one of the major
+challenges to global health. The development of new antimicrobial treatments
+such as antimicrobial peptides needs to be accelerated to combat this threat.
+However, the discovery of novel antimicrobial peptides is hampered by
+low-throughput biochemical assays. Computational techniques can be used for
+rapid screening of promising antimicrobial peptide candidates prior to testing
+in the wet lab. The vast majority of existing antimicrobial peptide predictors
+are non-targeted in nature, i.e., they can predict whether a given peptide
+sequence is antimicrobial, but they are unable to predict whether the sequence
+can target a particular microbial species. In this work, we have developed a
+targeted antimicrobial peptide activity predictor that can predict whether a
+peptide is effective against a given microbial species or not. This has been
+made possible through zero-shot and few-shot machine learning. The proposed
+predictor called AMP0 takes in the peptide amino acid sequence and any
+N/C-termini modifications together with the genomic sequence of a target
+microbial species to generate targeted predictions. It is important to note
+that the proposed method can generate predictions for species that are not part
+of its training set. The accuracy of predictions for novel test species can be
+further improved by providing a few example peptides for that species. Our
+computational cross-validation results show that the pro-posed scheme is
+particularly effective for targeted antimicrobial prediction in comparison to
+existing approaches and can be used for screening potential antimicrobial
+peptides in a targeted manner especially for cases in which the number of
+training examples is small. The webserver of the method is available at
+http://ampzero.pythonanywhere.com.
+"
+Brain-inspired global-local learning incorporated with neuromorphic  computing,Yujie Wu,http://arxiv.org/pdf/2006.03226v3.pdf,2020-06-05,"['cs.ne', 'cs.ai', 'q-bio.nc']",2006.03226v3.pdf,"  Two main routes of learning methods exist at present including error-driven
+global learning and neuroscience-oriented local learning. Integrating them into
+one network may provide complementary learning capabilities for versatile
+learning scenarios. At the same time, neuromorphic computing holds great
+promise, but still needs plenty of useful algorithms and algorithm-hardware
+co-designs for exploiting the advantages. Here, we report a neuromorphic hybrid
+learning model by introducing a brain-inspired meta-learning paradigm and a
+differentiable spiking model incorporating neuronal dynamics and synaptic
+plasticity. It can meta-learn local plasticity and receive top-down supervision
+information for multiscale synergic learning. We demonstrate the advantages of
+this model in multiple different tasks, including few-shot learning, continual
+learning, and fault-tolerance learning in neuromorphic vision sensors. It
+achieves significantly higher performance than single-learning methods, and
+shows promise in empowering neuromorphic applications revolution. We further
+implemented the hybrid model in the Tianjic neuromorphic platform by exploiting
+algorithm-hardware co-designs and proved that the model can fully utilize
+neuromorphic many-core architecture to develop hybrid computation paradigm.
+"
+Direct multimodal few-shot learning of speech and images,Leanne Nortje,http://arxiv.org/pdf/2012.05680v2.pdf,2020-12-10,"['cs.cl', 'cs.sd', 'eess.as']",2012.05680v2.pdf,"  We propose direct multimodal few-shot models that learn a shared embedding
+space of spoken words and images from only a few paired examples. Imagine an
+agent is shown an image along with a spoken word describing the object in the
+picture, e.g. pen, book and eraser. After observing a few paired examples of
+each class, the model is asked to identify the ""book"" in a set of unseen
+pictures. Previous work used a two-step indirect approach relying on learned
+unimodal representations: speech-speech and image-image comparisons are
+performed across the support set of given speech-image pairs. We propose two
+direct models which instead learn a single multimodal space where inputs from
+different modalities are directly comparable: a multimodal triplet network
+(MTriplet) and a multimodal correspondence autoencoder (MCAE). To train these
+direct models, we mine speech-image pairs: the support set is used to pair up
+unlabelled in-domain speech and images. In a speech-to-image digit matching
+task, direct models outperform indirect models, with the MTriplet achieving the
+best multimodal five-shot accuracy. We show that the improvements are due to
+the combination of unsupervised and transfer learning in the direct models, and
+the absence of two-step compounding errors.
+"
+What Makes Good In-Context Examples for GPT-$3$?,Jiachang Liu,http://arxiv.org/pdf/2101.06804v1.pdf,2021-01-17,['cs.cl'],2101.06804v1.pdf,"  GPT-$3$ has attracted lots of attention due to its superior performance
+across a wide range of NLP tasks, especially with its powerful and versatile
+in-context few-shot learning ability. Despite its success, we found that the
+empirical results of GPT-$3$ depend heavily on the choice of in-context
+examples. In this work, we investigate whether there are more effective
+strategies for judiciously selecting in-context examples (relative to random
+sampling) that better leverage GPT-$3$'s few-shot capabilities. Inspired by the
+recent success of leveraging a retrieval module to augment large-scale neural
+network models, we propose to retrieve examples that are semantically-similar
+to a test sample to formulate its corresponding prompt. Intuitively, the
+in-context examples selected with such a strategy may serve as more informative
+inputs to unleash GPT-$3$'s extensive knowledge. We evaluate the proposed
+approach on several natural language understanding and generation benchmarks,
+where the retrieval-based prompt selection approach consistently outperforms
+the random baseline. Moreover, it is observed that the sentence encoders
+fine-tuned on task-related datasets yield even more helpful retrieval results.
+Notably, significant gains are observed on tasks such as table-to-text
+generation (41.9% on the ToTTo dataset) and open-domain question answering
+(45.5% on the NQ dataset). We hope our investigation could help understand the
+behaviors of GPT-$3$ and large-scale pre-trained LMs in general and enhance
+their few-shot capabilities.
+"
+Modelling Latent Translations for Cross-Lingual Transfer,Edoardo Maria Ponti,http://arxiv.org/pdf/2107.11353v1.pdf,2021-07-23,['cs.cl'],2107.11353v1.pdf,"  While achieving state-of-the-art results in multiple tasks and languages,
+translation-based cross-lingual transfer is often overlooked in favour of
+massively multilingual pre-trained encoders. Arguably, this is due to its main
+limitations: 1) translation errors percolating to the classification phase and
+2) the insufficient expressiveness of the maximum-likelihood translation. To
+remedy this, we propose a new technique that integrates both steps of the
+traditional pipeline (translation and classification) into a single model, by
+treating the intermediate translations as a latent random variable. As a
+result, 1) the neural machine translation system can be fine-tuned with a
+variant of Minimum Risk Training where the reward is the accuracy of the
+downstream task classifier. Moreover, 2) multiple samples can be drawn to
+approximate the expected loss across all possible translations during
+inference. We evaluate our novel latent translation-based model on a series of
+multilingual NLU tasks, including commonsense reasoning, paraphrase
+identification, and natural language inference. We report gains for both
+zero-shot and few-shot learning setups, up to 2.7 accuracy points on average,
+which are even more prominent for low-resource languages (e.g., Haitian
+Creole). Finally, we carry out in-depth analyses comparing different underlying
+NMT models and assessing the impact of alternative translations on the
+downstream performance.
+"
+ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback,Mike Wu,http://arxiv.org/pdf/2107.14035v2.pdf,2021-07-23,"['cs.cy', 'cs.lg']",2107.14035v2.pdf,"  High-quality computer science education is limited by the difficulty of
+providing instructor feedback to students at scale. While this feedback could
+in principle be automated, supervised approaches to predicting the correct
+feedback are bottlenecked by the intractability of annotating large quantities
+of student code. In this paper, we instead frame the problem of providing
+feedback as few-shot classification, where a meta-learner adapts to give
+feedback to student code on a new programming question from just a few examples
+annotated by instructors. Because data for meta-training is limited, we propose
+a number of amendments to the typical few-shot learning framework, including
+task augmentation to create synthetic tasks, and additional side information to
+build stronger priors about each task. These additions are combined with a
+transformer architecture to embed discrete sequences (e.g. code) to a
+prototypical representation of a feedback class label. On a suite of few-shot
+natural language processing tasks, we match or outperform state-of-the-art
+performance. Then, on a collection of student solutions to exam questions from
+an introductory university course, we show that our approach reaches an average
+precision of 88% on unseen questions, surpassing the 82% precision of teaching
+assistants. Our approach was successfully deployed to deliver feedback to
+16,000 student exam-solutions in a programming course offered by a tier 1
+university. This is, to the best of our knowledge, the first successful
+deployment of a machine learning based feedback to open-ended student code.
+"
+Robust Retrieval Augmented Generation for Zero-shot Slot Filling,Michael Glass,http://arxiv.org/pdf/2108.13934v2.pdf,2021-08-31,"['cs.cl', 'cs.ai', 'cs.ir']",2108.13934v2.pdf,"  Automatically inducing high quality knowledge graphs from a given collection
+of documents still remains a challenging problem in AI. One way to make headway
+for this problem is through advancements in a related task known as slot
+filling. In this task, given an entity query in form of [Entity, Slot, ?], a
+system is asked to fill the slot by generating or extracting the missing value
+exploiting evidence extracted from relevant passage(s) in the given document
+collection. The recent works in the field try to solve this task in an
+end-to-end fashion using retrieval-based language models. In this paper, we
+present a novel approach to zero-shot slot filling that extends dense passage
+retrieval with hard negatives and robust training procedures for retrieval
+augmented generation models. Our model reports large improvements on both T-REx
+and zsRE slot filling datasets, improving both passage retrieval and slot value
+generation, and ranking at the top-1 position in the KILT leaderboard.
+Moreover, we demonstrate the robustness of our system showing its domain
+adaptation capability on a new variant of the TACRED dataset for slot filling,
+through a combination of zero/few-shot learning. We release the source code and
+pre-trained models.
+"
+Template-free Prompt Tuning for Few-shot NER,Ruotian Ma,http://arxiv.org/pdf/2109.13532v3.pdf,2021-09-28,"['cs.cl', 'cs.ai']",2109.13532v3.pdf,"  Prompt-based methods have been successfully applied in sentence-level
+few-shot learning tasks, mostly owing to the sophisticated design of templates
+and label words. However, when applied to token-level labeling tasks such as
+NER, it would be time-consuming to enumerate the template queries over all
+potential entity spans. In this work, we propose a more elegant method to
+reformulate NER tasks as LM problems without any templates. Specifically, we
+discard the template construction process while maintaining the word prediction
+paradigm of pre-training models to predict a class-related pivot word (or label
+word) at the entity position. Meanwhile, we also explore principled ways to
+automatically search for appropriate label words that the pre-trained models
+can easily adapt to. While avoiding complicated template-based process, the
+proposed LM objective also reduces the gap between different objectives used in
+pre-training and fine-tuning, thus it can better benefit the few-shot
+performance. Experimental results demonstrate the effectiveness of the proposed
+method over bert-tagger and template-based method under few-shot setting.
+Moreover, the decoding speed of the proposed method is up to 1930.12 times
+faster than the template-based method.
+"
+RAFT: A Real-World Few-Shot Text Classification Benchmark,Neel Alex,http://arxiv.org/pdf/2109.14076v3.pdf,2021-09-28,"['cs.cl', 'cs.ai', 'cs.lg']",2109.14076v3.pdf,"  Large pre-trained language models have shown promise for few-shot learning,
+completing text-based tasks given only a few task-specific examples. Will
+models soon solve classification tasks that have so far been reserved for human
+research assistants? Existing benchmarks are not designed to measure progress
+in applied settings, and so don't directly answer this question. The RAFT
+benchmark (Real-world Annotated Few-shot Tasks) focuses on naturally occurring
+tasks and uses an evaluation setup that mirrors deployment. Baseline
+evaluations on RAFT reveal areas current techniques struggle with: reasoning
+over long texts and tasks with many classes. Human baselines show that some
+classification tasks are difficult for non-expert humans, reflecting that
+real-world value sometimes depends on domain expertise. Yet even non-expert
+human baseline F1 scores exceed GPT-3 by an average of 0.11. The RAFT datasets
+and leaderboard will track which model improvements translate into real-world
+benefits at https://raft.elicit.org .
+"
+LFPT5: A Unified Framework for Lifelong Few-shot Language Learning Based  on Prompt Tuning of T5,Chengwei Qin,http://arxiv.org/pdf/2110.07298v3.pdf,2021-10-14,['cs.cl'],2110.07298v3.pdf,"  Existing approaches to lifelong language learning rely on plenty of labeled
+data for learning a new task, which is hard to obtain in most real scenarios.
+Considering that humans can continually learn new tasks from a handful of
+examples, we expect the models also to be able to generalize well on new
+few-shot tasks without forgetting the previous ones. In this work, we define
+this more challenging yet practical problem as Lifelong Few-shot Language
+Learning (LFLL) and propose a unified framework for it based on prompt tuning
+of T5. Our framework called LFPT5 takes full advantage of PT's strong few-shot
+learning ability, and simultaneously trains the model as a task solver and a
+data generator. Before learning a new domain of the same task type, LFPT5
+generates pseudo (labeled) samples of previously learned domains, and later
+gets trained on those samples to alleviate forgetting of previous knowledge as
+it learns the new domain. In addition, a KL divergence loss is minimized to
+achieve label consistency between the previous and the current model. While
+adapting to a new task type, LFPT5 includes and tunes additional prompt
+embeddings for the new task. With extensive experiments, we demonstrate that
+LFPT5 can be applied to various different types of tasks and significantly
+outperform previous methods in different LFLL settings.
+"
+MetaICL: Learning to Learn In Context,Sewon Min,http://arxiv.org/pdf/2110.15943v2.pdf,2021-10-29,"['cs.cl', 'cs.ai']",2110.15943v2.pdf,"  We introduce MetaICL (Meta-training for In-Context Learning), a new
+meta-training framework for few-shot learning where a pretrained language model
+is tuned to do in-context learning on a large set of training tasks. This
+meta-training enables the model to more effectively learn a new task in context
+at test time, by simply conditioning on a few training examples with no
+parameter updates or task-specific templates. We experiment on a large, diverse
+collection of tasks consisting of 142 NLP datasets including classification,
+question answering, natural language inference, paraphrase detection and more,
+across seven different meta-training/target splits. MetaICL outperforms a range
+of baselines including in-context learning without meta-training and multi-task
+learning followed by zero-shot transfer. We find that the gains are
+particularly significant for target tasks that have domain shifts from the
+meta-training tasks, and that using a diverse set of the meta-training tasks is
+key to improvements. We also show that MetaICL approaches (and sometimes beats)
+the performance of models fully finetuned on the target task, and outperforms
+much bigger models with nearly 8x parameters. Finally, we show that MetaICL is
+complementary to human-written instructions, and the best performance can be
+achieved by combining both approaches.
+"
+Scaling ASR Improves Zero and Few Shot Learning,Alex Xiao,http://arxiv.org/pdf/2111.05948v3.pdf,2021-11-10,"['cs.cl', 'cs.sd', 'eess.as']",2111.05948v3.pdf,"  With 4.5 million hours of English speech from 10 different sources across 120
+countries and models of up to 10 billion parameters, we explore the frontiers
+of scale for automatic speech recognition. We propose data selection techniques
+to efficiently scale training data to find the most valuable samples in massive
+datasets. To efficiently scale model sizes, we leverage various optimizations
+such as sparse transducer loss and model sharding. By training 1-10B parameter
+universal English ASR models, we push the limits of speech recognition
+performance across many domains. Furthermore, our models learn powerful speech
+representations with zero and few-shot capabilities on novel domains and styles
+of speech, exceeding previous results across multiple in-house and public
+benchmarks. For speakers with disorders due to brain damage, our best zero-shot
+and few-shot models achieve 22% and 60% relative improvement on the AphasiaBank
+test set, respectively, while realizing the best performance on public social
+media videos. Furthermore, the same universal model reaches equivalent
+performance with 500x less in-domain data on the SPGISpeech financial-domain
+dataset.
+"
+PointCLIP: Point Cloud Understanding by CLIP,Renrui Zhang,http://arxiv.org/pdf/2112.02413v1.pdf,2021-12-04,"['cs.cv', 'cs.ai', 'cs.ro']",2112.02413v1.pdf,"  Recently, zero-shot and few-shot learning via Contrastive Vision-Language
+Pre-training (CLIP) have shown inspirational performance on 2D visual
+recognition, which learns to match images with their corresponding texts in
+open-vocabulary settings. However, it remains under explored that whether CLIP,
+pre-trained by large-scale image-text pairs in 2D, can be generalized to 3D
+recognition. In this paper, we identify such a setting is feasible by proposing
+PointCLIP, which conducts alignment between CLIP-encoded point cloud and 3D
+category texts. Specifically, we encode a point cloud by projecting it into
+multi-view depth maps without rendering, and aggregate the view-wise zero-shot
+prediction to achieve knowledge transfer from 2D to 3D. On top of that, we
+design an inter-view adapter to better extract the global feature and
+adaptively fuse the few-shot knowledge learned from 3D into CLIP pre-trained in
+2D. By just fine-tuning the lightweight adapter in the few-shot settings, the
+performance of PointCLIP could be largely improved. In addition, we observe the
+complementary property between PointCLIP and classical 3D-supervised networks.
+By simple ensembling, PointCLIP boosts baseline's performance and even
+surpasses state-of-the-art models. Therefore, PointCLIP is a promising
+alternative for effective 3D point cloud understanding via CLIP under low
+resource cost and data regime. We conduct thorough experiments on
+widely-adopted ModelNet10, ModelNet40 and the challenging ScanObjectNN to
+demonstrate the effectiveness of PointCLIP. The code is released at
+https://github.com/ZrrSkywalker/PointCLIP.
+"
+A Survey of Deep Learning for Low-Shot Object Detection,Qihan Huang,http://arxiv.org/pdf/2112.02814v4.pdf,2021-12-06,"['cs.cv', 'cs.ai']",2112.02814v4.pdf,"  Object detection has achieved a huge breakthrough with deep neural networks
+and massive annotated data. However, current detection methods cannot be
+directly transferred to the scenario where the annotated data is scarce due to
+the severe overfitting problem. Although few-shot learning and zero-shot
+learning have been extensively explored in the field of image classification,
+it is indispensable to design new methods for object detection in the
+data-scarce scenario since object detection has an additional challenging
+localization task. Low-Shot Object Detection (LSOD) is an emerging research
+topic of detecting objects from a few or even no annotated samples, consisting
+of One-Shot Object Detection (OSOD), Few-Shot Object Detection (FSOD) and
+Zero-Shot Object Detection (ZSD). This survey provides a comprehensive review
+of LSOD methods. First, we propose a thorough taxonomy of LSOD methods and
+analyze them systematically, comprising some extensional topics of LSOD
+(semi-supervised LSOD, weakly-supervised LSOD, and incremental LSOD). Then, we
+indicate the pros and cons of current LSOD methods with a comparison of their
+performance. Finally, we discuss the challenges and promising directions of
+LSOD to provide guidance for future works.
+"
+"Vision-Language Intelligence: Tasks, Representation Learning, and Large  Models",Feng Li,http://arxiv.org/pdf/2203.01922v1.pdf,2022-03-03,"['cs.cv', 'cs.ai', 'cs.cl']",2203.01922v1.pdf,"  This paper presents a comprehensive survey of vision-language (VL)
+intelligence from the perspective of time. This survey is inspired by the
+remarkable progress in both computer vision and natural language processing,
+and recent trends shifting from single modality processing to multiple modality
+comprehension. We summarize the development in this field into three time
+periods, namely task-specific methods, vision-language pre-training (VLP)
+methods, and larger models empowered by large-scale weakly-labeled data. We
+first take some common VL tasks as examples to introduce the development of
+task-specific methods. Then we focus on VLP methods and comprehensively review
+key components of the model structures and training methods. After that, we
+show how recent work utilizes large-scale raw image-text data to learn
+language-aligned visual representations that generalize better on zero or few
+shot learning tasks. Finally, we discuss some potential future trends towards
+modality cooperation, unified representation, and knowledge incorporation. We
+believe that this review will be of help for researchers and practitioners of
+AI and ML, especially those interested in computer vision and natural language
+processing.
+"
+Rethinking Task Sampling for Few-shot Vision-Language Transfer Learning,Zhenhailong Wang,http://arxiv.org/pdf/2203.04904v3.pdf,2022-03-09,"['cs.mm', 'cs.cl', 'cs.cv']",2203.04904v3.pdf,"  Despite achieving state-of-the-art zero-shot performance, existing
+vision-language models still fall short of few-shot transfer ability on
+domain-specific problems. Classical fine-tuning often fails to prevent highly
+expressive models from exploiting spurious correlations. Although
+model-agnostic meta-learning (MAML) presents as a natural alternative for
+few-shot transfer learning, the expensive computation due to implicit
+second-order optimization limits its use on large-scale vision-language models
+such as CLIP. While much literature has been devoted to exploring alternative
+optimization strategies, we identify another essential aspect towards effective
+few-shot transfer learning, task sampling, which is previously only be viewed
+as part of data pre-processing in MAML. To show the impact of task sampling, we
+propose a simple algorithm, Model-Agnostic Multitask Fine-tuning (MAMF), which
+differentiates classical fine-tuning only on uniformly sampling multiple tasks.
+Despite its simplicity, we show that MAMF consistently outperforms classical
+fine-tuning on five few-shot vision-language classification tasks. We further
+show that the effectiveness of the bi-level optimization in MAML is highly
+sensitive to the zero-shot performance of a task in the context of few-shot
+vision-language classification. The goal of this paper is to provide new
+insights on what makes few-shot learning work, and encourage more research into
+investigating better task sampling strategies.
+"
+mGPT: Few-Shot Learners Go Multilingual,Oleh Shliazhko,http://arxiv.org/pdf/2204.07580v2.pdf,2022-04-15,"['cs.cl', 'cs.ai', '68-06, 68-04, 68t50, 68t01', 'i.2; i.2.7']",2204.07580v2.pdf,"  Recent studies report that autoregressive language models can successfully
+solve many NLP tasks via zero- and few-shot learning paradigms, which opens up
+new possibilities for using the pre-trained language models. This paper
+introduces two autoregressive GPT-like models with 1.3 billion and 13 billion
+parameters trained on 60 languages from 25 language families using Wikipedia
+and Colossal Clean Crawled Corpus. We reproduce the GPT-3 architecture using
+GPT-2 sources and the sparse attention mechanism; Deepspeed and Megatron
+frameworks allow us to parallelize the training and inference steps
+effectively. The resulting models show performance on par with the recently
+released XGLM models by Facebook, covering more languages and enhancing NLP
+possibilities for low resource languages of CIS countries and Russian small
+nations. We detail the motivation for the choices of the architecture design,
+thoroughly describe the data preparation pipeline, and train five small
+versions of the model to choose the most optimal multilingual tokenization
+strategy. We measure the model perplexity in all covered languages and evaluate
+it on the wide spectre of multilingual tasks, including classification,
+generative, sequence labeling and knowledge probing. The models were evaluated
+with the zero-shot and few-shot methods. Furthermore, we compared the
+classification tasks with the state-of-the-art multilingual model XGLM. source
+code and the mGPT XL model are publicly released.
+"
+In-BoXBART: Get Instructions into Biomedical Multi-Task Learning,Mihir Parmar,http://arxiv.org/pdf/2204.07600v1.pdf,2022-04-15,['cs.cl'],2204.07600v1.pdf,"  Single-task models have proven pivotal in solving specific tasks; however,
+they have limitations in real-world applications where multi-tasking is
+necessary and domain shifts are exhibited. Recently, instructional prompts have
+shown significant improvement towards multi-task generalization; however, the
+effect of instructional prompts and Multi-Task Learning (MTL) has not been
+systematically studied in the biomedical domain. Motivated by this, this paper
+explores the impact of instructional prompts for biomedical MTL. We introduce
+the BoX, a collection of 32 instruction tasks for Biomedical NLP across (X)
+various categories. Using this meta-dataset, we propose a unified model termed
+In-BoXBART, that can jointly learn all tasks of the BoX without any
+task-specific modules. To the best of our knowledge, this is the first attempt
+to propose a unified model in the biomedical domain and use instructions to
+achieve generalization across several biomedical tasks. Experimental results
+indicate that the proposed model: 1) outperforms the single-task baseline by
+~3% and multi-task (without instruction) baseline by ~18% on an average, and 2)
+shows ~23% improvement compared to the single-task baseline in few-shot
+learning (i.e., 32 instances per task) on an average. Our analysis indicates
+that there is significant room for improvement across tasks in the BoX,
+implying the scope for future research direction.
+"
+OPT: Open Pre-trained Transformer Language Models,Susan Zhang,http://arxiv.org/pdf/2205.01068v4.pdf,2022-05-02,"['cs.cl', 'cs.lg']",2205.01068v4.pdf,"  Large language models, which are often trained for hundreds of thousands of
+compute days, have shown remarkable capabilities for zero- and few-shot
+learning. Given their computational cost, these models are difficult to
+replicate without significant capital. For the few that are available through
+APIs, no access is granted to the full model weights, making them difficult to
+study. We present Open Pre-trained Transformers (OPT), a suite of decoder-only
+pre-trained transformers ranging from 125M to 175B parameters, which we aim to
+fully and responsibly share with interested researchers. We show that OPT-175B
+is comparable to GPT-3, while requiring only 1/7th the carbon footprint to
+develop. We are also releasing our logbook detailing the infrastructure
+challenges we faced, along with code for experimenting with all of the released
+models.
+"
+Relation Extraction as Open-book Examination: Retrieval-enhanced Prompt  Tuning,Xiang Chen,http://arxiv.org/pdf/2205.02355v2.pdf,2022-05-04,"['cs.cl', 'cs.ai', 'cs.ir', 'cs.lg']",2205.02355v2.pdf,"  Pre-trained language models have contributed significantly to relation
+extraction by demonstrating remarkable few-shot learning abilities. However,
+prompt tuning methods for relation extraction may still fail to generalize to
+those rare or hard patterns. Note that the previous parametric learning
+paradigm can be viewed as memorization regarding training data as a book and
+inference as the close-book test. Those long-tailed or hard patterns can hardly
+be memorized in parameters given few-shot instances. To this end, we regard RE
+as an open-book examination and propose a new semiparametric paradigm of
+retrieval-enhanced prompt tuning for relation extraction. We construct an
+open-book datastore for retrieval regarding prompt-based instance
+representations and corresponding relation labels as memorized key-value pairs.
+During inference, the model can infer relations by linearly interpolating the
+base output of PLM with the non-parametric nearest neighbor distribution over
+the datastore. In this way, our model not only infers relation through
+knowledge stored in the weights during training but also assists
+decision-making by unwinding and querying examples in the open-book datastore.
+Extensive experiments on benchmark datasets show that our method can achieve
+state-of-the-art in both standard supervised and few-shot settings. Code are
+available in https://github.com/zjunlp/PromptKG/tree/main/research/RetrievalRE.
+"
+Towards Unified Prompt Tuning for Few-shot Text Classification,Jianing Wang,http://arxiv.org/pdf/2205.05313v1.pdf,2022-05-11,"['cs.cl', 'cs.ai']",2205.05313v1.pdf,"  Prompt-based fine-tuning has boosted the performance of Pre-trained Language
+Models (PLMs) on few-shot text classification by employing task-specific
+prompts. Yet, PLMs are unfamiliar with prompt-style expressions during
+pre-training, which limits the few-shot learning performance on downstream
+tasks. It would be desirable if the models can acquire some prompting knowledge
+before adaptation to specific NLP tasks. We present the Unified Prompt Tuning
+(UPT) framework, leading to better few-shot text classification for BERT-style
+models by explicitly capturing prompting semantics from non-target NLP
+datasets. In UPT, a novel paradigm Prompt-Options-Verbalizer is proposed for
+joint prompt learning across different NLP tasks, forcing PLMs to capture
+task-invariant prompting knowledge. We further design a self-supervised task
+named Knowledge-enhanced Selective Masked Language Modeling to improve the
+PLM's generalization abilities for accurate adaptation to previously unseen
+tasks. After multi-task learning across multiple tasks, the PLM can be better
+prompt-tuned towards any dissimilar target tasks in low-resourced settings.
+Experiments over a variety of NLP tasks show that UPT consistently outperforms
+state-of-the-arts for prompt-based fine-tuning.
+"
+Towards Answering Open-ended Ethical Quandary Questions,Yejin Bang,http://arxiv.org/pdf/2205.05989v3.pdf,2022-05-12,"['cs.cl', 'cs.ai', 'cs.lg']",2205.05989v3.pdf,"  Considerable advancements have been made in various NLP tasks based on the
+impressive power of large language models (LLMs) and many NLP applications are
+deployed in our daily lives. In this work, we challenge the capability of LLMs
+with the new task of Ethical Quandary Generative Question Answering. Ethical
+quandary questions are more challenging to address because multiple conflicting
+answers may exist to a single quandary. We explore the current capability of
+LLMs in providing an answer with a deliberative exchange of different
+perspectives to an ethical quandary, in the approach of Socratic philosophy,
+instead of providing a closed answer like an oracle. We propose a model that
+searches for different ethical principles applicable to the ethical quandary
+and generates an answer conditioned on the chosen principles through
+prompt-based few-shot learning. We also discuss the remaining challenges and
+ethical issues involved in this task and suggest the direction toward
+developing responsible NLP systems by incorporating human values explicitly.
+"
+PromptDA: Label-guided Data Augmentation for Prompt-based Few-shot  Learners,Canyu Chen,http://arxiv.org/pdf/2205.09229v3.pdf,2022-05-18,"['cs.cl', 'cs.ai']",2205.09229v3.pdf,"  Recent advances in large pre-trained language models (PLMs) lead to
+impressive gains in natural language understanding (NLU) tasks with
+task-specific fine-tuning. However, directly fine-tuning PLMs heavily relies on
+sufficient labeled training instances, which are usually hard to obtain.
+Prompt-based tuning on PLMs has shown to be powerful for various downstream
+few-shot tasks. Existing works studying prompt-based tuning for few-shot NLU
+tasks mainly focus on deriving proper label words with a verbalizer or
+generating prompt templates to elicit semantics from PLMs. In addition,
+conventional data augmentation strategies such as synonym substitution, though
+widely adopted in low-resource scenarios, only bring marginal improvements for
+prompt-based few-shot learning. Thus, an important research question arises:
+how to design effective data augmentation methods for prompt-based few-shot
+tuning? To this end, considering the label semantics are essential in
+prompt-based tuning, we propose a novel label-guided data augmentation
+framework PromptDA, which exploits the enriched label semantic information for
+data augmentation. Extensive experiment results on few-shot text classification
+tasks demonstrate the superior performance of the proposed framework by
+effectively leveraging label semantics and data augmentation for natural
+language understanding. Our code is available at
+https://github.com/canyuchen/PromptDA.
+"
+What Makes Data-to-Text Generation Hard for Pretrained Language Models?,Moniba Keymanesh,http://arxiv.org/pdf/2205.11505v1.pdf,2022-05-23,"['cs.cl', 'cs.ai', 'cs.ir', 'cs.lg']",2205.11505v1.pdf,"  Expressing natural language descriptions of structured facts or relations --
+data-to-text generation (D2T) -- increases the accessibility of structured
+knowledge repositories. Previous work shows that pre-trained language
+models(PLMs) perform remarkably well on this task after fine-tuning on a
+significant amount of task-specific training data. On the other hand, while
+auto-regressive PLMs can generalize from a few task examples, their efficacy at
+D2T is largely unexplored. Furthermore, we have an incomplete understanding of
+the limits of PLMs on D2T.
+  In this work, we conduct an empirical study of both fine-tuned and
+auto-regressive PLMs on the DART multi-domain D2T dataset. We consider their
+performance as a function of the amount of task-specific data and how these
+data are incorporated into the models: zero and few-shot learning, and
+fine-tuning of model weights. In addition, we probe the limits of PLMs by
+measuring performance on subsets of the evaluation data: novel predicates and
+abstractive test examples. To improve the performance on these subsets, we
+investigate two techniques: providing predicate descriptions in the context and
+re-ranking generated candidates by information reflected in the source.
+Finally, we conduct a human evaluation of model errors and show that D2T
+generation tasks would benefit from datasets with more careful manual curation.
+"
+ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures  of Soft Prompts,Akari Asai,http://arxiv.org/pdf/2205.11961v2.pdf,2022-05-24,['cs.cl'],2205.11961v2.pdf,"  This work introduces a new multi-task, parameter-efficient language model
+(LM) tuning method that learns to transfer knowledge across different tasks via
+a mixture of soft prompts-small prefix embedding vectors pre-trained for
+different tasks. Our method, called ATTEMPT (ATTEntional Mixtures of Prompt
+Tuning), obtains source prompts as encodings of large-scale source tasks into a
+small number of parameters and trains an attention module to interpolate the
+source prompts and a newly initialized target prompt for every instance in the
+target task. During training, only the target task prompt and the attention
+weights, which are shared between tasks in multi-task training, are updated,
+while the original LM and source prompts are intact. ATTEMPT is highly
+parameter-efficient (e.g., updates 2,300 times fewer parameters than full
+fine-tuning) while achieving high task performance using knowledge from
+high-resource tasks. Moreover, it is modular using pre-trained soft prompts,
+and can flexibly add or remove source prompts for effective knowledge transfer.
+Our experimental results across 21 diverse NLP datasets show that ATTEMPT
+significantly outperforms prompt tuning and outperforms or matches fully
+fine-tuned or other parameter-efficient tuning approaches that use over ten
+times more parameters. Finally, ATTEMPT outperforms previous work in few-shot
+learning settings.
+"
+Making Large Language Models Better Reasoners with Step-Aware Verifier,Yifei Li,http://arxiv.org/pdf/2206.02336v3.pdf,2022-06-06,"['cs.cl', 'cs.ai']",2206.02336v3.pdf,"  Few-shot learning is a challenging task that requires language models to
+generalize from limited examples. Large language models like GPT-3 and PaLM
+have made impressive progress in this area, but they still face difficulties in
+reasoning tasks such as GSM8K, a benchmark for arithmetic problems. To improve
+their reasoning skills, previous work has proposed to guide the language model
+with prompts that elicit a series of reasoning steps before giving the final
+answer, achieving a significant improvement on GSM8K from 17.9% to 58.1% in
+problem-solving rate. In this paper, we present DIVERSE (Diverse Verifier on
+Reasoning Step), a novel approach that further enhances the reasoning
+capability of language models. DIVERSE has three main components: first, it
+generates diverse prompts to explore different reasoning paths for the same
+question; second, it uses a verifier to filter out incorrect answers based on a
+weighted voting scheme; and third, it verifies each reasoning step individually
+instead of the whole chain. We evaluate DIVERSE on the latest language model
+code-davinci-002 and show that it achieves new state-of-the-art results on six
+of eight reasoning benchmarks (e.g., GSM8K 74.4% to 83.2%).
+"
+Language Models are General-Purpose Interfaces,Yaru Hao,http://arxiv.org/pdf/2206.06336v1.pdf,2022-06-13,['cs.cl'],2206.06336v1.pdf,"  Foundation models have received much attention due to their effectiveness
+across a broad range of downstream applications. Though there is a big
+convergence in terms of architecture, most pretrained models are typically
+still developed for specific tasks or modalities. In this work, we propose to
+use language models as a general-purpose interface to various foundation
+models. A collection of pretrained encoders perceive diverse modalities (such
+as vision, and language), and they dock with a language model that plays the
+role of a universal task layer. We propose a semi-causal language modeling
+objective to jointly pretrain the interface and the modular encoders. We
+subsume the advantages and capabilities from both causal and non-causal
+modeling, thereby combining the best of two worlds. Specifically, the proposed
+method not only inherits the capabilities of in-context learning and open-ended
+generation from causal language modeling, but also is conducive to finetuning
+because of the bidirectional encoders. More importantly, our approach
+seamlessly unlocks the combinations of the above capabilities, e.g., enabling
+in-context learning or instruction following with finetuned encoders.
+Experimental results across various language-only and vision-language
+benchmarks show that our model outperforms or is competitive with specialized
+models on finetuning, zero-shot generalization, and few-shot learning.
+"
+FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and  Federated Image Classification,Aliaksandra Shysheya,http://arxiv.org/pdf/2206.08671v2.pdf,2022-06-17,"['stat.ml', 'cs.cv', 'cs.lg']",2206.08671v2.pdf,"  Modern deep learning systems are increasingly deployed in situations such as
+personalization and federated learning where it is necessary to support i)
+learning on small amounts of data, and ii) communication efficient distributed
+training protocols. In this work, we develop FiLM Transfer (FiT) which fulfills
+these requirements in the image classification setting by combining ideas from
+transfer learning (fixed pretrained backbones and fine-tuned FiLM adapter
+layers) and meta-learning (automatically configured Naive Bayes classifiers and
+episodic training) to yield parameter efficient models with superior
+classification accuracy at low-shot. The resulting parameter efficiency is key
+for enabling few-shot learning, inexpensive model updates for personalization,
+and communication efficient federated learning. We experiment with FiT on a
+wide range of downstream datasets and show that it achieves better
+classification accuracy than the leading Big Transfer (BiT) algorithm at
+low-shot and achieves state-of-the art accuracy on the challenging VTAB-1k
+benchmark, with fewer than 1% of the updateable parameters. Finally, we
+demonstrate the parameter efficiency and superior accuracy of FiT in
+distributed low-shot applications including model personalization and federated
+learning where model update size is an important performance metric.
+"
+A Reinforcement Learning-based Offensive semantics Censorship System for  Chatbots,Shaokang Cai,http://arxiv.org/pdf/2207.10569v1.pdf,2022-07-13,['cs.cl'],2207.10569v1.pdf,"  The rapid development of artificial intelligence (AI) technology has enabled
+large-scale AI applications to land in the market and practice. However, while
+AI technology has brought many conveniences to people in the productization
+process, it has also exposed many security issues. Especially, attacks against
+online learning vulnerabilities of chatbots occur frequently. Therefore, this
+paper proposes a semantics censorship chatbot system based on reinforcement
+learning, which is mainly composed of two parts: the Offensive semantics
+censorship model and the semantics purification model. Offensive semantics
+review can combine the context of user input sentences to detect the rapid
+evolution of Offensive semantics and respond to Offensive semantics responses.
+The semantics purification model For the case of chatting robot models, it has
+been contaminated by large numbers of offensive semantics, by strengthening the
+offensive reply learned by the learning algorithm, rather than rolling back to
+the early versions. In addition, by integrating a once-through learning
+approach, the speed of semantics purification is accelerated while reducing the
+impact on the quality of replies. The experimental results show that our
+proposed approach reduces the probability of the chat model generating
+offensive replies and that the integration of the few-shot learning algorithm
+improves the training speed rapidly while effectively slowing down the decline
+in BLEU values.
+"
+AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq  Model,Saleh Soltan,http://arxiv.org/pdf/2208.01448v2.pdf,2022-08-02,"['cs.cl', 'cs.lg']",2208.01448v2.pdf,"  In this work, we demonstrate that multilingual large-scale
+sequence-to-sequence (seq2seq) models, pre-trained on a mixture of denoising
+and Causal Language Modeling (CLM) tasks, are more efficient few-shot learners
+than decoder-only models on various tasks. In particular, we train a 20 billion
+parameter multilingual seq2seq model called Alexa Teacher Model (AlexaTM 20B)
+and show that it achieves state-of-the-art (SOTA) performance on 1-shot
+summarization tasks, outperforming a much larger 540B PaLM decoder model.
+AlexaTM 20B also achieves SOTA in 1-shot machine translation, especially for
+low-resource languages, across almost all language pairs supported by the model
+(Arabic, English, French, German, Hindi, Italian, Japanese, Marathi,
+Portuguese, Spanish, Tamil, and Telugu) on Flores-101 dataset. We also show in
+zero-shot setting, AlexaTM 20B outperforms GPT3 (175B) on SuperGLUE and SQuADv2
+datasets and provides SOTA performance on multilingual tasks such as XNLI,
+XCOPA, Paws-X, and XWinograd. Overall, our results present a compelling case
+for seq2seq models as a powerful alternative to decoder-only models for
+Large-scale Language Model (LLM) training.
+"
+Unsupervisedly Prompting AlphaFold2 for Few-Shot Learning of Accurate  Folding Landscape and Protein Structure Prediction,Jun Zhang,http://arxiv.org/pdf/2208.09652v2.pdf,2022-08-20,"['cs.lg', 'cs.ai', 'physics.bio-ph']",2208.09652v2.pdf,"  Data-driven predictive methods which can efficiently and accurately transform
+protein sequences into biologically active structures are highly valuable for
+scientific research and medical development. Determining accurate folding
+landscape using co-evolutionary information is fundamental to the success of
+modern protein structure prediction methods. As the state of the art,
+AlphaFold2 has dramatically raised the accuracy without performing explicit
+co-evolutionary analysis. Nevertheless, its performance still shows strong
+dependence on available sequence homologs. Based on the interrogation on the
+cause of such dependence, we presented EvoGen, a meta generative model, to
+remedy the underperformance of AlphaFold2 for poor MSA targets. By prompting
+the model with calibrated or virtually generated homologue sequences, EvoGen
+helps AlphaFold2 fold accurately in low-data regime and even achieve
+encouraging performance with single-sequence predictions. Being able to make
+accurate predictions with few-shot MSA not only generalizes AlphaFold2 better
+for orphan sequences, but also democratizes its use for high-throughput
+applications. Besides, EvoGen combined with AlphaFold2 yields a probabilistic
+structure generation method which could explore alternative conformations of
+protein sequences, and the task-aware differentiable algorithm for sequence
+generation will benefit other related tasks including protein design.
+"
+Disentangle and Remerge: Interventional Knowledge Distillation for  Few-Shot Object Detection from A Conditional Causal Perspective,Jiangmeng Li,http://arxiv.org/pdf/2208.12681v2.pdf,2022-08-26,['cs.cv'],2208.12681v2.pdf,"  Few-shot learning models learn representations with limited human
+annotations, and such a learning paradigm demonstrates practicability in
+various tasks, e.g., image classification, object detection, etc. However,
+few-shot object detection methods suffer from an intrinsic defect that the
+limited training data makes the model cannot sufficiently explore semantic
+information. To tackle this, we introduce knowledge distillation to the
+few-shot object detection learning paradigm. We further run a motivating
+experiment, which demonstrates that in the process of knowledge distillation,
+the empirical error of the teacher model degenerates the prediction performance
+of the few-shot object detection model as the student. To understand the
+reasons behind this phenomenon, we revisit the learning paradigm of knowledge
+distillation on the few-shot object detection task from the causal theoretic
+standpoint, and accordingly, develop a Structural Causal Model. Following the
+theoretical guidance, we propose a backdoor adjustment-based knowledge
+distillation method for the few-shot object detection task, namely Disentangle
+and Remerge (D&R), to perform conditional causal intervention toward the
+corresponding Structural Causal Model. Empirically, the experiments on
+benchmarks demonstrate that D&R can yield significant performance boosts in
+few-shot object detection. Code is available at
+https://github.com/ZYN-1101/DandR.git.
+"
+NeurIPS'22 Cross-Domain MetaDL competition: Design and baseline results,Dustin Carrión-Ojeda,http://arxiv.org/pdf/2208.14686v1.pdf,2022-08-31,"['cs.lg', 'cs.ai', 'cs.cv', 'cs.ne']",2208.14686v1.pdf,"  We present the design and baseline results for a new challenge in the
+ChaLearn meta-learning series, accepted at NeurIPS'22, focusing on
+""cross-domain"" meta-learning. Meta-learning aims to leverage experience gained
+from previous tasks to solve new tasks efficiently (i.e., with better
+performance, little training data, and/or modest computational resources).
+While previous challenges in the series focused on within-domain few-shot
+learning problems, with the aim of learning efficiently N-way k-shot tasks
+(i.e., N class classification problems with k training examples), this
+competition challenges the participants to solve ""any-way"" and ""any-shot""
+problems drawn from various domains (healthcare, ecology, biology,
+manufacturing, and others), chosen for their humanitarian and societal impact.
+To that end, we created Meta-Album, a meta-dataset of 40 image classification
+datasets from 10 domains, from which we carve out tasks with any number of
+""ways"" (within the range 2-20) and any number of ""shots"" (within the range
+1-20). The competition is with code submission, fully blind-tested on the
+CodaLab challenge platform. The code of the winners will be open-sourced,
+enabling the deployment of automated machine learning solutions for few-shot
+image classification across several domains.
+"
+Automatic Label Sequence Generation for Prompting Sequence-to-sequence  Models,Zichun Yu,http://arxiv.org/pdf/2209.09401v1.pdf,2022-09-20,"['cs.cl', 'cs.lg']",2209.09401v1.pdf,"  Prompting, which casts downstream applications as language modeling tasks,
+has shown to be sample efficient compared to standard fine-tuning with
+pre-trained models. However, one pitfall of prompting is the need of
+manually-designed patterns, whose outcome can be unintuitive and requires large
+validation sets to tune. To tackle the challenge, we propose AutoSeq, a fully
+automatic prompting method: (1) We adopt natural language prompts on
+sequence-to-sequence models, enabling free-form generation and larger label
+search space; (2) We propose label sequences -- phrases with indefinite lengths
+to verbalize the labels -- which eliminate the need of manual templates and are
+more expressive than single label words; (3) We use beam search to
+automatically generate a large amount of label sequence candidates and propose
+contrastive re-ranking to get the best combinations. AutoSeq significantly
+outperforms other no-manual-design methods, such as soft prompt tuning, adapter
+tuning, and automatic search on single label words; the generated label
+sequences are even better than curated manual ones on a variety of tasks. Our
+method reveals the potential of sequence-to-sequence models in few-shot
+learning and sheds light on a path to generic and automatic prompting. The
+source code of this paper can be obtained from
+https://github.com/thunlp/Seq2Seq-Prompt.
+"
+Collaboration of Pre-trained Models Makes Better Few-shot Learner,Renrui Zhang,http://arxiv.org/pdf/2209.12255v2.pdf,2022-09-25,['cs.cv'],2209.12255v2.pdf,"  Few-shot classification requires deep neural networks to learn generalized
+representations only from limited training images, which is challenging but
+significant in low-data regimes. Recently, CLIP-based methods have shown
+promising few-shot performance benefited from the contrastive language-image
+pre-training. Based on this point, we question if the large-scale pre-training
+can alleviate the few-shot data deficiency and also assist the representation
+learning by the pre-learned knowledge. In this paper, we propose CoMo, a
+Collaboration of pre-trained Models that incorporates diverse prior knowledge
+from various pre-training paradigms for better few-shot learning. Our CoMo
+includes: CLIP's language-contrastive knowledge, DINO's vision-contrastive
+knowledge, and DALL-E's language-generative knowledge. Specifically, CoMo works
+in two aspects: few-shot data expansion and diverse knowledge ensemble. For
+one, we generate synthetic images via zero-shot DALL-E to enrich the few-shot
+training data without any manpower. For the other, we introduce a learnable
+Multi-Knowledge Adapter (MK-Adapter) to adaptively blend the predictions from
+CLIP and DINO. By such collaboration, CoMo can fully unleash the potential of
+different pre-training methods and unify them to perform state-of-the-art for
+few-shot classification. We conduct extensive experiments on 11 datasets to
+demonstrate the superiority and generalization ability of our approach.
+"
+CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth  Pre-training,Tianyu Huang,http://arxiv.org/pdf/2210.01055v3.pdf,2022-10-03,['cs.cv'],2210.01055v3.pdf,"  Pre-training across 3D vision and language remains under development because
+of limited training data. Recent works attempt to transfer vision-language
+pre-training models to 3D vision. PointCLIP converts point cloud data to
+multi-view depth maps, adopting CLIP for shape classification. However, its
+performance is restricted by the domain gap between rendered depth maps and
+images, as well as the diversity of depth distributions. To address this issue,
+we propose CLIP2Point, an image-depth pre-training method by contrastive
+learning to transfer CLIP to the 3D domain, and adapt it to point cloud
+classification. We introduce a new depth rendering setting that forms a better
+visual effect, and then render 52,460 pairs of images and depth maps from
+ShapeNet for pre-training. The pre-training scheme of CLIP2Point combines
+cross-modality learning to enforce the depth features for capturing expressive
+visual and textual features and intra-modality learning to enhance the
+invariance of depth aggregation. Additionally, we propose a novel Dual-Path
+Adapter (DPA) module, i.e., a dual-path structure with simplified adapters for
+few-shot learning. The dual-path structure allows the joint use of CLIP and
+CLIP2Point, and the simplified adapter can well fit few-shot tasks without
+post-search. Experimental results show that CLIP2Point is effective in
+transferring CLIP knowledge to 3D vision. Our CLIP2Point outperforms PointCLIP
+and other self-supervised 3D networks, achieving state-of-the-art results on
+zero-shot and few-shot classification.
+"
+Instruction Tuning for Few-Shot Aspect-Based Sentiment Analysis,Siddharth Varia,http://arxiv.org/pdf/2210.06629v2.pdf,2022-10-12,['cs.cl'],2210.06629v2.pdf,"  Aspect-based Sentiment Analysis (ABSA) is a fine-grained sentiment analysis
+task which involves four elements from user-generated texts: aspect term,
+aspect category, opinion term, and sentiment polarity. Most computational
+approaches focus on some of the ABSA sub-tasks such as tuple (aspect term,
+sentiment polarity) or triplet (aspect term, opinion term, sentiment polarity)
+extraction using either pipeline or joint modeling approaches. Recently,
+generative approaches have been proposed to extract all four elements as (one
+or more) quadruplets from text as a single task. In this work, we take a step
+further and propose a unified framework for solving ABSA, and the associated
+sub-tasks to improve the performance in few-shot scenarios. To this end, we
+fine-tune a T5 model with instructional prompts in a multi-task learning
+fashion covering all the sub-tasks, as well as the entire quadruple prediction
+task. In experiments with multiple benchmark datasets, we show that the
+proposed multi-task prompting approach brings performance boost (by absolute
+8.29 F1) in the few-shot learning setting.
+"
+"RARR: Researching and Revising What Language Models Say, Using Language  Models",Luyu Gao,http://arxiv.org/pdf/2210.08726v3.pdf,2022-10-17,"['cs.cl', 'cs.ai', 'cs.ir', 'cs.lg']",2210.08726v3.pdf,"  Language models (LMs) now excel at many tasks such as few-shot learning,
+question answering, reasoning, and dialog. However, they sometimes generate
+unsupported or misleading content. A user cannot easily determine whether their
+outputs are trustworthy or not, because most LMs do not have any built-in
+mechanism for attribution to external evidence. To enable attribution while
+still preserving all the powerful advantages of recent generation models, we
+propose RARR (Retrofit Attribution using Research and Revision), a system that
+1) automatically finds attribution for the output of any text generation model
+and 2) post-edits the output to fix unsupported content while preserving the
+original output as much as possible. When applied to the output of several
+state-of-the-art LMs on a diverse set of generation tasks, we find that RARR
+significantly improves attribution while otherwise preserving the original
+input to a much greater degree than previously explored edit models.
+Furthermore, the implementation of RARR requires only a handful of training
+examples, a large language model, and standard web search.
+"
+TAPE: Assessing Few-shot Russian Language Understanding,Ekaterina Taktasheva,http://arxiv.org/pdf/2210.12813v1.pdf,2022-10-23,['cs.cl'],2210.12813v1.pdf,"  Recent advances in zero-shot and few-shot learning have shown promise for a
+scope of research and practical purposes. However, this fast-growing area lacks
+standardized evaluation suites for non-English languages, hindering progress
+outside the Anglo-centric paradigm. To address this line of research, we
+propose TAPE (Text Attack and Perturbation Evaluation), a novel benchmark that
+includes six more complex NLU tasks for Russian, covering multi-hop reasoning,
+ethical concepts, logic and commonsense knowledge. The TAPE's design focuses on
+systematic zero-shot and few-shot NLU evaluation: (i) linguistic-oriented
+adversarial attacks and perturbations for analyzing robustness, and (ii)
+subpopulations for nuanced interpretation. The detailed analysis of testing the
+autoregressive baselines indicates that simple spelling-based perturbations
+affect the performance the most, while paraphrasing the input has a more
+negligible effect. At the same time, the results demonstrate a significant gap
+between the neural and human baselines for most tasks. We publicly release TAPE
+(tape-benchmark.com) to foster research on robust LMs that can generalize to
+new tasks when little to no supervision is available.
+"
+Learning New Tasks from a Few Examples with Soft-Label Prototypes,Avyav Kumar Singh,http://arxiv.org/pdf/2210.17437v2.pdf,2022-10-31,"['cs.lg', 'cs.cl']",2210.17437v2.pdf,"  It has been experimentally demonstrated that humans are able to learn in a
+manner that allows them to make predictions on categories for which they have
+not seen any examples (Malaviya et al., 2022). Sucholutsky and Schonlau (2020)
+have recently presented a machine learning approach that aims to do the same.
+They utilise synthetically generated data and demonstrate that it is possible
+to achieve sub-linear scaling and develop models that can learn to recognise N
+classes from M training samples where M is less than N - aka less-than-one shot
+learning. Their method was, however, defined for univariate or simple
+multivariate data (Sucholutsky et al., 2021). We extend it to work on large,
+high-dimensional and real-world datasets and empirically validate it in this
+new and challenging setting. We apply this method to learn previously unseen
+NLP tasks from very few examples (4, 8 or 16). We first generate compact,
+sophisticated less-than-one shot representations called soft-label prototypes
+which are fitted on training data, capturing the distribution of different
+classes across the input domain space. We then use a modified k-Nearest
+Neighbours classifier to demonstrate that soft-label prototypes can classify
+data competitively, even outperforming much more computationally complex
+few-shot learning methods.
+"
+QAmeleon: Multilingual QA with Only 5 Examples,Priyanka Agrawal,http://arxiv.org/pdf/2211.08264v2.pdf,2022-11-15,['cs.cl'],2211.08264v2.pdf,"  The availability of large, high-quality datasets has been one of the main
+drivers of recent progress in question answering (QA). Such annotated datasets
+however are difficult and costly to collect, and rarely exist in languages
+other than English, rendering QA technology inaccessible to underrepresented
+languages. An alternative to building large monolingual training datasets is to
+leverage pre-trained language models (PLMs) under a few-shot learning setting.
+Our approach, QAmeleon, uses a PLM to automatically generate multilingual data
+upon which QA models are trained, thus avoiding costly annotation. Prompt
+tuning the PLM for data synthesis with only five examples per language delivers
+accuracy superior to translation-based baselines, bridges nearly 60% of the gap
+between an English-only baseline and a fully supervised upper bound trained on
+almost 50,000 hand labeled examples, and always leads to substantial
+improvements compared to fine-tuning a QA model directly on labeled examples in
+low resource settings. Experiments on the TyDiQA-GoldP and MLQA benchmarks show
+that few-shot prompt tuning for data synthesis scales across languages and is a
+viable alternative to large-scale annotation.
+"
+Explicit Knowledge Transfer for Weakly-Supervised Code Generation,Zhangir Azerbayev,http://arxiv.org/pdf/2211.16740v3.pdf,2022-11-30,['cs.cl'],2211.16740v3.pdf,"  Large language models (LLMs) can acquire strong code-generation capabilities
+through few-shot learning. In contrast, supervised fine-tuning is still needed
+for smaller models to achieve good performance. Such fine-tuning demands a
+large number of task-specific NL-code pairs, which are expensive to obtain. In
+this paper, we attempt to transfer the code generation ability of an LLM to a
+smaller model with the aid of weakly-supervised data. More specifically, we
+propose explicit knowledge transfer (EKT), which uses the few-shot capabilities
+of a teacher LLM to create NL-code pairs that we then filter for correctness
+and fine-tune the student on. We evaluate EKT on the task of generating code
+solutions to math word problems from the GSM8k dataset. We find that EKT not
+only yields better performance than training with expert iteration, but also
+outperforms knowledge distillation, another form of knowledge transfer. A
+GPT-Neo 1.3B model trained using EKT with a GPT-J teacher achieves a 12.4%
+pass@100 on GSM8k, while the same student and teacher trained with knowledge
+distillation yield only a 3.7% pass@100. We also show that it is possible for a
+student model to outperform the teacher using EKT.
+"
+Can In-context Learners Learn a Reasoning Concept from Demonstrations?,Michal Štefánik,http://arxiv.org/pdf/2212.01692v4.pdf,2022-12-03,"['cs.cl', 'cs.ai', 'cs.lg']",2212.01692v4.pdf,"  Language models exhibit an emergent ability to learn a new task from a small
+number of input-output demonstrations. However, recent work shows that
+in-context learners largely rely on their pre-trained knowledge, such as the
+sentiment of the labels, instead of learning new associations from the input.
+We argue that the commonly-used few-shot evaluation using a random selection of
+in-context demonstrations can not disentangle models' reliance on such biases,
+as most of the randomly-selected demonstrations do not present relations
+informative for prediction beyond exposing the task's input-output
+distribution.
+  Therefore, to evaluate models' in-context learning ability independent of
+models' memory, we introduce a Concept-sharing few-shot learning method
+choosing the demonstrations that share an underlying concept with the predicted
+sample. We extract a set of such concepts from available human explanations and
+measure how much models can benefit from presenting these concepts in few-shot
+demonstrations.
+  We find that most of the recent in-context learners can not consistently
+benefit from the demonstrated concepts, irrespective of the model size.
+However, we note that T0 models are more sensitive to exhibited concepts,
+benefiting from concept-sharing demonstrations in 7 out of 8 evaluation
+scenarios.
+"
+Frozen CLIP Model is An Efficient Point Cloud Backbone,Xiaoshui Huang,http://arxiv.org/pdf/2212.04098v2.pdf,2022-12-08,['cs.cv'],2212.04098v2.pdf,"  The pretraining-finetuning paradigm has demonstrated great success in NLP and
+2D image fields because of the high-quality representation ability and
+transferability of their pretrained models. However, pretraining such a strong
+model is difficult in the 3D point cloud field since the training data is
+limited and point cloud collection is expensive. This paper introduces
+Efficient Point Cloud Learning (EPCL), an effective and efficient point cloud
+learner for directly training high-quality point cloud models with a frozen
+CLIP model. Our EPCL connects the 2D and 3D modalities by semantically aligning
+the 2D features and point cloud features without paired 2D-3D data.
+Specifically, the input point cloud is divided into a sequence of tokens and
+directly fed into the frozen CLIP model to learn point cloud representation.
+Furthermore, we design a task token to narrow the gap between 2D images and 3D
+point clouds. Comprehensive experiments on 3D detection, semantic segmentation,
+classification and few-shot learning demonstrate that the 2D CLIP model can be
+an efficient point cloud backbone and our method achieves state-of-the-art
+accuracy on both real-world and synthetic downstream tasks. Code will be
+available.
+"
+Federated Few-Shot Learning for Mobile NLP,Dongqi Cai,http://arxiv.org/pdf/2212.05974v2.pdf,2022-12-12,"['cs.lg', 'cs.cl']",2212.05974v2.pdf,"  Natural language processing (NLP) sees rich mobile applications. To support
+various language understanding tasks, a foundation NLP model is often
+fine-tuned in a federated, privacy-preserving setting (FL). This process
+currently relies on at least hundreds of thousands of labeled training samples
+from mobile clients; yet mobile users often lack willingness or knowledge to
+label their data. Such an inadequacy of data labels is known as a few-shot
+scenario; it becomes the key blocker for mobile NLP applications.
+  For the first time, this work investigates federated NLP in the few-shot
+scenario (FedFSL). By retrofitting algorithmic advances of pseudo labeling and
+prompt learning, we first establish a training pipeline that delivers
+competitive accuracy when only 0.05% (fewer than 100) of the training data is
+labeled and the remaining is unlabeled. To instantiate the workflow, we further
+present a system FeS, addressing the high execution cost with novel designs.
+(1) Curriculum pacing, which injects pseudo labels to the training workflow at
+a rate commensurate to the learning progress; (2) Representational diversity, a
+mechanism for selecting the most learnable data, only for which pseudo labels
+will be generated; (3) Co-planning of a model's training depth and layer
+capacity. Together, these designs reduce the training delay, client energy, and
+network traffic by up to 46.0$\times$, 41.2$\times$ and 3000.0$\times$,
+respectively. Through algorithm/system co-design, FFNLP demonstrates that FL
+can apply to challenging settings where most training samples are unlabeled.
+"
+FewFedWeight: Few-shot Federated Learning Framework across Multiple NLP  Tasks,Weilong Dong,http://arxiv.org/pdf/2212.08354v1.pdf,2022-12-16,['cs.cl'],2212.08354v1.pdf,"  Massively multi-task learning with large language models has recently made
+substantial progress on few-shot generalization. However, this is usually
+performed in a centralized learning fashion, ignoring the privacy sensitivity
+issue of (annotated) data used in multiple tasks. To mitigate this issue, we
+propose FewFedWeight, a few-shot federated learning framework across multiple
+tasks, to achieve the best of both worlds: privacy preservation and cross-task
+generalization. FewFedWeight trains client models in isolated devices without
+sharing data. It broadcasts the global model in the server to each client and
+produces pseudo data for clients so that knowledge from the global model can be
+explored to enhance few-shot learning of each client model. An energy-based
+algorithm is further proposed to weight pseudo samples in order to reduce the
+negative impact of noise from the generated pseudo data. Adaptive model weights
+of client models are also tuned according to their performance. We use these
+model weights to dynamically aggregate client models to update the global
+model. Experiments on 118 NLP tasks show that FewFedWeight can significantly
+improve the performance of client models on 61% tasks with an average
+performance improvement rate of 30.5% over the baseline and substantially
+outperform FedAvg and other decentralized learning methods.
+"
+Contrastive Distillation Is a Sample-Efficient Self-Supervised Loss  Policy for Transfer Learning,Chris Lengerich,http://arxiv.org/pdf/2212.11353v1.pdf,2022-12-21,"['cs.cl', 'cs.lg']",2212.11353v1.pdf,"  Traditional approaches to RL have focused on learning decision policies
+directly from episodic decisions, while slowly and implicitly learning the
+semantics of compositional representations needed for generalization. While
+some approaches have been adopted to refine representations via auxiliary
+self-supervised losses while simultaneously learning decision policies,
+learning compositional representations from hand-designed and
+context-independent self-supervised losses (multi-view) still adapts relatively
+slowly to the real world, which contains many non-IID subspaces requiring rapid
+distribution shift in both time and spatial attention patterns at varying
+levels of abstraction. In contrast, supervised language model cascades have
+shown the flexibility to adapt to many diverse manifolds, and hints of
+self-learning needed for autonomous task transfer. However, to date, transfer
+methods for language models like few-shot learning and fine-tuning still
+require human supervision and transfer learning using self-learning methods has
+been underexplored. We propose a self-supervised loss policy called contrastive
+distillation which manifests latent variables with high mutual information with
+both source and target tasks from weights to tokens. We show how this
+outperforms common methods of transfer learning and suggests a useful design
+axis of trading off compute for generalizability for online transfer.
+Contrastive distillation is improved through sampling from memory and suggests
+a simple algorithm for more efficiently sampling negative examples for
+contrastive losses than random sampling.
+"
+Exploring Efficient Few-shot Adaptation for Vision Transformers,Chengming Xu,http://arxiv.org/pdf/2301.02419v1.pdf,2023-01-06,['cs.cv'],2301.02419v1.pdf,"  The task of Few-shot Learning (FSL) aims to do the inference on novel
+categories containing only few labeled examples, with the help of knowledge
+learned from base categories containing abundant labeled training samples.
+While there are numerous works into FSL task, Vision Transformers (ViTs) have
+rarely been taken as the backbone to FSL with few trials focusing on naive
+finetuning of whole backbone or classification layer.} Essentially, despite
+ViTs have been shown to enjoy comparable or even better performance on other
+vision tasks, it is still very nontrivial to efficiently finetune the ViTs in
+real-world FSL scenarios. To this end, we propose a novel efficient Transformer
+Tuning (eTT) method that facilitates finetuning ViTs in the FSL tasks. The key
+novelties come from the newly presented Attentive Prefix Tuning (APT) and
+Domain Residual Adapter (DRA) for the task and backbone tuning, individually.
+Specifically, in APT, the prefix is projected to new key and value pairs that
+are attached to each self-attention layer to provide the model with
+task-specific information. Moreover, we design the DRA in the form of learnable
+offset vectors to handle the potential domain gaps between base and novel data.
+To ensure the APT would not deviate from the initial task-specific information
+much, we further propose a novel prototypical regularization, which maximizes
+the similarity between the projected distribution of prefix and initial
+prototypes, regularizing the update procedure. Our method receives outstanding
+performance on the challenging Meta-Dataset. We conduct extensive experiments
+to show the efficacy of our model.
+"
+Unleashing the Power of Shared Label Structures for Human Activity  Recognition,Xiyuan Zhang,http://arxiv.org/pdf/2301.03462v2.pdf,2023-01-01,"['cs.lg', 'cs.ai', 'eess.sp']",2301.03462v2.pdf,"  Current human activity recognition (HAR) techniques regard activity labels as
+integer class IDs without explicitly modeling the semantics of class labels. We
+observe that different activity names often have shared structures. For
+example, ""open door"" and ""open fridge"" both have ""open"" as the action; ""kicking
+soccer ball"" and ""playing tennis ball"" both have ""ball"" as the object. Such
+shared structures in label names can be translated to the similarity in sensory
+data and modeling common structures would help uncover knowledge across
+different activities, especially for activities with limited samples. In this
+paper, we propose SHARE, a HAR framework that takes into account shared
+structures of label names for different activities. To exploit the shared
+structures, SHARE comprises an encoder for extracting features from input
+sensory time series and a decoder for generating label names as a token
+sequence. We also propose three label augmentation techniques to help the model
+more effectively capture semantic structures across activities, including a
+basic token-level augmentation, and two enhanced embedding-level and
+sequence-level augmentations utilizing the capabilities of pre-trained models.
+SHARE outperforms state-of-the-art HAR models in extensive experiments on seven
+HAR benchmark datasets. We also evaluate in few-shot learning and label
+imbalance settings and observe even more significant performance gap.
+"
+"See, Think, Confirm: Interactive Prompting Between Vision and Language  Models for Knowledge-based Visual Reasoning",Zhenfang Chen,http://arxiv.org/pdf/2301.05226v1.pdf,2023-01-12,"['cs.cv', 'cs.ai', 'cs.cl', 'cs.lg']",2301.05226v1.pdf,"  Large pre-trained vision and language models have demonstrated remarkable
+capacities for various tasks. However, solving the knowledge-based visual
+reasoning tasks remains challenging, which requires a model to comprehensively
+understand image content, connect the external world knowledge, and perform
+step-by-step reasoning to answer the questions correctly. To this end, we
+propose a novel framework named Interactive Prompting Visual Reasoner (IPVR)
+for few-shot knowledge-based visual reasoning. IPVR contains three stages, see,
+think and confirm. The see stage scans the image and grounds the visual concept
+candidates with a visual perception model. The think stage adopts a pre-trained
+large language model (LLM) to attend to the key concepts from candidates
+adaptively. It then transforms them into text context for prompting with a
+visual captioning model and adopts the LLM to generate the answer. The confirm
+stage further uses the LLM to generate the supporting rationale to the answer,
+verify the generated rationale with a cross-modality classifier and ensure that
+the rationale can infer the predicted output consistently. We conduct
+experiments on a range of knowledge-based visual reasoning datasets. We found
+our IPVR enjoys several benefits, 1). it achieves better performance than the
+previous few-shot learning baselines; 2). it enjoys the total transparency and
+trustworthiness of the whole reasoning process by providing rationales for each
+reasoning step; 3). it is computation-efficient compared with other fine-tuning
+baselines.
+"
+Large Language Models Are Latent Variable Models: Explaining and Finding  Good Demonstrations for In-Context Learning,Xinyi Wang,http://arxiv.org/pdf/2301.11916v3.pdf,2023-01-27,"['cs.cl', 'cs.ai', 'cs.lg']",2301.11916v3.pdf,"  In recent years, pre-trained large language models (LLMs) have demonstrated
+remarkable efficiency in achieving an inference-time few-shot learning
+capability known as in-context learning. However, existing literature has
+highlighted the sensitivity of this capability to the selection of few-shot
+demonstrations. Current understandings of the underlying mechanisms by which
+this capability arises from regular language model pretraining objectives
+remain disconnected from the real-world LLMs. This study aims to examine the
+in-context learning phenomenon through a Bayesian lens, viewing real-world LLMs
+as latent variable models. On this premise, we propose an algorithm to select
+optimal demonstrations from a set of annotated data with a small LM, and then
+directly generalize the selected demonstrations to larger LMs. We demonstrate
+significant improvement over baselines, averaged over eight GPT models on eight
+real-world text classification datasets. We also demonstrate the real-world
+usefulness of our algorithm on GSM8K, a math word problem dataset. Our
+empirical findings support our hypothesis that LLMs implicitly infer a latent
+variable containing task information.
+"
+Language Quantized AutoEncoders: Towards Unsupervised Text-Image  Alignment,Hao Liu,http://arxiv.org/pdf/2302.00902v2.pdf,2023-02-02,"['cs.lg', 'cs.cl', 'cs.cv']",2302.00902v2.pdf,"  Recent progress in scaling up large language models has shown impressive
+capabilities in performing few-shot learning across a wide range of text-based
+tasks. However, a key limitation is that these language models fundamentally
+lack visual perception - a crucial attribute needed to extend these models to
+be able to interact with the real world and solve vision tasks, such as in
+visual-question answering and robotics. Prior works have largely connected
+image to text through pretraining and/or fine-tuning on curated image-text
+datasets, which can be a costly and expensive process. In order to resolve this
+limitation, we propose a simple yet effective approach called
+Language-Quantized AutoEncoder (LQAE), a modification of VQ-VAE that learns to
+align text-image data in an unsupervised manner by leveraging pretrained
+language models (e.g., BERT, RoBERTa). Our main idea is to encode image as
+sequences of text tokens by directly quantizing image embeddings using a
+pretrained language codebook. We then apply random masking followed by a BERT
+model, and have the decoder reconstruct the original image from BERT predicted
+text token embeddings. By doing so, LQAE learns to represent similar images
+with similar clusters of text tokens, thereby aligning these two modalities
+without the use of aligned text-image pairs. This enables few-shot image
+classification with large language models (e.g., GPT-3) as well as linear
+classification of images based on BERT text features. To the best of our
+knowledge, our work is the first work that uses unaligned images for multimodal
+tasks by leveraging the power of pretrained language models.
+"
+The unreasonable effectiveness of few-shot learning for machine  translation,Xavier Garcia,http://arxiv.org/pdf/2302.01398v1.pdf,2023-02-02,['cs.cl'],2302.01398v1.pdf,"  We demonstrate the potential of few-shot translation systems, trained with
+unpaired language data, for both high and low-resource language pairs. We show
+that with only 5 examples of high-quality translation data shown at inference,
+a transformer decoder-only model trained solely with self-supervised learning,
+is able to match specialized supervised state-of-the-art models as well as more
+general commercial translation systems. In particular, we outperform the best
+performing system on the WMT'21 English - Chinese news translation task by only
+using five examples of English - Chinese parallel data at inference. Moreover,
+our approach in building these models does not necessitate joint multilingual
+training or back-translation, is conceptually simple and shows the potential to
+extend to the multilingual setting. Furthermore, the resulting models are two
+orders of magnitude smaller than state-of-the-art language models. We then
+analyze the factors which impact the performance of few-shot translation
+systems, and highlight that the quality of the few-shot demonstrations heavily
+determines the quality of the translations generated by our models. Finally, we
+show that the few-shot paradigm also provides a way to control certain
+attributes of the translation -- we show that we are able to control for
+regional varieties and formality using only a five examples at inference,
+paving the way towards controllable machine translation systems.
+"
+CrossCodeBench: Benchmarking Cross-Task Generalization of Source Code  Models,Changan Niu,http://arxiv.org/pdf/2302.04030v2.pdf,2023-02-08,"['cs.se', 'cs.ai']",2302.04030v2.pdf,"  Despite the recent advances showing that a model pre-trained on large-scale
+source code data is able to gain appreciable generalization capability, it
+still requires a sizeable amount of data on the target task for fine-tuning.
+And the effectiveness of the model generalization is largely affected by the
+size and quality of the fine-tuning data, which is detrimental for target tasks
+with limited or unavailable resources. Therefore, cross-task generalization,
+with the goal of improving the generalization of the model to unseen tasks that
+have not been seen before, is of strong research and application value.
+  In this paper, we propose a large-scale benchmark that includes 216 existing
+code-related tasks. Then, we annotate each task with the corresponding meta
+information such as task description and instruction, which contains detailed
+information about the task and a solution guide. This also helps us to easily
+create a wide variety of ``training/evaluation'' task splits to evaluate the
+various cross-task generalization capabilities of the model. Then we perform
+some preliminary experiments to demonstrate that the cross-task generalization
+of models can be largely improved by in-context learning methods such as
+few-shot learning and learning from task instructions, which shows the
+promising prospects of conducting cross-task learning research on our
+benchmark. We hope that the collection of the datasets and our benchmark will
+facilitate future work that is not limited to cross-task generalization.
+"
+Re-ViLM: Retrieval-Augmented Visual Language Model for Zero and Few-Shot  Image Captioning,Zhuolin Yang,http://arxiv.org/pdf/2302.04858v2.pdf,2023-02-09,"['cs.cv', 'cs.ai', 'cs.cl', 'cs.ir', 'cs.lg']",2302.04858v2.pdf,"  Augmenting pretrained language models (LMs) with a vision encoder (e.g.,
+Flamingo) has obtained the state-of-the-art results in image-to-text
+generation. However, these models store all the knowledge within their
+parameters, thus often requiring enormous model parameters to model the
+abundant visual concepts and very rich textual descriptions. Additionally, they
+are inefficient in incorporating new data, requiring a computational-expensive
+fine-tuning process. In this work, we introduce a Retrieval-augmented Visual
+Language Model, Re-ViLM, built upon the Flamingo, that supports retrieving the
+relevant knowledge from the external database for zero and in-context few-shot
+image-to-text generations. By storing certain knowledge explicitly in the
+external database, our approach reduces the number of model parameters and can
+easily accommodate new data during evaluation by simply updating the database.
+We also construct an interleaved image and text data that facilitates
+in-context few-shot learning capabilities. We demonstrate that Re-ViLM
+significantly boosts performance for image-to-text generation tasks, especially
+for zero-shot and few-shot generation in out-of-domain settings with 4 times
+less parameters compared with baseline methods.
+"
+Mask-guided BERT for Few Shot Text Classification,Wenxiong Liao,http://arxiv.org/pdf/2302.10447v3.pdf,2023-02-21,"['cs.cl', 'cs.ai']",2302.10447v3.pdf,"  Transformer-based language models have achieved significant success in
+various domains. However, the data-intensive nature of the transformer
+architecture requires much labeled data, which is challenging in low-resource
+scenarios (i.e., few-shot learning (FSL)). The main challenge of FSL is the
+difficulty of training robust models on small amounts of samples, which
+frequently leads to overfitting. Here we present Mask-BERT, a simple and
+modular framework to help BERT-based architectures tackle FSL. The proposed
+approach fundamentally differs from existing FSL strategies such as prompt
+tuning and meta-learning. The core idea is to selectively apply masks on text
+inputs and filter out irrelevant information, which guides the model to focus
+on discriminative tokens that influence prediction results. In addition, to
+make the text representations from different categories more separable and the
+text representations from the same category more compact, we introduce a
+contrastive learning loss function. Experimental results on public-domain
+benchmark datasets demonstrate the effectiveness of Mask-BERT.
+"
+Meta-Learning with Adaptive Weighted Loss for Imbalanced Cold-Start  Recommendation,Minchang Kim,http://arxiv.org/pdf/2302.14640v2.pdf,2023-02-28,"['cs.ir', 'cs.lg']",2302.14640v2.pdf,"  Sequential recommenders have made great strides in capturing a user's
+preferences. Nevertheless, the cold-start recommendation remains a fundamental
+challenge as they typically involve limited user-item interactions for
+personalization. Recently, gradient-based meta-learning approaches have emerged
+in the sequential recommendation field due to their fast adaptation and
+easy-to-integrate abilities. The meta-learning algorithms formulate the
+cold-start recommendation as a few-shot learning problem, where each user is
+represented as a task to be adapted. While meta-learning algorithms generally
+assume that task-wise samples are evenly distributed over classes or values,
+user-item interactions in real-world applications do not conform to such a
+distribution (e.g., watching favorite videos multiple times, leaving only
+positive ratings without any negative ones). Consequently, imbalanced user
+feedback, which accounts for the majority of task training data, may dominate
+the user adaptation process and prevent meta-learning algorithms from learning
+meaningful meta-knowledge for personalized recommendations. To alleviate this
+limitation, we propose a novel sequential recommendation framework based on
+gradient-based meta-learning that captures the imbalanced rating distribution
+of each user and computes adaptive loss for user-specific learning. Our work is
+the first to tackle the impact of imbalanced ratings in cold-start sequential
+recommendation scenarios. Through extensive experiments conducted on real-world
+datasets, we demonstrate the effectiveness of our framework.
+"
+"Prompt, Generate, then Cache: Cascade of Foundation Models makes Strong  Few-shot Learners",Renrui Zhang,http://arxiv.org/pdf/2303.02151v1.pdf,2023-03-03,"['cs.cv', 'cs.cl']",2303.02151v1.pdf,"  Visual recognition in low-data regimes requires deep neural networks to learn
+generalized representations from limited training samples. Recently, CLIP-based
+methods have shown promising few-shot performance benefited from the
+contrastive language-image pre-training. We then question, if the more diverse
+pre-training knowledge can be cascaded to further assist few-shot
+representation learning. In this paper, we propose CaFo, a Cascade of
+Foundation models that incorporates diverse prior knowledge of various
+pre-training paradigms for better few-shot learning. Our CaFo incorporates
+CLIP's language-contrastive knowledge, DINO's vision-contrastive knowledge,
+DALL-E's vision-generative knowledge, and GPT-3's language-generative
+knowledge. Specifically, CaFo works by 'Prompt, Generate, then Cache'. Firstly,
+we leverage GPT-3 to produce textual inputs for prompting CLIP with rich
+downstream linguistic semantics. Then, we generate synthetic images via DALL-E
+to expand the few-shot training data without any manpower. At last, we
+introduce a learnable cache model to adaptively blend the predictions from CLIP
+and DINO. By such collaboration, CaFo can fully unleash the potential of
+different pre-training methods and unify them to perform state-of-the-art for
+few-shot classification. Code is available at
+https://github.com/ZrrSkywalker/CaFo.
+"
+Knowledge-augmented Few-shot Visual Relation Detection,Tianyu Yu,http://arxiv.org/pdf/2303.05342v1.pdf,2023-03-09,"['cs.cv', 'cs.ai']",2303.05342v1.pdf,"  Visual Relation Detection (VRD) aims to detect relationships between objects
+for image understanding. Most existing VRD methods rely on thousands of
+training samples of each relationship to achieve satisfactory performance. Some
+recent papers tackle this problem by few-shot learning with elaborately
+designed pipelines and pre-trained word vectors. However, the performance of
+existing few-shot VRD models is severely hampered by the poor generalization
+capability, as they struggle to handle the vast semantic diversity of visual
+relationships. Nonetheless, humans have the ability to learn new relationships
+with just few examples based on their knowledge. Inspired by this, we devise a
+knowledge-augmented, few-shot VRD framework leveraging both textual knowledge
+and visual relation knowledge to improve the generalization ability of few-shot
+VRD. The textual knowledge and visual relation knowledge are acquired from a
+pre-trained language model and an automatically constructed visual relation
+knowledge graph, respectively. We extensively validate the effectiveness of our
+framework. Experiments conducted on three benchmarks from the commonly used
+Visual Genome dataset show that our performance surpasses existing
+state-of-the-art models with a large improvement.
+"
+Gradient-Regulated Meta-Prompt Learning for Generalizable  Vision-Language Models,Juncheng Li,http://arxiv.org/pdf/2303.06571v2.pdf,2023-03-12,['cs.cv'],2303.06571v2.pdf,"  Prompt tuning, a recently emerging paradigm, enables the powerful
+vision-language pre-training models to adapt to downstream tasks in a parameter
+-- and data -- efficient way, by learning the ``soft prompts'' to condition
+frozen pre-training models. Though effective, it is particularly problematic in
+the few-shot scenario, where prompt tuning performance is sensitive to the
+initialization and requires a time-consuming process to find a good
+initialization, thus restricting the fast adaptation ability of the
+pre-training models. In addition, prompt tuning could undermine the
+generalizability of the pre-training models, because the learnable prompt
+tokens are easy to overfit to the limited training samples. To address these
+issues, we introduce a novel Gradient-RegulAted Meta-prompt learning (GRAM)
+framework that jointly meta-learns an efficient soft prompt initialization for
+better adaptation and a lightweight gradient regulating function for strong
+cross-domain generalizability in a meta-learning paradigm using only the
+unlabeled image-text pre-training data. Rather than designing a specific prompt
+tuning method, our GRAM can be easily incorporated into various prompt tuning
+methods in a model-agnostic way, and comprehensive experiments show that GRAM
+brings about consistent improvement for them in several settings (i.e.,
+few-shot learning, cross-domain generalization, cross-dataset generalization,
+etc.) over 11 datasets. Further, experiments show that GRAM enables the
+orthogonal methods of textual and visual prompt tuning to work in a
+mutually-enhanced way, offering better generalizability beyond the uni-modal
+prompt tuning methods.
+"
+Decomposed Prototype Learning for Few-Shot Scene Graph Generation,Xingchen Li,http://arxiv.org/pdf/2303.10863v1.pdf,2023-03-20,['cs.cv'],2303.10863v1.pdf,"  Today's scene graph generation (SGG) models typically require abundant manual
+annotations to learn new predicate types. Thus, it is difficult to apply them
+to real-world applications with a long-tailed distribution of predicates. In
+this paper, we focus on a new promising task of SGG: few-shot SGG (FSSGG).
+FSSGG encourages models to be able to quickly transfer previous knowledge and
+recognize novel predicates well with only a few examples. Although many
+advanced approaches have achieved great success on few-shot learning (FSL)
+tasks, straightforwardly extending them into FSSGG is not applicable due to two
+intrinsic characteristics of predicate concepts: 1) Each predicate category
+commonly has multiple semantic meanings under different contexts. 2) The visual
+appearance of relation triplets with the same predicate differs greatly under
+different subject-object pairs. Both issues make it hard to model conventional
+latent representations for predicate categories with state-of-the-art FSL
+methods. To this end, we propose a novel Decomposed Prototype Learning (DPL).
+Specifically, we first construct a decomposable prototype space to capture
+intrinsic visual patterns of subjects and objects for predicates, and enhance
+their feature representations with these decomposed prototypes. Then, we devise
+an intelligent metric learner to assign adaptive weights to each support sample
+by considering the relevance of their subject-object pairs. We further re-split
+the VG dataset and compare DPL with various FSL methods to benchmark this task.
+Extensive results show that DPL achieves excellent performance in both base and
+novel categories.
+"
+Supervised Masked Knowledge Distillation for Few-Shot Transformers,Han Lin,http://arxiv.org/pdf/2303.15466v2.pdf,2023-03-25,"['cs.cv', 'cs.ai']",2303.15466v2.pdf,"  Vision Transformers (ViTs) emerge to achieve impressive performance on many
+data-abundant computer vision tasks by capturing long-range dependencies among
+local features. However, under few-shot learning (FSL) settings on small
+datasets with only a few labeled data, ViT tends to overfit and suffers from
+severe performance degradation due to its absence of CNN-alike inductive bias.
+Previous works in FSL avoid such problem either through the help of
+self-supervised auxiliary losses, or through the dextile uses of label
+information under supervised settings. But the gap between self-supervised and
+supervised few-shot Transformers is still unfilled. Inspired by recent advances
+in self-supervised knowledge distillation and masked image modeling (MIM), we
+propose a novel Supervised Masked Knowledge Distillation model (SMKD) for
+few-shot Transformers which incorporates label information into
+self-distillation frameworks. Compared with previous self-supervised methods,
+we allow intra-class knowledge distillation on both class and patch tokens, and
+introduce the challenging task of masked patch tokens reconstruction across
+intra-class images. Experimental results on four few-shot classification
+benchmark datasets show that our method with simple design outperforms previous
+methods by a large margin and achieves a new start-of-the-art. Detailed
+ablation studies confirm the effectiveness of each component of our model. Code
+for this paper is available here: https://github.com/HL-hanlin/SMKD.
+"
+"Multimodal C4: An Open, Billion-scale Corpus of Images Interleaved with  Text",Wanrong Zhu,http://arxiv.org/pdf/2304.06939v3.pdf,2023-04-14,"['cs.cv', 'cs.cl']",2304.06939v3.pdf,"  In-context vision and language models like Flamingo support arbitrarily
+interleaved sequences of images and text as input. This format not only enables
+few-shot learning via interleaving independent supervised (image, text)
+examples, but also, more complex prompts involving interaction between images,
+e.g., ""What do image A and image B have in common?"" To support this interface,
+pretraining occurs over web corpora that similarly contain interleaved
+images+text. To date, however, large-scale data of this form have not been
+publicly available.
+  We release Multimodal C4, an augmentation of the popular text-only C4 corpus
+with images interleaved. We use a linear assignment algorithm to place images
+into longer bodies of text using CLIP features, a process that we show
+outperforms alternatives. Multimodal C4 spans everyday topics like cooking,
+travel, technology, etc. A manual inspection of a random sample of documents
+shows that a vast majority (88%) of images are topically relevant, and that
+linear assignment frequently selects individual sentences specifically
+well-aligned with each image (80%). After filtering NSFW images, ads, etc., the
+resulting corpus consists of 101.2M documents with 571M images interleaved in
+43B English tokens.
+"
+A Survey on Few-Shot Class-Incremental Learning,Songsong Tian,http://arxiv.org/pdf/2304.08130v2.pdf,2023-04-17,['cs.cv'],2304.08130v2.pdf,"  Large deep learning models are impressive, but they struggle when real-time
+data is not available. Few-shot class-incremental learning (FSCIL) poses a
+significant challenge for deep neural networks to learn new tasks from just a
+few labeled samples without forgetting the previously learned ones. This setup
+easily leads to catastrophic forgetting and overfitting problems, severely
+affecting model performance. Studying FSCIL helps overcome deep learning model
+limitations on data volume and acquisition time, while improving practicality
+and adaptability of machine learning models. This paper provides a
+comprehensive survey on FSCIL. Unlike previous surveys, we aim to synthesize
+few-shot learning and incremental learning, focusing on introducing FSCIL from
+two perspectives, while reviewing over 30 theoretical research studies and more
+than 20 applied research studies. From the theoretical perspective, we provide
+a novel categorization approach that divides the field into five subcategories,
+including traditional machine learning methods, meta-learning based methods,
+feature and feature space-based methods, replay-based methods, and dynamic
+network structure-based methods. We also evaluate the performance of recent
+theoretical research on benchmark datasets of FSCIL. From the application
+perspective, FSCIL has achieved impressive achievements in various fields of
+computer vision such as image classification, object detection, and image
+segmentation, as well as in natural language processing and graph. We summarize
+the important applications. Finally, we point out potential future research
+directions, including applications, problem setups, and theory development.
+Overall, this paper offers a comprehensive analysis of the latest advances in
+FSCIL from a methodological, performance, and application perspective.
+"
+Unified Quantum State Tomography and Hamiltonian Learning Using  Transformer Models: A Language-Translation-Like Approach for Quantum Systems,Zheng An,http://arxiv.org/pdf/2304.12010v1.pdf,2023-04-24,['quant-ph'],2304.12010v1.pdf,"  Schr\""odinger's equation serves as a fundamental component in characterizing
+quantum systems, wherein both quantum state tomography and Hamiltonian learning
+are instrumental in comprehending and interpreting quantum systems. While
+numerous techniques exist for carrying out state tomography and learning
+Hamiltonians individually, no method has been developed to combine these two
+aspects. In this study, we introduce a new approach that employs the attention
+mechanism in transformer models to effectively merge quantum state tomography
+and Hamiltonian learning. By carefully choosing and preparing the training
+data, our method integrates both tasks without altering the model's
+architecture, allowing the model to effectively learn the intricate
+relationships between quantum states and Hamiltonian. We also demonstrate the
+effectiveness of our approach across various quantum systems, ranging from
+simple 2-qubit cases to more involved 2D antiferromagnetic Heisenberg
+structures. The data collection process is streamlined, as it only necessitates
+a one-way generation process beginning with state tomography. Furthermore, the
+scalability and few-shot learning capabilities of our method could potentially
+minimize the resources required for characterizing and optimizing quantum
+systems. Our research provides valuable insights into the relationship between
+Hamiltonian structure and quantum system behavior, fostering opportunities for
+additional studies on quantum systems and the advancement of quantum
+computation and associated technologies.
+"
+Analogy-Forming Transformers for Few-Shot 3D Parsing,Nikolaos Gkanatsios,http://arxiv.org/pdf/2304.14382v2.pdf,2023-04-27,"['cs.cv', 'cs.ai', 'cs.lg']",2304.14382v2.pdf,"  We present Analogical Networks, a model that encodes domain knowledge
+explicitly, in a collection of structured labelled 3D scenes, in addition to
+implicitly, as model parameters, and segments 3D object scenes with analogical
+reasoning: instead of mapping a scene to part segments directly, our model
+first retrieves related scenes from memory and their corresponding part
+structures, and then predicts analogous part structures for the input scene,
+via an end-to-end learnable modulation mechanism. By conditioning on more than
+one retrieved memories, compositions of structures are predicted, that mix and
+match parts across the retrieved memories. One-shot, few-shot or many-shot
+learning are treated uniformly in Analogical Networks, by conditioning on the
+appropriate set of memories, whether taken from a single, few or many memory
+exemplars, and inferring analogous parses. We show Analogical Networks are
+competitive with state-of-the-art 3D segmentation transformers in many-shot
+settings, and outperform them, as well as existing paradigms of meta-learning
+and few-shot learning, in few-shot settings. Analogical Networks successfully
+segment instances of novel object categories simply by expanding their memory,
+without any weight updates. Our code and models are publicly available in the
+project webpage: http://analogicalnets.github.io/.
+"
+HQP: A Human-Annotated Dataset for Detecting Online Propaganda,Abdurahman Maarouf,http://arxiv.org/pdf/2304.14931v2.pdf,2023-04-28,['cs.cl'],2304.14931v2.pdf,"  Online propaganda poses a severe threat to the integrity of societies.
+However, existing datasets for detecting online propaganda have a key
+limitation: they were annotated using weak labels that can be noisy and even
+incorrect. To address this limitation, our work makes the following
+contributions: (1) We present HQP: a novel dataset (N=30,000) for detecting
+online propaganda with high-quality labels. To the best of our knowledge, HQP
+is the first dataset for detecting online propaganda that was created through
+human annotation. (2) We show empirically that state-of-the-art language models
+fail in detecting online propaganda when trained with weak labels (AUC: 64.03).
+In contrast, state-of-the-art language models can accurately detect online
+propaganda when trained with our high-quality labels (AUC: 92.25), which is an
+improvement of ~44%. (3) To address the cost of labeling, we extend our work to
+few-shot learning. Specifically, we show that prompt-based learning using a
+small sample of high-quality labels can still achieve a reasonable performance
+(AUC: 80.27). Finally, we discuss implications for the NLP community to balance
+the cost and quality of labeling. Crucially, our work highlights the importance
+of high-quality labels for sensitive NLP tasks such as propaganda detection.
+"
+Parameter-Efficient Cross-lingual Transfer of Vision and Language Models  via Translation-based Alignment,Zhen Zhang,http://arxiv.org/pdf/2305.03510v2.pdf,2023-05-02,"['cs.cl', 'cs.ai']",2305.03510v2.pdf,"  Pre-trained vision and language models such as CLIP have witnessed remarkable
+success in connecting images and texts with a primary focus on English texts.
+Despite recent efforts to extend CLIP to support other languages, disparities
+in performance among different languages have been observed due to uneven
+resource availability. Additionally, current cross-lingual transfer methods of
+those pre-trained models would consume excessive resources for a large number
+of languages. Therefore, we propose a new parameter-efficient cross-lingual
+transfer learning framework that utilizes a translation-based alignment method
+to mitigate multilingual disparities and explores parameter-efficient
+fine-tuning methods for parameter-efficient cross-lingual transfer. Extensive
+experiments on XTD and Multi30K datasets, covering 11 languages under
+zero-shot, few-shot, and full-dataset learning scenarios, show that our
+framework significantly reduces the multilingual disparities among languages
+and improves cross-lingual transfer results, especially in low-resource
+scenarios, while only keeping and fine-tuning an extremely small number of
+parameters compared to the full model (e.g., Our framework only requires 0.16\%
+additional parameters of a full-model for each language in the few-shot
+learning scenario). The codes are available at
+\url{https://github.com/eric-ai-lab/PECTVLM}. The codes are available at
+\url{https://github.com/eric-ai-lab/PECTVLM}.
+"
+CodeIE: Large Code Generation Models are Better Few-Shot Information  Extractors,Peng Li,http://arxiv.org/pdf/2305.05711v2.pdf,2023-05-09,"['cs.cl', 'cs.ai']",2305.05711v2.pdf,"  Large language models (LLMs) pre-trained on massive corpora have demonstrated
+impressive few-shot learning ability on many NLP tasks. A common practice is to
+recast the task into a text-to-text format such that generative LLMs of natural
+language (NL-LLMs) like GPT-3 can be prompted to solve it. However, it is
+nontrivial to perform information extraction (IE) tasks with NL-LLMs since the
+output of the IE task is usually structured and therefore is hard to be
+converted into plain text. In this paper, we propose to recast the structured
+output in the form of code instead of natural language and utilize generative
+LLMs of code (Code-LLMs) such as Codex to perform IE tasks, in particular,
+named entity recognition and relation extraction. In contrast to NL-LLMs, we
+show that Code-LLMs can be well-aligned with these IE tasks by designing
+code-style prompts and formulating these IE tasks as code generation tasks.
+Experiment results on seven benchmarks show that our method consistently
+outperforms fine-tuning moderate-size pre-trained models specially designed for
+IE tasks (e.g., UIE) and prompting NL-LLMs under few-shot settings. We further
+conduct a series of in-depth analyses to demonstrate the merits of leveraging
+Code-LLMs for IE tasks.
+"
+Qualifying Chinese Medical Licensing Examination with Knowledge Enhanced  Generative Pre-training Model,Jiageng Wu,http://arxiv.org/pdf/2305.10163v2.pdf,2023-05-17,"['cs.cl', 'cs.ai', 'cs.cy']",2305.10163v2.pdf,"  Generative Pre-Training (GPT) models like ChatGPT have demonstrated
+exceptional performance in various Natural Language Processing (NLP) tasks.
+Although ChatGPT has been integrated into the overall workflow to boost
+efficiency in many domains, the lack of flexibility in the finetuning process
+hinders its applications in areas that demand extensive domain expertise and
+semantic knowledge, such as healthcare. In this paper, we evaluate ChatGPT on
+the China National Medical Licensing Examination (CNMLE) and propose a novel
+approach to improve ChatGPT from two perspectives: integrating medical domain
+knowledge and enabling few-shot learning. By using a simple but effective
+retrieval method, medical background knowledge is extracted as semantic
+instructions to guide the inference of ChatGPT. Similarly, relevant medical
+questions are identified and fed as demonstrations to ChatGPT. Experimental
+results show that directly applying ChatGPT fails to qualify the CNMLE at a
+score of 51 (i.e., only 51\% of questions are answered correctly). While our
+knowledge-enhanced model achieves a high score of 70 on CNMLE-2022 which not
+only passes the qualification but also surpasses the average score of humans
+(61). This research demonstrates the potential of knowledge-enhanced ChatGPT to
+serve as versatile medical assistants, capable of analyzing real-world medical
+problems in a more accessible, user-friendly, and adaptable manner.
+"
+PointGPT: Auto-regressively Generative Pre-training from Point Clouds,Guangyan Chen,http://arxiv.org/pdf/2305.11487v2.pdf,2023-05-19,['cs.cv'],2305.11487v2.pdf,"  Large language models (LLMs) based on the generative pre-training transformer
+(GPT) have demonstrated remarkable effectiveness across a diverse range of
+downstream tasks. Inspired by the advancements of the GPT, we present PointGPT,
+a novel approach that extends the concept of GPT to point clouds, addressing
+the challenges associated with disorder properties, low information density,
+and task gaps. Specifically, a point cloud auto-regressive generation task is
+proposed to pre-train transformer models. Our method partitions the input point
+cloud into multiple point patches and arranges them in an ordered sequence
+based on their spatial proximity. Then, an extractor-generator based
+transformer decoder, with a dual masking strategy, learns latent
+representations conditioned on the preceding point patches, aiming to predict
+the next one in an auto-regressive manner. Our scalable approach allows for
+learning high-capacity models that generalize well, achieving state-of-the-art
+performance on various downstream tasks. In particular, our approach achieves
+classification accuracies of 94.9% on the ModelNet40 dataset and 93.4% on the
+ScanObjectNN dataset, outperforming all other transformer models. Furthermore,
+our method also attains new state-of-the-art accuracies on all four few-shot
+learning benchmarks.
+"
+A Survey of Diffusion Models in Natural Language Processing,Hao Zou,http://arxiv.org/pdf/2305.14671v2.pdf,2023-05-24,['cs.cl'],2305.14671v2.pdf,"  This survey paper provides a comprehensive review of the use of diffusion
+models in natural language processing (NLP). Diffusion models are a class of
+mathematical models that aim to capture the diffusion of information or signals
+across a network or manifold. In NLP, diffusion models have been used in a
+variety of applications, such as natural language generation, sentiment
+analysis, topic modeling, and machine translation. This paper discusses the
+different formulations of diffusion models used in NLP, their strengths and
+limitations, and their applications. We also perform a thorough comparison
+between diffusion models and alternative generative models, specifically
+highlighting the autoregressive (AR) models, while also examining how diverse
+architectures incorporate the Transformer in conjunction with diffusion models.
+Compared to AR models, diffusion models have significant advantages for
+parallel generation, text interpolation, token-level controls such as syntactic
+structures and semantic contents, and robustness. Exploring further
+permutations of integrating Transformers into diffusion models would be a
+valuable pursuit. Also, the development of multimodal diffusion models and
+large-scale diffusion language models with notable capabilities for few-shot
+learning would be important directions for the future advance of diffusion
+models in NLP.
+"
+Benchmarking Arabic AI with Large Language Models,Ahmed Abdelali,http://arxiv.org/pdf/2305.14982v1.pdf,2023-05-24,"['cs.cl', 'cs.ai', '68t50', 'f.2.2; i.2.7']",2305.14982v1.pdf,"  With large Foundation Models (FMs), language technologies (AI in general) are
+entering a new paradigm: eliminating the need for developing large-scale
+task-specific datasets and supporting a variety of tasks through set-ups
+ranging from zero-shot to few-shot learning. However, understanding FMs
+capabilities requires a systematic benchmarking effort by comparing FMs
+performance with the state-of-the-art (SOTA) task-specific models. With that
+goal, past work focused on the English language and included a few efforts with
+multiple languages. Our study contributes to ongoing research by evaluating FMs
+performance for standard Arabic NLP and Speech processing, including a range of
+tasks from sequence tagging to content classification across diverse domains.
+We start with zero-shot learning using GPT-3.5-turbo, Whisper, and USM,
+addressing 33 unique tasks using 59 publicly available datasets resulting in 96
+test setups. For a few tasks, FMs performs on par or exceeds the performance of
+the SOTA models but for the majority it under-performs. Given the importance of
+prompt for the FMs performance, we discuss our prompt strategies in detail and
+elaborate on our findings. Our future work on Arabic AI will explore few-shot
+prompting, expand the range of tasks, and investigate additional open-source
+models.
+"
+Sentiment Analysis in the Era of Large Language Models: A Reality Check,Wenxuan Zhang,http://arxiv.org/pdf/2305.15005v1.pdf,2023-05-24,['cs.cl'],2305.15005v1.pdf,"  Sentiment analysis (SA) has been a long-standing research area in natural
+language processing. It can offer rich insights into human sentiments and
+opinions and has thus seen considerable interest from both academia and
+industry. With the advent of large language models (LLMs) such as ChatGPT,
+there is a great potential for their employment on SA problems. However, the
+extent to which existing LLMs can be leveraged for different sentiment analysis
+tasks remains unclear. This paper aims to provide a comprehensive investigation
+into the capabilities of LLMs in performing various sentiment analysis tasks,
+from conventional sentiment classification to aspect-based sentiment analysis
+and multifaceted analysis of subjective texts. We evaluate performance across
+13 tasks on 26 datasets and compare the results against small language models
+(SLMs) trained on domain-specific datasets. Our study reveals that while LLMs
+demonstrate satisfactory performance in simpler tasks, they lag behind in more
+complex tasks requiring deeper understanding or structured sentiment
+information. However, LLMs significantly outperform SLMs in few-shot learning
+settings, suggesting their potential when annotation resources are limited. We
+also highlight the limitations of current evaluation practices in assessing
+LLMs' SA abilities and propose a novel benchmark, \textsc{SentiEval}, for a
+more comprehensive and realistic evaluation. Data and code during our
+investigations are available at
+\url{https://github.com/DAMO-NLP-SG/LLM-Sentiment}.
+"
+Impact of Large Language Models on Generating Software Specifications,Danning Xie,http://arxiv.org/pdf/2306.03324v2.pdf,2023-06-06,['cs.se'],2306.03324v2.pdf,"  Software specifications are essential for ensuring the reliability of
+software systems. Existing specification extraction approaches, however, suffer
+from limited generalizability and require manual efforts. The recent emergence
+of Large Language Models (LLMs), which have been successfully applied to
+numerous software engineering tasks, offers a promising avenue for automating
+this process. In this paper, we conduct the first empirical study to evaluate
+the capabilities of LLMs for generating software specifications from software
+comments or documentation. We evaluate LLMs' performance with Few Shot Learning
+(FSL), enabling LLMs to generalize from a small number of examples, as well as
+different prompt construction strategies, and compare the performance of LLMs
+with traditional approaches. Additionally, we conduct a comparative diagnosis
+of the failure cases from both LLMs and traditional methods, identifying their
+unique strengths and weaknesses. Lastly, we conduct extensive experiments on 15
+state of the art LLMs, evaluating their performance and cost effectiveness for
+generating software specifications.
+  Our results show that with FSL, LLMs outperform traditional methods (by
+5.6%), and more sophisticated prompt construction strategies can further
+enlarge this performance gap (up to 5.1 to 10.0%). Yet, LLMs suffer from their
+unique challenges, such as ineffective prompts and the lack of domain
+knowledge, which together account for 53 to 60% of LLM unique failures. The
+strong performance of open source models (e.g., StarCoder) makes closed source
+models (e.g., GPT 3 Davinci) less desirable due to size and cost. Our study
+offers valuable insights for future research to improve specification
+generation.
+"
+One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning,Arnav Chavan,http://arxiv.org/pdf/2306.07967v2.pdf,2023-06-13,"['cs.lg', 'cs.ai', 'cs.cv']",2306.07967v2.pdf,"  We present Generalized LoRA (GLoRA), an advanced approach for universal
+parameter-efficient fine-tuning tasks. Enhancing Low-Rank Adaptation (LoRA),
+GLoRA employs a generalized prompt module to optimize pre-trained model weights
+and adjust intermediate activations, providing more flexibility and capability
+across diverse tasks and datasets. Moreover, GLoRA facilitates efficient
+parameter adaptation by employing a scalable, modular, layer-wise structure
+search that learns individual adapter of each layer. Originating from a unified
+mathematical formulation, GLoRA exhibits strong transfer learning, few-shot
+learning and domain generalization abilities, as it adapts to new tasks through
+not only weights but also additional dimensions like activations. Comprehensive
+experiments demonstrate that GLoRA outperforms all previous methods in natural,
+specialized, and structured vision benchmarks, achieving superior accuracy with
+fewer parameters and computations. The proposed method on LLaMA-1 and LLaMA-2
+also show considerable enhancements compared to the original LoRA in the
+language domain. Furthermore, our structural re-parameterization design ensures
+that GLoRA incurs no extra inference cost, rendering it a practical solution
+for resource-limited applications. Code and models are available at:
+https://github.com/Arnav0400/ViT-Slim/tree/master/GLoRA.
+"
+Democratizing LLMs for Low-Resource Languages by Leveraging their  English Dominant Abilities with Linguistically-Diverse Prompts,Xuan-Phi Nguyen,http://arxiv.org/pdf/2306.11372v1.pdf,2023-06-20,"['cs.cl', 'cs.ai']",2306.11372v1.pdf,"  Large language models (LLMs) are known to effectively perform tasks by simply
+observing few exemplars. However, in low-resource languages, obtaining such
+hand-picked exemplars can still be challenging, where unsupervised techniques
+may be necessary. Moreover, competent generative capabilities of LLMs are
+observed only in high-resource languages, while their performances among
+under-represented languages fall behind due to pre-training data imbalance. To
+elicit LLMs' ability onto low-resource languages without any supervised data,
+we propose to assemble synthetic exemplars from a diverse set of high-resource
+languages to prompt the LLMs to translate from any language into English. These
+prompts are then used to create intra-lingual exemplars to perform tasks in the
+target languages. Our unsupervised prompting method performs on par with
+supervised few-shot learning in LLMs of different sizes for translations
+between English and 13 Indic and 21 African low-resource languages. We also
+show that fine-tuning a 7B model on data generated from our method helps it
+perform competitively with a 175B model. In non-English translation tasks, our
+method even outperforms supervised prompting by up to 3 chrF++ in many
+low-resource languages. When evaluated on zero-shot multilingual summarization,
+our method surpasses other English-pivoting baselines by up to 4 ROUGE-L and is
+also favored by GPT-4.
+"
+ProtoDiff: Learning to Learn Prototypical Networks by Task-Guided  Diffusion,Yingjun Du,http://arxiv.org/pdf/2306.14770v2.pdf,2023-06-26,"['cs.lg', 'cs.ai']",2306.14770v2.pdf,"  Prototype-based meta-learning has emerged as a powerful technique for
+addressing few-shot learning challenges. However, estimating a deterministic
+prototype using a simple average function from a limited number of examples
+remains a fragile process. To overcome this limitation, we introduce ProtoDiff,
+a novel framework that leverages a task-guided diffusion model during the
+meta-training phase to gradually generate prototypes, thereby providing
+efficient class representations. Specifically, a set of prototypes is optimized
+to achieve per-task prototype overfitting, enabling accurately obtaining the
+overfitted prototypes for individual tasks. Furthermore, we introduce a
+task-guided diffusion process within the prototype space, enabling the
+meta-learning of a generative process that transitions from a vanilla prototype
+to an overfitted prototype. ProtoDiff gradually generates task-specific
+prototypes from random noise during the meta-test stage, conditioned on the
+limited samples available for the new task. Furthermore, to expedite training
+and enhance ProtoDiff's performance, we propose the utilization of residual
+prototype learning, which leverages the sparsity of the residual prototype. We
+conduct thorough ablation studies to demonstrate its ability to accurately
+capture the underlying prototype distribution and enhance generalization. The
+new state-of-the-art performance on within-domain, cross-domain, and few-task
+few-shot classification further substantiates the benefit of ProtoDiff.
+"
+Effective Transfer of Pretrained Large Visual Model for Fabric Defect  Segmentation via Specifc Knowledge Injection,Zhewei Chen,http://arxiv.org/pdf/2306.16186v1.pdf,2023-06-28,"['cs.cv', 'cs.ai', 'i.2.10; i.4.9; i.5.4']",2306.16186v1.pdf,"  Fabric defect segmentation is integral to textile quality control. Despite
+this, the scarcity of high-quality annotated data and the diversity of fabric
+defects present significant challenges to the application of deep learning in
+this field. These factors limit the generalization and segmentation performance
+of existing models, impeding their ability to handle the complexity of diverse
+fabric types and defects. To overcome these obstacles, this study introduces an
+innovative method to infuse specialized knowledge of fabric defects into the
+Segment Anything Model (SAM), a large-scale visual model. By introducing and
+training a unique set of fabric defect-related parameters, this approach
+seamlessly integrates domain-specific knowledge into SAM without the need for
+extensive modifications to the pre-existing model parameters. The revamped SAM
+model leverages generalized image understanding learned from large-scale
+natural image datasets while incorporating fabric defect-specific knowledge,
+ensuring its proficiency in fabric defect segmentation tasks. The experimental
+results reveal a significant improvement in the model's segmentation
+performance, attributable to this novel amalgamation of generic and
+fabric-specific knowledge. When benchmarking against popular existing
+segmentation models across three datasets, our proposed model demonstrates a
+substantial leap in performance. Its impressive results in cross-dataset
+comparisons and few-shot learning experiments further demonstrate its potential
+for practical applications in textile quality control.
+"
+Prompting classes: Exploring the Power of Prompt Class Learning in  Weakly Supervised Semantic Segmentation,Balamurali Murugesan,http://arxiv.org/pdf/2307.00097v2.pdf,2023-06-30,['cs.cv'],2307.00097v2.pdf,"  Recently, CLIP-based approaches have exhibited remarkable performance on
+generalization and few-shot learning tasks, fueled by the power of contrastive
+language-vision pre-training. In particular, prompt tuning has emerged as an
+effective strategy to adapt the pre-trained language-vision models to
+downstream tasks by employing task-related textual tokens. Motivated by this
+progress, in this work we question whether other fundamental problems, such as
+weakly supervised semantic segmentation (WSSS), can benefit from prompt tuning.
+Our findings reveal two interesting observations that shed light on the impact
+of prompt tuning on WSSS. First, modifying only the class token of the text
+prompt results in a greater impact on the Class Activation Map (CAM), compared
+to arguably more complex strategies that optimize the context. And second, the
+class token associated with the image ground truth does not necessarily
+correspond to the category that yields the best CAM. Motivated by these
+observations, we introduce a novel approach based on a PrOmpt cLass lEarning
+(POLE) strategy. Through extensive experiments we demonstrate that our simple,
+yet efficient approach achieves SOTA performance in a well-known WSSS
+benchmark. These results highlight not only the benefits of language-vision
+models in WSSS but also the potential of prompt learning for this problem. The
+code is available at https://github.com/rB080/WSS_POLE.
+"
+Meta-training with Demonstration Retrieval for Efficient Few-shot  Learning,Aaron Mueller,http://arxiv.org/pdf/2307.00119v1.pdf,2023-06-30,['cs.cl'],2307.00119v1.pdf,"  Large language models show impressive results on few-shot NLP tasks. However,
+these models are memory and computation-intensive. Meta-training allows one to
+leverage smaller models for few-shot generalization in a domain-general and
+task-agnostic manner; however, these methods alone results in models that may
+not have sufficient parameterization or knowledge to adapt quickly to a large
+variety of tasks. To overcome this issue, we propose meta-training with
+demonstration retrieval, where we use a dense passage retriever to retrieve
+semantically similar labeled demonstrations to each example for more varied
+supervision. By separating external knowledge from model parameters, we can use
+meta-training to train parameter-efficient models that generalize well on a
+larger variety of tasks. We construct a meta-training set from UnifiedQA and
+CrossFit, and propose a demonstration bank based on UnifiedQA tasks. To our
+knowledge, our work is the first to combine retrieval with meta-training, to
+use DPR models to retrieve demonstrations, and to leverage demonstrations from
+many tasks simultaneously, rather than randomly sampling demonstrations from
+the training set of the target task. Our approach outperforms a variety of
+targeted parameter-efficient and retrieval-augmented few-shot methods on QA,
+NLI, and text classification tasks (including SQuAD, QNLI, and TREC). Our
+approach can be meta-trained and fine-tuned quickly on a single GPU.
+"
+TablEye: Seeing small Tables through the Lens of Images,Seung-eon Lee,http://arxiv.org/pdf/2307.02491v1.pdf,2023-07-04,"['cs.lg', 'cs.ai']",2307.02491v1.pdf,"  The exploration of few-shot tabular learning becomes imperative. Tabular data
+is a versatile representation that captures diverse information, yet it is not
+exempt from limitations, property of data and model size. Labeling extensive
+tabular data can be challenging, and it may not be feasible to capture every
+important feature. Few-shot tabular learning, however, remains relatively
+unexplored, primarily due to scarcity of shared information among independent
+datasets and the inherent ambiguity in defining boundaries within tabular data.
+To the best of our knowledge, no meaningful and unrestricted few-shot tabular
+learning techniques have been developed without imposing constraints on the
+dataset. In this paper, we propose an innovative framework called TablEye,
+which aims to overcome the limit of forming prior knowledge for tabular data by
+adopting domain transformation. It facilitates domain transformation by
+generating tabular images, which effectively conserve the intrinsic semantics
+of the original tabular data. This approach harnesses rigorously tested
+few-shot learning algorithms and embedding functions to acquire and apply prior
+knowledge. Leveraging shared data domains allows us to utilize this prior
+knowledge, originally learned from the image domain. Specifically, TablEye
+demonstrated a superior performance by outstripping the TabLLM in a 4-shot task
+with a maximum 0.11 AUC and a STUNT in a 1- shot setting, where it led on
+average by 3.17% accuracy.
+"
+Text Descriptions are Compressive and Invariant Representations for  Visual Learning,Zhili Feng,http://arxiv.org/pdf/2307.04317v2.pdf,2023-07-10,"['cs.cv', 'cs.lg']",2307.04317v2.pdf,"  Modern image classification is based upon directly predicting classes via
+large discriminative networks, which do not directly contain information about
+the intuitive visual features that may constitute a classification decision.
+Recently, work in vision-language models (VLM) such as CLIP has provided ways
+to specify natural language descriptions of image classes, but typically
+focuses on providing single descriptions for each class. In this work, we
+demonstrate that an alternative approach, in line with humans' understanding of
+multiple visual features per class, can also provide compelling performance in
+the robust few-shot learning setting. In particular, we introduce a novel
+method, \textit{SLR-AVD (Sparse Logistic Regression using Augmented Visual
+Descriptors)}. This method first automatically generates multiple visual
+descriptions of each class via a large language model (LLM), then uses a VLM to
+translate these descriptions to a set of visual feature embeddings of each
+image, and finally uses sparse logistic regression to select a relevant subset
+of these features to classify each image. Core to our approach is the fact
+that, information-theoretically, these descriptive features are more invariant
+to domain shift than traditional image embeddings, even though the VLM training
+process is not explicitly designed for invariant representation learning. These
+invariant descriptive features also compose a better input compression scheme.
+When combined with finetuning, we show that SLR-AVD is able to outperform
+existing state-of-the-art finetuning approaches on both in-distribution and
+out-of-distribution performance.
+"
+DialogStudio: Towards Richest and Most Diverse Unified Dataset  Collection for Conversational AI,Jianguo Zhang,http://arxiv.org/pdf/2307.10172v2.pdf,2023-07-19,"['cs.cl', 'cs.ai']",2307.10172v2.pdf,"  Despite advancements in conversational AI, language models encounter
+challenges to handle diverse conversational tasks, and existing dialogue
+dataset collections often lack diversity and comprehensiveness. To tackle these
+issues, we introduce DialogStudio: the largest and most diverse collection of
+dialogue datasets, unified under a consistent format while preserving their
+original information. Our collection encompasses data from open-domain
+dialogues, task-oriented dialogues, natural language understanding,
+conversational recommendation, dialogue summarization, and knowledge-grounded
+dialogues, making it an incredibly rich and diverse resource for dialogue
+research and model training. To further enhance the utility of DialogStudio, we
+identify the licenses for each dataset and design domain-aware prompts for
+selected dialogues to facilitate instruction-aware fine-tuning. Furthermore, we
+develop conversational AI models using the dataset collection, and our
+experiments in both zero-shot and few-shot learning scenarios demonstrate the
+superiority of DialogStudio. To improve transparency and support dataset and
+task-based research, as well as language model pre-training, all datasets,
+licenses, codes, and models associated with DialogStudio are made publicly
+accessible at https://github.com/salesforce/DialogStudio
+"
+Mutual Reinforcement Effects in Japanese Sentence Classification and  Named Entity Recognition Tasks,Chengguang Gan,http://arxiv.org/pdf/2307.10291v2.pdf,2023-07-18,['cs.cl'],2307.10291v2.pdf,"  Information extraction(IE) is a crucial subfield within natural language
+processing. However, for the traditionally segmented approach to sentence
+classification and Named Entity Recognition, the intricate interactions between
+these individual subtasks remain largely uninvestigated. In this study, we
+propose an integrative analysis, converging sentence classification with Named
+Entity Recognition, with the objective to unveil and comprehend the mutual
+reinforcement effect within these two information extraction subtasks. To
+achieve this, we introduce a Sentence Classification and Named Entity
+Recognition Multi-task (SCNM) approach that combines Sentence Classification
+(SC) and Named Entity Recognition (NER). We develop a Sentence-to-Label
+Generation (SLG) framework for SCNM and construct a Wikipedia dataset
+containing both SC and NER. Using a format converter, we unify input formats
+and employ a generative model to generate SC-labels, NER-labels, and associated
+text segments. We propose a Constraint Mechanism (CM) to improve generated
+format accuracy. Our results show SC accuracy increased by 1.13 points and NER
+by 1.06 points in SCNM compared to standalone tasks, with CM raising format
+accuracy from 63.61 to 100. The findings indicate mutual reinforcement effects
+between SC and NER, and integration enhances both tasks' performance. We
+additionally implemented the SLG framework on single SC task. It yielded
+superior accuracies compared to the baseline on two distinct Japanese SC
+datasets. Notably, in the experiment of few-shot learning, SLG framework shows
+much better performance than fine-tune method. These empirical findings
+contribute additional evidence to affirm the efficacy of the SLG framework.
+"
+CohortGPT: An Enhanced GPT for Participant Recruitment in Clinical Study,Zihan Guan,http://arxiv.org/pdf/2307.11346v1.pdf,2023-07-21,"['cs.cl', 'cs.ai']",2307.11346v1.pdf,"  Participant recruitment based on unstructured medical texts such as clinical
+notes and radiology reports has been a challenging yet important task for the
+cohort establishment in clinical research. Recently, Large Language Models
+(LLMs) such as ChatGPT have achieved tremendous success in various downstream
+tasks thanks to their promising performance in language understanding,
+inference, and generation. It is then natural to test their feasibility in
+solving the cohort recruitment task, which involves the classification of a
+given paragraph of medical text into disease label(s). However, when applied to
+knowledge-intensive problem settings such as medical text classification, where
+the LLMs are expected to understand the decision made by human experts and
+accurately identify the implied disease labels, the LLMs show a mediocre
+performance. A possible explanation is that, by only using the medical text,
+the LLMs neglect to use the rich context of additional information that
+languages afford. To this end, we propose to use a knowledge graph as auxiliary
+information to guide the LLMs in making predictions. Moreover, to further boost
+the LLMs adapt to the problem setting, we apply a chain-of-thought (CoT) sample
+selection strategy enhanced by reinforcement learning, which selects a set of
+CoT samples given each individual medical report. Experimental results and
+various ablation studies show that our few-shot learning method achieves
+satisfactory performance compared with fine-tuning strategies and gains superb
+advantages when the available data is limited. The code and sample dataset of
+the proposed CohortGPT model is available at:
+https://anonymous.4open.science/r/CohortGPT-4872/
+"
+Identifying Misinformation on YouTube through Transcript Contextual  Analysis with Transformer Models,Christos Christodoulou,http://arxiv.org/pdf/2307.12155v1.pdf,2023-07-22,['cs.cl'],2307.12155v1.pdf,"  Misinformation on YouTube is a significant concern, necessitating robust
+detection strategies. In this paper, we introduce a novel methodology for video
+classification, focusing on the veracity of the content. We convert the
+conventional video classification task into a text classification task by
+leveraging the textual content derived from the video transcripts. We employ
+advanced machine learning techniques like transfer learning to solve the
+classification challenge. Our approach incorporates two forms of transfer
+learning: (a) fine-tuning base transformer models such as BERT, RoBERTa, and
+ELECTRA, and (b) few-shot learning using sentence-transformers MPNet and
+RoBERTa-large. We apply the trained models to three datasets: (a) YouTube
+Vaccine-misinformation related videos, (b) YouTube Pseudoscience videos, and
+(c) Fake-News dataset (a collection of articles). Including the Fake-News
+dataset extended the evaluation of our approach beyond YouTube videos. Using
+these datasets, we evaluated the models distinguishing valid information from
+misinformation. The fine-tuned models yielded Matthews Correlation
+Coefficient>0.81, accuracy>0.90, and F1 score>0.90 in two of three datasets.
+Interestingly, the few-shot models outperformed the fine-tuned ones by 20% in
+both Accuracy and F1 score for the YouTube Pseudoscience dataset, highlighting
+the potential utility of this approach -- especially in the context of limited
+training data.
+"
+ChatGPT for Arabic Grammatical Error Correction,Sang Yun Kwon,http://arxiv.org/pdf/2308.04492v1.pdf,2023-08-08,['cs.ai'],2308.04492v1.pdf,"  Recently, large language models (LLMs) fine-tuned to follow human instruction
+have exhibited significant capabilities in various English NLP tasks. However,
+their performance in grammatical error correction (GEC) tasks, particularly in
+non-English languages, remains significantly unexplored. In this paper, we
+delve into abilities of instruction fine-tuned LLMs in Arabic GEC, a task made
+complex due to Arabic's rich morphology. Our findings suggest that various
+prompting methods, coupled with (in-context) few-shot learning, demonstrate
+considerable effectiveness, with GPT-4 achieving up to $65.49$
+F\textsubscript{1} score under expert prompting (approximately $5$ points
+higher than our established baseline). This highlights the potential of LLMs in
+low-resource settings, offering a viable approach for generating useful
+synthetic data for model training. Despite these positive results, we find that
+instruction fine-tuned models, regardless of their size, significantly
+underperform compared to fully fine-tuned models of significantly smaller
+sizes. This disparity highlights a substantial room for improvements for LLMs.
+Inspired by methods from low-resource machine translation, we also develop a
+method exploiting synthetic data that significantly outperforms previous models
+on two standard Arabic benchmarks. Our work sets new SoTA for Arabic GEC, with
+$72.19\%$ and $73.26$ F$_{1}$ on the 2014 and 2015 QALB datasets, respectively.
+"
+LLMeBench: A Flexible Framework for Accelerating LLMs Benchmarking,Fahim Dalvi,http://arxiv.org/pdf/2308.04945v1.pdf,2023-08-09,"['cs.cl', 'cs.ai', '68t50', 'f.2.2; i.2.7']",2308.04945v1.pdf,"  The recent development and success of Large Language Models (LLMs)
+necessitate an evaluation of their performance across diverse NLP tasks in
+different languages. Although several frameworks have been developed and made
+publicly available, their customization capabilities for specific tasks and
+datasets are often complex for different users. In this study, we introduce the
+LLMeBench framework. Initially developed to evaluate Arabic NLP tasks using
+OpenAI's GPT and BLOOM models; it can be seamlessly customized for any NLP task
+and model, regardless of language. The framework also features zero- and
+few-shot learning settings. A new custom dataset can be added in less than 10
+minutes, and users can use their own model API keys to evaluate the task at
+hand. The developed framework has been already tested on 31 unique NLP tasks
+using 53 publicly available datasets within 90 experimental setups, involving
+approximately 296K data points. We plan to open-source the framework for the
+community (https://github.com/qcri/LLMeBench/). A video demonstrating the
+framework is available online (https://youtu.be/FkQn4UjYA0s).
+"
+Link-Context Learning for Multimodal LLMs,Yan Tai,http://arxiv.org/pdf/2308.07891v1.pdf,2023-08-15,"['cs.cv', 'cs.cl']",2308.07891v1.pdf,"  The ability to learn from context with novel concepts, and deliver
+appropriate responses are essential in human conversations. Despite current
+Multimodal Large Language Models (MLLMs) and Large Language Models (LLMs) being
+trained on mega-scale datasets, recognizing unseen images or understanding
+novel concepts in a training-free manner remains a challenge. In-Context
+Learning (ICL) explores training-free few-shot learning, where models are
+encouraged to ``learn to learn"" from limited tasks and generalize to unseen
+tasks. In this work, we propose link-context learning (LCL), which emphasizes
+""reasoning from cause and effect"" to augment the learning capabilities of
+MLLMs. LCL goes beyond traditional ICL by explicitly strengthening the causal
+relationship between the support set and the query set. By providing
+demonstrations with causal links, LCL guides the model to discern not only the
+analogy but also the underlying causal associations between data points, which
+empowers MLLMs to recognize unseen images and understand novel concepts more
+effectively. To facilitate the evaluation of this novel approach, we introduce
+the ISEKAI dataset, comprising exclusively of unseen generated image-label
+pairs designed for link-context learning. Extensive experiments show that our
+LCL-MLLM exhibits strong link-context learning capabilities to novel concepts
+over vanilla MLLMs. Code and data will be released at
+https://github.com/isekai-portal/Link-Context-Learning.
+"
+CodeCoT and Beyond: Learning to Program and Test like a Developer,Dong Huang,http://arxiv.org/pdf/2308.08784v1.pdf,2023-08-17,"['cs.se', 'cs.ai']",2308.08784v1.pdf,"  In natural language processing, transformer-based large language models
+(LLMs) like GPT-x models developed by OpenAI have revolutionized the landscape.
+Despite their impressive capabilities, these models often encounter challenges
+when handling tasks that differ from their training data, resulting in
+compromised performance. To address this, few-shot learning has emerged as a
+valuable technique, allowing LLMs to adapt with minimal task-specific data. One
+innovative strategy, known as Chain-of-Thought Prompting (CoT), has been
+introduced to guide LLMs in revealing cognitive processes during multi-step
+reasoning. In this paper, we propose Code Chain-of-Thought~(CodeCoT), which
+consists of two components: the Vanilla CodeCoT and the Self-exam CodeCoT. The
+latter incorporates self-examination, empowering the model to iteratively
+generate code, formulate test cases, and refine its outputs. Specifically, the
+process entails the generation of test examples by the model corresponding to
+the code it is tasked to implement. If it fails on the test examples, then it
+regenerates the code based on the erroneous code and associated error types.
+Through comprehensive experiments, we observed that both techniques
+significantly enhance code generation accuracy across various LLM variants. Our
+evaluation results reveal that CodeCoT improves the code generation
+effectiveness, including an unprecedented pass@1 accuracy of 79.27\% using the
+Self-exam CodeCoT approach on the gpt-3.5-turbo-0613 model in the HumanEval
+dataset.
+"
+Large Language Models Vote: Prompting for Rare Disease Identification,David Oniani,http://arxiv.org/pdf/2308.12890v2.pdf,2023-08-24,"['cs.cl', 'cs.ai']",2308.12890v2.pdf,"  The emergence of generative Large Language Models (LLMs) emphasizes the need
+for accurate and efficient prompting approaches. LLMs are often applied in
+Few-Shot Learning (FSL) contexts, where tasks are executed with minimal
+training data. FSL has become popular in many Artificial Intelligence (AI)
+subdomains, including AI for health. Rare diseases affect a small fraction of
+the population. Rare disease identification from clinical notes inherently
+requires FSL techniques due to limited data availability. Manual data
+collection and annotation is both expensive and time-consuming. In this paper,
+we propose Models-Vote Prompting (MVP), a flexible prompting approach for
+improving the performance of LLM queries in FSL settings. MVP works by
+prompting numerous LLMs to perform the same tasks and then conducting a
+majority vote on the resulting outputs. This method achieves improved results
+to any one model in the ensemble on one-shot rare disease identification and
+classification tasks. We also release a novel rare disease dataset for FSL,
+available to those who signed the MIMIC-IV Data Use Agreement (DUA).
+Furthermore, in using MVP, each model is prompted multiple times, substantially
+increasing the time needed for manual annotation, and to address this, we
+assess the feasibility of using JSON for automating generative LLM evaluation.
+"
+Diagnosing Infeasible Optimization Problems Using Large Language Models,Hao Chen,http://arxiv.org/pdf/2308.12923v1.pdf,2023-08-23,"['cs.hc', 'cs.cl', 'cs.lg', 'math.oc']",2308.12923v1.pdf,"  Decision-making problems can be represented as mathematical optimization
+models, finding wide applications in fields such as economics, engineering and
+manufacturing, transportation, and health care. Optimization models are
+mathematical abstractions of the problem of making the best decision while
+satisfying a set of requirements or constraints. One of the primary barriers to
+deploying these models in practice is the challenge of helping practitioners
+understand and interpret such models, particularly when they are infeasible,
+meaning no decision satisfies all the constraints. Existing methods for
+diagnosing infeasible optimization models often rely on expert systems,
+necessitating significant background knowledge in optimization. In this paper,
+we introduce OptiChat, a first-of-its-kind natural language-based system
+equipped with a chatbot GUI for engaging in interactive conversations about
+infeasible optimization models. OptiChat can provide natural language
+descriptions of the optimization model itself, identify potential sources of
+infeasibility, and offer suggestions to make the model feasible. The
+implementation of OptiChat is built on GPT-4, which interfaces with an
+optimization solver to identify the minimal subset of constraints that render
+the entire optimization problem infeasible, also known as the Irreducible
+Infeasible Subset (IIS). We utilize few-shot learning, expert chain-of-thought,
+key-retrieve, and sentiment prompts to enhance OptiChat's reliability. Our
+experiments demonstrate that OptiChat assists both expert and non-expert users
+in improving their understanding of the optimization models, enabling them to
+quickly identify the sources of infeasibility.
+"
+Less is More: Towards Efficient Few-shot 3D Semantic Segmentation via  Training-free Networks,Xiangyang Zhu,http://arxiv.org/pdf/2308.12961v1.pdf,2023-08-24,['cs.cv'],2308.12961v1.pdf,"  To reduce the reliance on large-scale datasets, recent works in 3D
+segmentation resort to few-shot learning. Current 3D few-shot semantic
+segmentation methods first pre-train the models on `seen' classes, and then
+evaluate their generalization performance on `unseen' classes. However, the
+prior pre-training stage not only introduces excessive time overhead, but also
+incurs a significant domain gap on `unseen' classes. To tackle these issues, we
+propose an efficient Training-free Few-shot 3D Segmentation netwrok, TFS3D, and
+a further training-based variant, TFS3D-T. Without any learnable parameters,
+TFS3D extracts dense representations by trigonometric positional encodings, and
+achieves comparable performance to previous training-based methods. Due to the
+elimination of pre-training, TFS3D can alleviate the domain gap issue and save
+a substantial amount of time. Building upon TFS3D, TFS3D-T only requires to
+train a lightweight query-support transferring attention (QUEST), which
+enhances the interaction between the few-shot query and support data.
+Experiments demonstrate TFS3D-T improves previous state-of-the-art methods by
++6.93% and +17.96% mIoU respectively on S3DIS and ScanNet, while reducing the
+training time by -90%, indicating superior effectiveness and efficiency.
+"
+"LongBench: A Bilingual, Multitask Benchmark for Long Context  Understanding",Yushi Bai,http://arxiv.org/pdf/2308.14508v1.pdf,2023-08-28,['cs.cl'],2308.14508v1.pdf,"  Although large language models (LLMs) demonstrate impressive performance for
+many language tasks, most of them can only handle texts a few thousand tokens
+long, limiting their applications on longer sequence inputs, such as books,
+reports, and codebases. Recent works have proposed methods to improve LLMs'
+long context capabilities by extending context windows and more sophisticated
+memory mechanisms. However, comprehensive benchmarks tailored for evaluating
+long context understanding are lacking. In this paper, we introduce LongBench,
+the first bilingual, multi-task benchmark for long context understanding,
+enabling a more rigorous evaluation of long context understanding. LongBench
+comprises 21 datasets across 6 task categories in both English and Chinese,
+with an average length of 6,711 words (English) and 13,386 characters
+(Chinese). These tasks cover key long-text application areas including
+single-doc QA, multi-doc QA, summarization, few-shot learning, synthetic tasks,
+and code completion. All datasets in LongBench are standardized into a unified
+format, allowing for effortless automatic evaluation of LLMs. Upon
+comprehensive evaluation of 8 LLMs on LongBench, we find that: (1) Commercial
+model (GPT-3.5-Turbo-16k) outperforms other open-sourced models, but still
+struggles on longer contexts. (2) Scaled position embedding and fine-tuning on
+longer sequences lead to substantial improvement on long context understanding.
+(3) Context compression technique such as retrieval brings improvement for
+model with weak ability on long contexts, but the performance still lags behind
+models that have strong long context understanding capability. The code and
+datasets are available at https://github.com/THUDM/LongBench.
+"
+TransPrompt v2: A Transferable Prompting Framework for Cross-task Text  Classification,Jianing Wang,http://arxiv.org/pdf/2308.15010v1.pdf,2023-08-29,['cs.cl'],2308.15010v1.pdf,"  Text classification is one of the most imperative tasks in natural language
+processing (NLP). Recent advances with pre-trained language models (PLMs) have
+shown remarkable success on this task. However, the satisfying results obtained
+by PLMs heavily depend on the large amounts of task-specific labeled data,
+which may not be feasible in many application scenarios due to data access and
+privacy constraints. The recently-proposed prompt-based fine-tuning paradigm
+improves the performance of PLMs for few-shot text classification with
+task-specific templates. Yet, it is unclear how the prompting knowledge can be
+transferred across tasks, for the purpose of mutual reinforcement. We propose
+TransPrompt v2, a novel transferable prompting framework for few-shot learning
+across similar or distant text classification tasks. For learning across
+similar tasks, we employ a multi-task meta-knowledge acquisition (MMA)
+procedure to train a meta-learner that captures the cross-task transferable
+knowledge. For learning across distant tasks, we further inject the task type
+descriptions into the prompt, and capture the intra-type and inter-type prompt
+embeddings among multiple distant tasks. Additionally, two de-biasing
+techniques are further designed to make the trained meta-learner more
+task-agnostic and unbiased towards any tasks. After that, the meta-learner can
+be adapted to each specific task with better parameters initialization.
+Extensive experiments show that TransPrompt v2 outperforms single-task and
+cross-task strong baselines over multiple NLP tasks and datasets. We further
+show that the meta-learner can effectively improve the performance of PLMs on
+previously unseen tasks. In addition, TransPrompt v2 also outperforms strong
+fine-tuning baselines when learning with full training sets.
+"
+AskIt: Unified Programming Interface for Programming with Large Language  Models,Katsumi Okuda,http://arxiv.org/pdf/2308.15645v1.pdf,2023-08-29,"['cs.pl', 'cs.ai', 'cs.se']",2308.15645v1.pdf,"  In the evolving landscape of software development, Large Language Models
+(LLMs) exhibit a unique phenomenon known as emergent abilities, demonstrating
+adeptness across numerous tasks, from text summarization to code generation.
+While these abilities open up novel avenues in software design and crafting,
+their incorporation presents substantial challenges. Developers grapple with
+decisions surrounding the direct embedding of LLMs within applications versus
+employing them for code generation. Moreover, effective prompt design becomes a
+critical concern, given the necessity of data extraction from natural language
+outputs. To address these intricacies, this paper introduces AskIt, a
+domain-specific language (DSL) specifically designed for LLMs. AskIt simplifies
+LLM integration, offering type-guided output control, template-based function
+definitions, and a unified interface that diminishes the distinction between
+LLM-based code generation and application integration. Furthermore, through
+Programming by Example (PBE), AskIt harnesses the power of few-shot learning at
+the programming language level. Our evaluations underscore AskIt's potency.
+Across 50 tasks, AskIt generated concise prompts for the given tasks, achieving
+a 16.14% reduction in prompt length relative to benchmarks. Additionally, by
+enabling the transition from direct LLM application usage to function
+generation, AskIt achieved significant speedups, as observed in our GSM8K
+benchmark experiments. Through these advancements, AskIt streamlines the
+integration of LLMs in software development, offering a more efficient,
+versatile approach for leveraging emergent abilities. The implementations of
+AskIt in TypeScript and Python are available at
+https://github.com/katsumiok/ts-askit and https://github.com/katsumiok/pyaskit,
+respectively.
+"
+Self-Sampling Meta SAM: Enhancing Few-shot Medical Image Segmentation  with Meta-Learning,Yiming Zhang,http://arxiv.org/pdf/2308.16466v3.pdf,2023-08-31,['cs.cv'],2308.16466v3.pdf,"  While the Segment Anything Model (SAM) excels in semantic segmentation for
+general-purpose images, its performance significantly deteriorates when applied
+to medical images, primarily attributable to insufficient representation of
+medical images in its training dataset. Nonetheless, gathering comprehensive
+datasets and training models that are universally applicable is particularly
+challenging due to the long-tail problem common in medical images. To address
+this gap, here we present a Self-Sampling Meta SAM (SSM-SAM) framework for
+few-shot medical image segmentation. Our innovation lies in the design of three
+key modules: 1) An online fast gradient descent optimizer, further optimized by
+a meta-learner, which ensures swift and robust adaptation to new tasks. 2) A
+Self-Sampling module designed to provide well-aligned visual prompts for
+improved attention allocation; and 3) A robust attention-based decoder
+specifically designed for medical few-shot learning to capture relationship
+between different slices. Extensive experiments on a popular abdominal CT
+dataset and an MRI dataset demonstrate that the proposed method achieves
+significant improvements over state-of-the-art methods in few-shot
+segmentation, with an average improvements of 10.21% and 1.80% in terms of DSC,
+respectively. In conclusion, we present a novel approach for rapid online
+adaptation in interactive image segmentation, adapting to a new organ in just
+0.83 minutes. Code is publicly available on GitHub upon acceptance.
+"
+Prompt-based Node Feature Extractor for Few-shot Learning on  Text-Attributed Graphs,Xuanwen Huang,http://arxiv.org/pdf/2309.02848v1.pdf,2023-09-06,['cs.si'],2309.02848v1.pdf,"  Text-attributed Graphs (TAGs) are commonly found in the real world, such as
+social networks and citation networks, and consist of nodes represented by
+textual descriptions. Currently, mainstream machine learning methods on TAGs
+involve a two-stage modeling approach: (1) unsupervised node feature extraction
+with pre-trained language models (PLMs); and (2) supervised learning using
+Graph Neural Networks (GNNs). However, we observe that these representations,
+which have undergone large-scale pre-training, do not significantly improve
+performance with a limited amount of training samples. The main issue is that
+existing methods have not effectively integrated information from the graph and
+downstream tasks simultaneously. In this paper, we propose a novel framework
+called G-Prompt, which combines a graph adapter and task-specific prompts to
+extract node features. First, G-Prompt introduces a learnable GNN layer
+(\emph{i.e.,} adaptor) at the end of PLMs, which is fine-tuned to better
+capture the masked tokens considering graph neighborhood information. After the
+adapter is trained, G-Prompt incorporates task-specific prompts to obtain
+\emph{interpretable} node representations for the downstream task. Our
+experiment results demonstrate that our proposed method outperforms current
+state-of-the-art (SOTA) methods on few-shot node classification. More
+importantly, in zero-shot settings, the G-Prompt embeddings can not only
+provide better task interpretability than vanilla PLMs but also achieve
+comparable performance with fully-supervised baselines.
+"
+Cross-Image Context Matters for Bongard Problems,Nikhil Raghuraman,http://arxiv.org/pdf/2309.03468v1.pdf,2023-09-07,"['cs.cv', 'cs.ai', 'cs.lg']",2309.03468v1.pdf,"  Current machine learning methods struggle to solve Bongard problems, which
+are a type of IQ test that requires deriving an abstract ""concept"" from a set
+of positive and negative ""support"" images, and then classifying whether or not
+a new query image depicts the key concept. On Bongard-HOI, a benchmark for
+natural-image Bongard problems, existing methods have only reached 66% accuracy
+(where chance is 50%). Low accuracy is often attributed to neural nets' lack of
+ability to find human-like symbolic rules. In this work, we point out that many
+existing methods are forfeiting accuracy due to a much simpler problem: they do
+not incorporate information contained in the support set as a whole, and rely
+instead on information extracted from individual supports. This is a critical
+issue, because unlike in few-shot learning tasks concerning object
+classification, the ""key concept"" in a typical Bongard problem can only be
+distinguished using multiple positives and multiple negatives. We explore a
+variety of simple methods to take this cross-image context into account, and
+demonstrate substantial gains over prior methods, leading to new
+state-of-the-art performance on Bongard-LOGO (75.3%) and Bongard-HOI (72.45%)
+and strong performance on the original Bongard problem set (60.84%).
+"
+DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning,Zhengxiang Shi,http://arxiv.org/pdf/2309.05173v2.pdf,2023-09-11,"['cs.cl', 'cs.ai', 'cs.cv', 'cs.lg']",2309.05173v2.pdf,"  Prompt tuning (PT), where a small amount of trainable soft (continuous)
+prompt vectors is affixed to the input of language models (LM), has shown
+promising results across various tasks and models for parameter-efficient
+fine-tuning (PEFT). PT stands out from other PEFT approaches because it
+maintains competitive performance with fewer trainable parameters and does not
+drastically scale up its parameters as the model size expands. However, PT
+introduces additional soft prompt tokens, leading to longer input sequences,
+which significantly impacts training and inference time and memory usage due to
+the Transformer's quadratic complexity. Particularly concerning for Large
+Language Models (LLMs) that face heavy daily querying. To address this issue,
+we propose Decomposed Prompt Tuning (DePT), which decomposes the soft prompt
+into a shorter soft prompt and a pair of low-rank matrices that are then
+optimised with two different learning rates. This allows DePT to achieve better
+performance while saving over 20% memory and time costs compared to vanilla PT
+and its variants, without changing trainable parameter sizes. Through extensive
+experiments on 23 natural language processing (NLP) and vision-language (VL)
+tasks, we demonstrate that DePT outperforms state-of-the-art PEFT approaches,
+including the full fine-tuning baseline in some scenarios. Additionally, we
+empirically show that DEPT grows more efficient as the model size increases.
+Our further study reveals that DePT integrates seamlessly with
+parameter-efficient transfer learning in the few-shot learning setting and
+highlights its adaptability to various model architectures and sizes.
+"
+Zero-shot Learning with Minimum Instruction to Extract Social  Determinants and Family History from Clinical Notes using GPT Model,Neel Bhate,http://arxiv.org/pdf/2309.05475v2.pdf,2023-09-11,['cs.cl'],2309.05475v2.pdf,"  Demographics, Social determinants of health, and family history documented in
+the unstructured text within the electronic health records are increasingly
+being studied to understand how this information can be utilized with the
+structured data to improve healthcare outcomes. After the GPT models were
+released, many studies have applied GPT models to extract this information from
+the narrative clinical notes. Different from the existing work, our research
+focuses on investigating the zero-shot learning on extracting this information
+together by providing minimum information to the GPT model. We utilize
+de-identified real-world clinical notes annotated for demographics, various
+social determinants, and family history information. Given that the GPT model
+might provide text different from the text in the original data, we explore two
+sets of evaluation metrics, including the traditional NER evaluation metrics
+and semantic similarity evaluation metrics, to completely understand the
+performance. Our results show that the GPT-3.5 method achieved an average of
+0.975 F1 on demographics extraction, 0.615 F1 on social determinants
+extraction, and 0.722 F1 on family history extraction. We believe these results
+can be further improved through model fine-tuning or few-shots learning.
+Through the case studies, we also identified the limitations of the GPT models,
+which need to be addressed in future research.
+"
+GLAD: Content-aware Dynamic Graphs For Log Anomaly Detection,Yufei Li,http://arxiv.org/pdf/2309.05953v1.pdf,2023-09-12,"['cs.lg', 'cs.ir']",2309.05953v1.pdf,"  Logs play a crucial role in system monitoring and debugging by recording
+valuable system information, including events and states. Although various
+methods have been proposed to detect anomalies in log sequences, they often
+overlook the significance of considering relations among system components,
+such as services and users, which can be identified from log contents.
+Understanding these relations is vital for detecting anomalies and their
+underlying causes. To address this issue, we introduce GLAD, a Graph-based Log
+Anomaly Detection framework designed to detect relational anomalies in system
+logs. GLAD incorporates log semantics, relational patterns, and sequential
+patterns into a unified framework for anomaly detection. Specifically, GLAD
+first introduces a field extraction module that utilizes prompt-based few-shot
+learning to identify essential fields from log contents. Then GLAD constructs
+dynamic log graphs for sliding windows by interconnecting extracted fields and
+log events parsed from the log parser. These graphs represent events and fields
+as nodes and their relations as edges. Subsequently, GLAD utilizes a
+temporal-attentive graph edge anomaly detection model for identifying anomalous
+relations in these dynamic log graphs. This model employs a Graph Neural
+Network (GNN)-based encoder enhanced with transformers to capture content,
+structural and temporal features. We evaluate our proposed method on three
+datasets, and the results demonstrate the effectiveness of GLAD in detecting
+anomalies indicated by varying relational patterns.
+"
+Using Large Language Model to Solve and Explain Physics Word Problems  Approaching Human Level,Jingzhe Ding,http://arxiv.org/pdf/2309.08182v2.pdf,2023-09-15,"['cs.cl', 'cs.ai', 'i.2.7']",2309.08182v2.pdf,"  Our work demonstrates that large language model (LLM) pre-trained on texts
+can not only solve pure math word problems, but also physics word problems,
+whose solution requires calculation and inference based on prior physical
+knowledge. We collect and annotate the first physics word problem
+dataset-PhysQA, which contains over 1000 junior high school physics word
+problems (covering Kinematics, Mass&Density, Mechanics, Heat, Electricity).
+Then we use OpenAI' s GPT3.5 to generate the answer of these problems and found
+that GPT3.5 could automatically solve 49.3% of the problems through zero-shot
+learning and 73.2% through few-shot learning. This result demonstrates that by
+using similar problems and their answers as prompt, LLM could solve elementary
+physics word problems approaching human level performance. In addition to
+solving problems, GPT3.5 can also summarize the knowledge or topics covered by
+the problems, provide relevant explanations, and generate new physics word
+problems based on the input. Our work is the first research to focus on the
+automatic solving, explanation, and generation of physics word problems across
+various types and scenarios, and we achieve an acceptable and state-of-the-art
+accuracy. This underscores the potential of LLMs for further applications in
+secondary education.
+"
+SCT: A Simple Baseline for Parameter-Efficient Fine-Tuning via Salient  Channels,Henry Hengyuan Zhao,http://arxiv.org/pdf/2309.08513v2.pdf,2023-09-15,"['cs.cv', 'cs.ai']",2309.08513v2.pdf,"  Pre-trained vision transformers have strong representation benefits to
+various downstream tasks. Recently, many parameter-efficient fine-tuning (PEFT)
+methods have been proposed, and their experiments demonstrate that tuning only
+1% of extra parameters could surpass full fine-tuning in low-data resource
+scenarios. However, these methods overlook the task-specific information when
+fine-tuning diverse downstream tasks. In this paper, we propose a simple yet
+effective method called ""Salient Channel Tuning"" (SCT) to leverage the
+task-specific information by forwarding the model with the task images to
+select partial channels in a feature map that enables us to tune only 1/8
+channels leading to significantly lower parameter costs. Experiments outperform
+full fine-tuning on 18 out of 19 tasks in the VTAB-1K benchmark by adding only
+0.11M parameters of the ViT-B, which is 780$\times$ fewer than its full
+fine-tuning counterpart. Furthermore, experiments on domain generalization and
+few-shot learning surpass other PEFT methods with lower parameter costs,
+demonstrating our proposed tuning technique's strong capability and
+effectiveness in the low-data regime.
+"
+nnSAM: Plug-and-play Segment Anything Model Improves nnUNet Performance,Yunxiang Li,http://arxiv.org/pdf/2309.16967v2.pdf,2023-09-29,"['cs.cv', 'eess.iv']",2309.16967v2.pdf,"  The recent developments of foundation models in computer vision, especially
+the Segment Anything Model (SAM), allow scalable and domain-agnostic image
+segmentation to serve as a general-purpose segmentation tool. In parallel, the
+field of medical image segmentation has benefited significantly from
+specialized neural networks like the nnUNet, which is trained on
+domain-specific datasets and can automatically configure the network to tailor
+to specific segmentation challenges. To combine the advantages of foundation
+models and domain-specific models, we present nnSAM, which synergistically
+integrates the SAM model with the nnUNet model to achieve more accurate and
+robust medical image segmentation. The nnSAM model leverages the powerful and
+robust feature extraction capabilities of SAM, while harnessing the automatic
+configuration capabilities of nnUNet to promote dataset-tailored learning. Our
+comprehensive evaluation of nnSAM model on different sizes of training samples
+shows that it allows few-shot learning, which is highly relevant for medical
+image segmentation where high-quality, annotated data can be scarce and costly
+to obtain. By melding the strengths of both its predecessors, nnSAM positions
+itself as a potential new benchmark in medical image segmentation, offering a
+tool that combines broad applicability with specialized efficiency. The code is
+available at https://github.com/Kent0n-Li/Medical-Image-Segmentation.
+"
+An evaluation of GPT models for phenotype concept recognition,Tudor Groza,http://arxiv.org/pdf/2309.17169v1.pdf,2023-09-29,"['cs.cl', 'cs.ai']",2309.17169v1.pdf,"  Objective: Clinical deep phenotyping plays a critical role in both the
+diagnosis of patients with rare disorders as well as in building care
+coordination plans. The process relies on modelling and curating patient
+profiles using ontology concepts, usually from the Human Phenotype Ontology.
+Machine learning methods have been widely adopted to support this phenotype
+concept recognition task. With the significant shift in the use of large
+language models (LLMs) for most NLP tasks, herewithin, we examine the
+performance of the latest Generative Pre-trained Transformer (GPT) models
+underpinning ChatGPT in clinical deep phenotyping. Materials and Methods: The
+experimental setup of the study included seven prompts of various levels of
+specificity, two GPT models (gpt-3.5 and gpt-4.0) and an established gold
+standard for phenotype recognition. Results: Our results show that, currently,
+these models have not yet achieved state of the art performance. The best run,
+using few-shots learning, achieved 0.41 F1 score, compared to a 0.62 F1 score
+achieved by the current best in class tool. Conclusion: The non-deterministic
+nature of the outcomes and the lack of concordance between different runs using
+the same prompt and input makes the use of these LLMs in clinical settings
+problematic.
+"
+RA-DIT: Retrieval-Augmented Dual Instruction Tuning,Xi Victoria Lin,http://arxiv.org/pdf/2310.01352v3.pdf,2023-10-02,"['cs.cl', 'cs.ai']",2310.01352v3.pdf,"  Retrieval-augmented language models (RALMs) improve performance by accessing
+long-tail and up-to-date knowledge from external data stores, but are
+challenging to build. Existing approaches require either expensive
+retrieval-specific modifications to LM pre-training or use post-hoc integration
+of the data store that leads to suboptimal performance. We introduce
+Retrieval-Augmented Dual Instruction Tuning (RA-DIT), a lightweight fine-tuning
+methodology that provides a third option by retrofitting any LLM with retrieval
+capabilities. Our approach operates in two distinct fine-tuning steps: (1) one
+updates a pre-trained LM to better use retrieved information, while (2) the
+other updates the retriever to return more relevant results, as preferred by
+the LM. By fine-tuning over tasks that require both knowledge utilization and
+contextual awareness, we demonstrate that each stage yields significant
+performance improvements, and using both leads to additional gains. Our best
+model, RA-DIT 65B, achieves state-of-the-art performance across a range of
+knowledge-intensive zero- and few-shot learning benchmarks, significantly
+outperforming existing in-context RALM approaches by up to +8.9% in 0-shot
+setting and +1.4% in 5-shot setting on average.
+"
+UniPredict: Large Language Models are Universal Tabular Predictors,Ruiyu Wang,http://arxiv.org/pdf/2310.03266v1.pdf,2023-10-05,['cs.lg'],2310.03266v1.pdf,"  Tabular data prediction is a fundamental machine learning task for many
+applications. Existing methods predominantly employ discriminative modeling and
+operate under the assumption of a fixed target column, necessitating
+re-training for every new predictive task. Inspired by the generative power of
+large language models (LLMs), this paper exploits the idea of building
+universal tabular data predictors based on generative modeling, namely
+UniPredict. Here, we show that scaling up an LLM to extensive tabular datasets
+with the capability of comprehending diverse tabular inputs and predicting for
+target variables following the input instructions. Specifically, we train a
+single LLM on an aggregation of 169 tabular datasets with diverse targets and
+compare its performance against baselines that are trained on each dataset
+separately. We observe this versatile UniPredict model demonstrates an
+advantage over other models, ranging from 5.4% to 13.4%, when compared with the
+best tree-boosting baseline and the best neural network baseline, respectively.
+We further test UniPredict in few-shot learning settings on another 62 tabular
+datasets. Our method achieves strong performance in quickly adapting to new
+tasks, where our method outperforms XGBoost over 100% on the low-resource setup
+and shows a significant margin over all baselines. We envision that UniPredict
+sheds light on developing a universal tabular data prediction system that
+learns from data at scale and serves a wide range of prediction tasks.
+"
+LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios  via Prompt Compression,Huiqiang Jiang,http://arxiv.org/pdf/2310.06839v1.pdf,2023-10-10,"['cs.cl', 'cs.lg']",2310.06839v1.pdf,"  In long context scenarios, large language models (LLMs) face three main
+challenges: higher computational/financial cost, longer latency, and inferior
+performance. Some studies reveal that the performance of LLMs depends on both
+the density and the position of the key information (question relevant) in the
+input prompt. Inspired by these findings, we propose LongLLMLingua for prompt
+compression towards improving LLMs' perception of the key information to
+simultaneously address the three challenges. We conduct evaluation on a wide
+range of long context scenarios including single-/multi-document QA, few-shot
+learning, summarization, synthetic tasks, and code completion. The experimental
+results show that LongLLMLingua compressed prompt can derive higher performance
+with much less cost. The latency of the end-to-end system is also reduced. For
+example, on NaturalQuestions benchmark, LongLLMLingua gains a performance boost
+of up to 17.1% over the original prompt with ~4x fewer tokens as input to
+GPT-3.5-Turbo. It can derive cost savings of \$28.5 and \$27.4 per 1,000
+samples from the LongBench and ZeroScrolls benchmark, respectively.
+Additionally, when compressing prompts of ~10k tokens at a compression rate of
+2x-10x, LongLLMLingua can speed up the end-to-end latency by 1.4x-3.8x. Our
+code is available at https://aka.ms/LLMLingua.
+"
+Empower Text-Attributed Graphs Learning with Large Language Models  (LLMs),Jianxiang Yu,http://arxiv.org/pdf/2310.09872v1.pdf,2023-10-15,['cs.lg'],2310.09872v1.pdf,"  Text-attributed graphs have recently garnered significant attention due to
+their wide range of applications in web domains. Existing methodologies employ
+word embedding models for acquiring text representations as node features,
+which are subsequently fed into Graph Neural Networks (GNNs) for training.
+Recently, the advent of Large Language Models (LLMs) has introduced their
+powerful capabilities in information retrieval and text generation, which can
+greatly enhance the text attributes of graph data. Furthermore, the acquisition
+and labeling of extensive datasets are both costly and time-consuming
+endeavors. Consequently, few-shot learning has emerged as a crucial problem in
+the context of graph learning tasks. In order to tackle this challenge, we
+propose a lightweight paradigm called ENG, which adopts a plug-and-play
+approach to empower text-attributed graphs through node generation using LLMs.
+Specifically, we utilize LLMs to extract semantic information from the labels
+and generate samples that belong to these categories as exemplars.
+Subsequently, we employ an edge predictor to capture the structural information
+inherent in the raw dataset and integrate the newly generated samples into the
+original graph. This approach harnesses LLMs for enhancing class-level
+information and seamlessly introduces labeled nodes and edges without modifying
+the raw dataset, thereby facilitating the node classification task in few-shot
+scenarios. Extensive experiments demonstrate the outstanding performance of our
+proposed paradigm, particularly in low-shot scenarios. For instance, in the
+1-shot setting of the ogbn-arxiv dataset, ENG achieves a 76% improvement over
+the baseline model.
+"
+In-Context Learning with Iterative Demonstration Selection,Chengwei Qin,http://arxiv.org/pdf/2310.09881v2.pdf,2023-10-15,"['cs.cl', 'cs.ai']",2310.09881v2.pdf,"  Spurred by advancements in scale, large language models (LLMs) have
+demonstrated strong few-shot learning ability via in-context learning (ICL).
+However, the performance of ICL has been shown to be highly sensitive to the
+selection of few-shot demonstrations. Selecting the most suitable examples as
+context remains an ongoing challenge and an open problem. Existing literature
+has highlighted the importance of selecting examples that are diverse or
+semantically similar to the test sample while ignoring the fact that the
+optimal selection dimension, i.e., diversity or similarity, is task-specific.
+Leveraging the merits of both dimensions, we propose Iterative Demonstration
+Selection (IDS). Using zero-shot chain-of-thought reasoning (Zero-shot-CoT),
+IDS iteratively selects examples that are diverse but still strongly correlated
+with the test sample as ICL demonstrations. Specifically, IDS applies
+Zero-shot-CoT to the test sample before demonstration selection. The output
+reasoning path is then used to choose demonstrations that are prepended to the
+test sample for inference. The generated answer is accompanied by its
+corresponding reasoning path for extracting a new set of demonstrations in the
+next iteration. After several iterations, IDS adopts majority voting to obtain
+the final result. Through extensive experiments on tasks including commonsense
+reasoning, question answering, topic classification, and sentiment analysis, we
+demonstrate that IDS can consistently outperform existing ICL demonstration
+selection methods.
+"
+The Skipped Beat: A Study of Sociopragmatic Understanding in LLMs for 64  Languages,Chiyu Zhang,http://arxiv.org/pdf/2310.14557v1.pdf,2023-10-23,['cs.cl'],2310.14557v1.pdf,"  Instruction tuned large language models (LLMs), such as ChatGPT, demonstrate
+remarkable performance in a wide range of tasks. Despite numerous recent
+studies that examine the performance of instruction-tuned LLMs on various NLP
+benchmarks, there remains a lack of comprehensive investigation into their
+ability to understand cross-lingual sociopragmatic meaning (SM), i.e., meaning
+embedded within social and interactive contexts. This deficiency arises partly
+from SM not being adequately represented in any of the existing benchmarks. To
+address this gap, we present SPARROW, an extensive multilingual benchmark
+specifically designed for SM understanding. SPARROW comprises 169 datasets
+covering 13 task types across six primary categories (e.g., anti-social
+language detection, emotion recognition). SPARROW datasets encompass 64
+different languages originating from 12 language families representing 16
+writing scripts. We evaluate the performance of various multilingual pretrained
+language models (e.g., mT5) and instruction-tuned LLMs (e.g., BLOOMZ, ChatGPT)
+on SPARROW through fine-tuning, zero-shot, and/or few-shot learning. Our
+comprehensive analysis reveals that existing open-source instruction tuned LLMs
+still struggle to understand SM across various languages, performing close to a
+random baseline in some cases. We also find that although ChatGPT outperforms
+many LLMs, it still falls behind task-specific finetuned models with a gap of
+12.19 SPARROW score. Our benchmark is available at:
+https://github.com/UBC-NLP/SPARROW
+"
+PAC-tuning:Fine-tuning Pretrained Language Models with PAC-driven  Perturbed Gradient Descent,Guangliang Liu,http://arxiv.org/pdf/2310.17588v1.pdf,2023-10-26,"['cs.lg', 'cs.cl']",2310.17588v1.pdf,"  Fine-tuning pretrained language models (PLMs) for downstream tasks is a
+large-scale optimization problem, in which the choice of the training algorithm
+critically determines how well the trained model can generalize to unseen test
+data, especially in the context of few-shot learning. To achieve good
+generalization performance and avoid overfitting, techniques such as data
+augmentation and pruning are often applied. However, adding these
+regularizations necessitates heavy tuning of the hyperparameters of
+optimization algorithms, such as the popular Adam optimizer. In this paper, we
+propose a two-stage fine-tuning method, PAC-tuning, to address this
+optimization challenge. First, based on PAC-Bayes training, PAC-tuning directly
+minimizes the PAC-Bayes generalization bound to learn proper parameter
+distribution. Second, PAC-tuning modifies the gradient by injecting noise with
+the variance learned in the first stage into the model parameters during
+training, resulting in a variant of perturbed gradient descent (PGD). In the
+past, the few-shot scenario posed difficulties for PAC-Bayes training because
+the PAC-Bayes bound, when applied to large models with limited training data,
+might not be stringent. Our experimental results across 5 GLUE benchmark tasks
+demonstrate that PAC-tuning successfully handles the challenges of fine-tuning
+tasks and outperforms strong baseline methods by a visible margin, further
+confirming the potential to apply PAC training for any other settings where the
+Adam optimizer is currently used for training.
+"
+Unleashing the Power of Pre-trained Language Models for Offline  Reinforcement Learning,Ruizhe Shi,http://arxiv.org/pdf/2310.20587v3.pdf,2023-10-31,['cs.lg'],2310.20587v3.pdf,"  Offline reinforcement learning (RL) aims to find a near-optimal policy using
+pre-collected datasets. In real-world scenarios, data collection could be
+costly and risky; therefore, offline RL becomes particularly challenging when
+the in-domain data is limited. Given recent advances in Large Language Models
+(LLMs) and their few-shot learning prowess, this paper introduces
+$\textbf{La}$nguage Models for $\textbf{Mo}$tion Control ($\textbf{LaMo}$), a
+general framework based on Decision Transformers to effectively use pre-trained
+Language Models (LMs) for offline RL. Our framework highlights four crucial
+components: (1) Initializing Decision Transformers with sequentially
+pre-trained LMs, (2) employing the LoRA fine-tuning method, in contrast to
+full-weight fine-tuning, to combine the pre-trained knowledge from LMs and
+in-domain knowledge effectively, (3) using the non-linear MLP transformation
+instead of linear projections, to generate embeddings, and (4) integrating an
+auxiliary language prediction loss during fine-tuning to stabilize the LMs and
+retain their original abilities on languages. Empirical results indicate
+$\textbf{LaMo}$ achieves state-of-the-art performance in sparse-reward tasks
+and closes the gap between value-based offline RL methods and decision
+transformers in dense-reward tasks. In particular, our method demonstrates
+superior performance in scenarios with limited data samples. Our project
+website is $\href{https://lamo2023.github.io}{\text{this https URL}}$.
+"
+On Task-personalized Multimodal Few-shot Learning for Visually-rich  Document Entity Retrieval,Jiayi Chen,http://arxiv.org/pdf/2311.00693v1.pdf,2023-11-01,['cs.ai'],2311.00693v1.pdf,"  Visually-rich document entity retrieval (VDER), which extracts key
+information (e.g. date, address) from document images like invoices and
+receipts, has become an important topic in industrial NLP applications. The
+emergence of new document types at a constant pace, each with its unique entity
+types, presents a unique challenge: many documents contain unseen entity types
+that occur only a couple of times. Addressing this challenge requires models to
+have the ability of learning entities in a few-shot manner. However, prior
+works for Few-shot VDER mainly address the problem at the document level with a
+predefined global entity space, which doesn't account for the entity-level
+few-shot scenario: target entity types are locally personalized by each task
+and entity occurrences vary significantly among documents. To address this
+unexplored scenario, this paper studies a novel entity-level few-shot VDER
+task. The challenges lie in the uniqueness of the label space for each task and
+the increased complexity of out-of-distribution (OOD) contents. To tackle this
+novel task, we present a task-aware meta-learning based framework, with a
+central focus on achieving effective task personalization that distinguishes
+between in-task and out-of-task distribution. Specifically, we adopt a
+hierarchical decoder (HC) and employ contrastive learning (ContrastProtoNet) to
+achieve this goal. Furthermore, we introduce a new dataset, FewVEX, to boost
+future research in the field of entity-level few-shot VDER. Experimental
+results demonstrate our approaches significantly improve the robustness of
+popular meta-learning baselines.
+"
+A Survey of Large Language Models for Autonomous Driving,Zhenjie Yang,http://arxiv.org/pdf/2311.01043v1.pdf,2023-11-02,['cs.ai'],2311.01043v1.pdf,"  Autonomous driving technology, a catalyst for revolutionizing transportation
+and urban mobility, has the tend to transition from rule-based systems to
+data-driven strategies. Traditional module-based systems are constrained by
+cumulative errors among cascaded modules and inflexible pre-set rules. In
+contrast, end-to-end autonomous driving systems have the potential to avoid
+error accumulation due to their fully data-driven training process, although
+they often lack transparency due to their ``black box"" nature, complicating the
+validation and traceability of decisions. Recently, large language models
+(LLMs) have demonstrated abilities including understanding context, logical
+reasoning, and generating answers. A natural thought is to utilize these
+abilities to empower autonomous driving. By combining LLM with foundation
+vision models, it could open the door to open-world understanding, reasoning,
+and few-shot learning, which current autonomous driving systems are lacking. In
+this paper, we systematically review a research line about \textit{Large
+Language Models for Autonomous Driving (LLM4AD)}. This study evaluates the
+current state of technological advancements, distinctly outlining the principal
+challenges and prospective directions for the field. For the convenience of
+researchers in academia and industry, we provide real-time updates on the
+latest advances in the field as well as relevant open-source resources via the
+designated link: https://github.com/Thinklab-SJTU/Awesome-LLM4AD.
+"
+Robust Fine-Tuning of Vision-Language Models for Domain Generalization,Kevin Vogt-Lowell,http://arxiv.org/pdf/2311.02236v1.pdf,2023-11-03,"['cs.cv', 'cs.ai', 'cs.cl', 'cs.lg']",2311.02236v1.pdf,"  Transfer learning enables the sharing of common knowledge among models for a
+variety of downstream tasks, but traditional methods suffer in limited training
+data settings and produce narrow models incapable of effectively generalizing
+under distribution shifts. Foundation models have recently demonstrated
+impressive zero-shot inference capabilities and robustness under distribution
+shifts. However, zero-shot evaluation for these models has been predominantly
+confined to benchmarks with simple distribution shifts, limiting our
+understanding of their effectiveness under the more realistic shifts found in
+practice. Moreover, common fine-tuning methods for these models have yet to be
+evaluated against vision models in few-shot scenarios where training data is
+limited. To address these gaps, we present a new recipe for few-shot
+fine-tuning of the popular vision-language foundation model CLIP and evaluate
+its performance on challenging benchmark datasets with realistic distribution
+shifts from the WILDS collection. Our experimentation demonstrates that, while
+zero-shot CLIP fails to match performance of trained vision models on more
+complex benchmarks, few-shot CLIP fine-tuning outperforms its vision-only
+counterparts in terms of in-distribution and out-of-distribution accuracy at
+all levels of training data availability. This provides a strong incentive for
+adoption of foundation models within few-shot learning applications operating
+with real-world data. Code is available at
+https://github.com/mit-ll/robust-vision-language-finetuning
+"
+"A Minimalist Dataset for Systematic Generalization of Perception,  Syntax, and Semantics",Qing Li,http://arxiv.org/pdf/2103.01403v3.pdf,2021-03-02,"['cs.lg', 'cs.ai', 'cs.cv']",2103.01403v3.pdf,"  Inspired by humans' exceptional ability to master arithmetic and generalize
+to new problems, we present a new dataset, Handwritten arithmetic with INTegers
+(HINT), to examine machines' capability of learning generalizable concepts at
+three levels: perception, syntax, and semantics. In HINT, machines are tasked
+with learning how concepts are perceived from raw signals such as images (i.e.,
+perception), how multiple concepts are structurally combined to form a valid
+expression (i.e., syntax), and how concepts are realized to afford various
+reasoning tasks (i.e., semantics), all in a weakly supervised manner. Focusing
+on systematic generalization, we carefully design a five-fold test set to
+evaluate both the interpolation and the extrapolation of learned concepts
+w.r.t. the three levels. Further, we design a few-shot learning split to
+determine whether or not models can rapidly learn new concepts and generalize
+them to more complex scenarios. To comprehend existing models' limitations, we
+undertake extensive experiments with various sequence-to-sequence models,
+including RNNs, Transformers, and GPT-3 (with the chain of thought prompting).
+The results indicate that current models struggle to extrapolate to long-range
+syntactic dependency and semantics. Models exhibit a considerable gap toward
+human-level generalization when evaluated with new concepts in a few-shot
+setting. Moreover, we discover that it is infeasible to solve HINT by merely
+scaling up the dataset and the model size; this strategy contributes little to
+the extrapolation of syntax and semantics. Finally, in zero-shot GPT-3
+experiments, the chain of thought prompting exhibits impressive results and
+significantly boosts the test accuracy. We believe the HINT dataset and the
+experimental findings are of great interest to the learning community on
+systematic generalization.
+"
+Lesion2Vec: Deep Metric Learning for Few-Shot Multiple Lesions  Recognition in Wireless Capsule Endoscopy Video,Sodiq Adewole,http://arxiv.org/pdf/2101.04240v2.pdf,2021-01-11,['cs.cv'],2101.04240v2.pdf,"  Effective and rapid detection of lesions in the Gastrointestinal tract is
+critical to gastroenterologist's response to some life-threatening diseases.
+Wireless Capsule Endoscopy (WCE) has revolutionized traditional endoscopy
+procedure by allowing gastroenterologists visualize the entire GI tract
+non-invasively. Once the tiny capsule is swallowed, it sequentially capture
+images of the GI tract at about 2 to 6 frames per second (fps). A single video
+can last up to 8 hours producing between 30,000 to 100,000 images. Automating
+the detection of frames containing specific lesion in WCE video would relieve
+gastroenterologists the arduous task of reviewing the entire video before
+making diagnosis. While the WCE produces large volume of images, only about 5\%
+of the frames contain lesions that aid the diagnosis process. Convolutional
+Neural Network (CNN) based models have been very successful in various image
+classification tasks. However, they suffer excessive parameters, are sample
+inefficient and rely on very large amount of training data. Deploying a CNN
+classifier for lesion detection task will require time-to-time fine-tuning to
+generalize to any unforeseen category. In this paper, we propose a metric-based
+learning framework followed by a few-shot lesion recognition in WCE data.
+Metric-based learning is a meta-learning framework designed to establish
+similarity or dissimilarity between concepts while few-shot learning (FSL) aims
+to identify new concepts from only a small number of examples. We train a
+feature extractor to learn a representation for different small bowel lesions
+using metric-based learning. At the testing stage, the category of an unseen
+sample is predicted from only a few support examples, thereby allowing the
+model to generalize to a new category that has never been seen before. We
+demonstrated the efficacy of this method on real patient capsule endoscopy
+data.
+"
+Program Synthesis with Large Language Models,Jacob Austin,http://arxiv.org/pdf/2108.07732v1.pdf,2021-08-16,"['cs.pl', 'cs.lg']",2108.07732v1.pdf,"  This paper explores the limits of the current generation of large language
+models for program synthesis in general purpose programming languages. We
+evaluate a collection of such models (with between 244M and 137B parameters) on
+two new benchmarks, MBPP and MathQA-Python, in both the few-shot and
+fine-tuning regimes. Our benchmarks are designed to measure the ability of
+these models to synthesize short Python programs from natural language
+descriptions. The Mostly Basic Programming Problems (MBPP) dataset contains 974
+programming tasks, designed to be solvable by entry-level programmers. The
+MathQA-Python dataset, a Python version of the MathQA benchmark, contains 23914
+problems that evaluate the ability of the models to synthesize code from more
+complex text. On both datasets, we find that synthesis performance scales
+log-linearly with model size. Our largest models, even without finetuning on a
+code dataset, can synthesize solutions to 59.6 percent of the problems from
+MBPP using few-shot learning with a well-designed prompt. Fine-tuning on a
+held-out portion of the dataset improves performance by about 10 percentage
+points across most model sizes. On the MathQA-Python dataset, the largest
+fine-tuned model achieves 83.8 percent accuracy. Going further, we study the
+model's ability to engage in dialog about code, incorporating human feedback to
+improve its solutions. We find that natural language feedback from a human
+halves the error rate compared to the model's initial prediction. Additionally,
+we conduct an error analysis to shed light on where these models fall short and
+what types of programs are most difficult to generate. Finally, we explore the
+semantic grounding of these models by fine-tuning them to predict the results
+of program execution. We find that even our best models are generally unable to
+predict the output of a program given a specific input.
+"
+Unsupervised Law Article Mining based on Deep Pre-Trained Language  Representation Models with Application to the Italian Civil Code,Andrea Tagarelli,http://arxiv.org/pdf/2112.03033v1.pdf,2021-12-02,"['cs.cl', 'cs.ai', 'cs.ir', 'physics.soc-ph']",2112.03033v1.pdf,"  Modeling law search and retrieval as prediction problems has recently emerged
+as a predominant approach in law intelligence. Focusing on the law article
+retrieval task, we present a deep learning framework named LamBERTa, which is
+designed for civil-law codes, and specifically trained on the Italian civil
+code. To our knowledge, this is the first study proposing an advanced approach
+to law article prediction for the Italian legal system based on a BERT
+(Bidirectional Encoder Representations from Transformers) learning framework,
+which has recently attracted increased attention among deep learning
+approaches, showing outstanding effectiveness in several natural language
+processing and learning tasks. We define LamBERTa models by fine-tuning an
+Italian pre-trained BERT on the Italian civil code or its portions, for law
+article retrieval as a classification task. One key aspect of our LamBERTa
+framework is that we conceived it to address an extreme classification
+scenario, which is characterized by a high number of classes, the few-shot
+learning problem, and the lack of test query benchmarks for Italian legal
+prediction tasks. To solve such issues, we define different methods for the
+unsupervised labeling of the law articles, which can in principle be applied to
+any law article code system. We provide insights into the explainability and
+interpretability of our LamBERTa models, and we present an extensive
+experimental analysis over query sets of different type, for single-label as
+well as multi-label evaluation tasks. Empirical evidence has shown the
+effectiveness of LamBERTa, and also its superiority against widely used
+deep-learning text classifiers and a few-shot learner conceived for an
+attribute-aware prediction task.
+"
+"Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A  Large-Scale Generative Language Model",Shaden Smith,http://arxiv.org/pdf/2201.11990v3.pdf,2022-01-28,['cs.cl'],2201.11990v3.pdf,"  Pretrained general-purpose language models can achieve state-of-the-art
+accuracies in various natural language processing domains by adapting to
+downstream tasks via zero-shot, few-shot and fine-tuning techniques. Because of
+their success, the size of these models has increased rapidly, requiring
+high-performance hardware, software, and algorithmic techniques to enable
+training such large models. As the result of a joint effort between Microsoft
+and NVIDIA, we present details on the training of the largest monolithic
+transformer based language model, Megatron-Turing NLG 530B (MT-NLG), with 530
+billion parameters. In this paper, we first focus on the infrastructure as well
+as the 3D parallelism methodology used to train this model using DeepSpeed and
+Megatron. Next, we detail the training process, the design of our training
+corpus, and our data curation techniques, which we believe is a key ingredient
+to the success of the model. Finally, we discuss various evaluation results, as
+well as other interesting observations and new properties exhibited by MT-NLG.
+We demonstrate that MT-NLG achieves superior zero-, one-, and few-shot learning
+accuracies on several NLP benchmarks and establishes new state-of-the-art
+results. We believe that our contributions will help further the development of
+large-scale training infrastructures, large-scale language models, and natural
+language generations.
+"
+Data Distributional Properties Drive Emergent In-Context Learning in  Transformers,Stephanie C. Y. Chan,http://arxiv.org/pdf/2205.05055v6.pdf,2022-04-22,"['cs.lg', 'cs.ai', 'cs.cl']",2205.05055v6.pdf,"  Large transformer-based models are able to perform in-context few-shot
+learning, without being explicitly trained for it. This observation raises the
+question: what aspects of the training regime lead to this emergent behavior?
+Here, we show that this behavior is driven by the distributions of the training
+data itself. In-context learning emerges when the training data exhibits
+particular distributional properties such as burstiness (items appear in
+clusters rather than being uniformly distributed over time) and having large
+numbers of rarely occurring classes. In-context learning also emerges more
+strongly when item meanings or interpretations are dynamic rather than fixed.
+These properties are exemplified by natural language, but are also inherent to
+naturalistic data in a wide range of other domains. They also depart
+significantly from the uniform, i.i.d. training distributions typically used
+for standard supervised learning. In our initial experiments, we found that
+in-context learning traded off against more conventional weight-based learning,
+and models were unable to achieve both simultaneously. However, our later
+experiments uncovered that the two modes of learning could co-exist in a single
+model when it was trained on data following a skewed Zipfian distribution --
+another common property of naturalistic data, including language. In further
+experiments, we found that naturalistic data distributions were only able to
+elicit in-context learning in transformers, and not in recurrent models. In
+sum, our findings indicate how the transformer architecture works together with
+particular properties of the training data to drive the intriguing emergent
+in-context learning behaviour of large language models, and how future work
+might encourage both in-context and in-weights learning in domains beyond
+language.
+"
+Large Language Models are Zero-Shot Reasoners,Takeshi Kojima,http://arxiv.org/pdf/2205.11916v4.pdf,2022-05-24,"['cs.cl', 'cs.ai', 'cs.lg']",2205.11916v4.pdf,"  Pretrained large language models (LLMs) are widely used in many sub-fields of
+natural language processing (NLP) and generally known as excellent few-shot
+learners with task-specific exemplars. Notably, chain of thought (CoT)
+prompting, a recent technique for eliciting complex multi-step reasoning
+through step-by-step answer examples, achieved the state-of-the-art
+performances in arithmetics and symbolic reasoning, difficult system-2 tasks
+that do not follow the standard scaling laws for LLMs. While these successes
+are often attributed to LLMs' ability for few-shot learning, we show that LLMs
+are decent zero-shot reasoners by simply adding ""Let's think step by step""
+before each answer. Experimental results demonstrate that our Zero-shot-CoT,
+using the same single prompt template, significantly outperforms zero-shot LLM
+performances on diverse benchmark reasoning tasks including arithmetics
+(MultiArith, GSM8K, AQUA-RAT, SVAMP), symbolic reasoning (Last Letter, Coin
+Flip), and other logical reasoning tasks (Date Understanding, Tracking Shuffled
+Objects), without any hand-crafted few-shot examples, e.g. increasing the
+accuracy on MultiArith from 17.7% to 78.7% and GSM8K from 10.4% to 40.7% with
+large InstructGPT model (text-davinci-002), as well as similar magnitudes of
+improvements with another off-the-shelf large model, 540B parameter PaLM. The
+versatility of this single prompt across very diverse reasoning tasks hints at
+untapped and understudied fundamental zero-shot capabilities of LLMs,
+suggesting high-level, multi-task broad cognitive capabilities may be extracted
+by simple prompting. We hope our work not only serves as the minimal strongest
+zero-shot baseline for the challenging reasoning benchmarks, but also
+highlights the importance of carefully exploring and analyzing the enormous
+zero-shot knowledge hidden inside LLMs before crafting finetuning datasets or
+few-shot exemplars.
+"
+Hungry Hungry Hippos: Towards Language Modeling with State Space Models,Daniel Y. Fu,http://arxiv.org/pdf/2212.14052v3.pdf,2022-12-28,"['cs.lg', 'cs.cl']",2212.14052v3.pdf,"  State space models (SSMs) have demonstrated state-of-the-art sequence
+modeling performance in some modalities, but underperform attention in language
+modeling. Moreover, despite scaling nearly linearly in sequence length instead
+of quadratically, SSMs are still slower than Transformers due to poor hardware
+utilization. In this paper, we make progress on understanding the expressivity
+gap between SSMs and attention in language modeling, and on reducing the
+hardware barrier between SSMs and attention. First, we use synthetic language
+modeling tasks to understand the gap between SSMs and attention. We find that
+existing SSMs struggle with two capabilities: recalling earlier tokens in the
+sequence and comparing tokens across the sequence. To understand the impact on
+language modeling, we propose a new SSM layer, H3, that is explicitly designed
+for these abilities. H3 matches attention on the synthetic languages and comes
+within 0.4 PPL of Transformers on OpenWebText. Furthermore, a hybrid
+125M-parameter H3-attention model that retains two attention layers
+surprisingly outperforms Transformers on OpenWebText by 1.0 PPL. Next, to
+improve the efficiency of training SSMs on modern hardware, we propose
+FlashConv. FlashConv uses a fused block FFT algorithm to improve efficiency on
+sequences up to 8K, and introduces a novel state passing algorithm that
+exploits the recurrent properties of SSMs to scale to longer sequences.
+FlashConv yields 2$\times$ speedup on the long-range arena benchmark and allows
+hybrid language models to generate text 2.4$\times$ faster than Transformers.
+Using FlashConv, we scale hybrid H3-attention language models up to 2.7B
+parameters on the Pile and find promising initial results, achieving lower
+perplexity than Transformers and outperforming Transformers in zero- and
+few-shot learning on a majority of tasks in the SuperGLUE benchmark.
+"
+CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIP,Runnan Chen,http://arxiv.org/pdf/2301.04926v2.pdf,2023-01-12,['cs.cv'],2301.04926v2.pdf,"  Contrastive Language-Image Pre-training (CLIP) achieves promising results in
+2D zero-shot and few-shot learning. Despite the impressive performance in 2D,
+applying CLIP to help the learning in 3D scene understanding has yet to be
+explored. In this paper, we make the first attempt to investigate how CLIP
+knowledge benefits 3D scene understanding. We propose CLIP2Scene, a simple yet
+effective framework that transfers CLIP knowledge from 2D image-text
+pre-trained models to a 3D point cloud network. We show that the pre-trained 3D
+network yields impressive performance on various downstream tasks, i.e.,
+annotation-free and fine-tuning with labelled data for semantic segmentation.
+Specifically, built upon CLIP, we design a Semantic-driven Cross-modal
+Contrastive Learning framework that pre-trains a 3D network via semantic and
+spatial-temporal consistency regularization. For the former, we first leverage
+CLIP's text semantics to select the positive and negative point samples and
+then employ the contrastive loss to train the 3D network. In terms of the
+latter, we force the consistency between the temporally coherent point cloud
+features and their corresponding image features. We conduct experiments on
+SemanticKITTI, nuScenes, and ScanNet. For the first time, our pre-trained
+network achieves annotation-free 3D semantic segmentation with 20.8% and 25.08%
+mIoU on nuScenes and ScanNet, respectively. When fine-tuned with 1% or 100%
+labelled data, our method significantly outperforms other self-supervised
+methods, with improvements of 8% and 1% mIoU, respectively. Furthermore, we
+demonstrate the generalizability for handling cross-domain datasets. Code is
+publicly available https://github.com/runnanchen/CLIP2Scene.
+"
+An Empirical Evaluation of Using Large Language Models for Automated  Unit Test Generation,Max Schäfer,http://arxiv.org/pdf/2302.06527v3.pdf,2023-02-13,"['cs.se', 'cs.ai']",2302.06527v3.pdf,"  Unit tests play a key role in ensuring the correctness of software. However,
+manually creating unit tests is a laborious task, motivating the need for
+automation. Large Language Models (LLMs) have recently been applied to this
+problem, utilizing additional training or few-shot learning on examples of
+existing tests. This paper presents a large-scale empirical evaluation on the
+effectiveness of LLMs for automated unit test generation without additional
+training or manual effort, providing the LLM with the signature and
+implementation of the function under test, along with usage examples extracted
+from documentation. We also attempt to repair failed generated tests by
+re-prompting the model with the failing test and error message. We implement
+our approach in TestPilot, a test generation tool for JavaScript that
+automatically generates unit tests for all API functions in an npm package. We
+evaluate TestPilot using OpenAI's gpt3.5-turbo LLM on 25 npm packages with a
+total of 1,684 API functions. The generated tests achieve a median statement
+coverage of 70.2% and branch coverage of 52.8%, significantly improving on
+Nessie, a recent feedback-directed JavaScript test generation technique, which
+achieves only 51.3% statement coverage and 25.6% branch coverage. We also find
+that 92.8% of TestPilot's generated tests have no more than 50% similarity with
+existing tests (as measured by normalized edit distance), with none of them
+being exact copies. Finally, we run TestPilot with two additional LLMs,
+OpenAI's older code-cushman-002 LLM and the open LLM StarCoder. Overall, we
+observed similar results with the former (68.2% median statement coverage), and
+somewhat worse results with the latter (54.0% median statement coverage),
+suggesting that the effectiveness of the approach is influenced by the size and
+training set of the LLM, but does not fundamentally depend on the specific
+model.
+"
+On the Opportunities and Challenges of Foundation Models for Geospatial  Artificial Intelligence,Gengchen Mai,http://arxiv.org/pdf/2304.06798v1.pdf,2023-04-13,"['cs.ai', 'cs.cl', 'cs.cv', 'i.2.0; i.2.4; i.2.7; i.2.10; i.5.1']",2304.06798v1.pdf,"  Large pre-trained models, also known as foundation models (FMs), are trained
+in a task-agnostic manner on large-scale data and can be adapted to a wide
+range of downstream tasks by fine-tuning, few-shot, or even zero-shot learning.
+Despite their successes in language and vision tasks, we have yet seen an
+attempt to develop foundation models for geospatial artificial intelligence
+(GeoAI). In this work, we explore the promises and challenges of developing
+multimodal foundation models for GeoAI. We first investigate the potential of
+many existing FMs by testing their performances on seven tasks across multiple
+geospatial subdomains including Geospatial Semantics, Health Geography, Urban
+Geography, and Remote Sensing. Our results indicate that on several geospatial
+tasks that only involve text modality such as toponym recognition, location
+description recognition, and US state-level/county-level dementia time series
+forecasting, these task-agnostic LLMs can outperform task-specific
+fully-supervised models in a zero-shot or few-shot learning setting. However,
+on other geospatial tasks, especially tasks that involve multiple data
+modalities (e.g., POI-based urban function classification, street view
+image-based urban noise intensity classification, and remote sensing image
+scene classification), existing foundation models still underperform
+task-specific models. Based on these observations, we propose that one of the
+major challenges of developing a FM for GeoAI is to address the multimodality
+nature of geospatial tasks. After discussing the distinct challenges of each
+geospatial data modality, we suggest the possibility of a multimodal foundation
+model which can reason over various types of geospatial data through geospatial
+alignments. We conclude this paper by discussing the unique risks and
+challenges to develop such a model for GeoAI.
+"
+Learning to detect an animal sound from five examples,Inês Nolasco,http://arxiv.org/pdf/2305.13210v1.pdf,2023-05-22,"['cs.sd', 'eess.as', 'q-bio.qm']",2305.13210v1.pdf,"  Automatic detection and classification of animal sounds has many applications
+in biodiversity monitoring and animal behaviour. In the past twenty years, the
+volume of digitised wildlife sound available has massively increased, and
+automatic classification through deep learning now shows strong results.
+However, bioacoustics is not a single task but a vast range of small-scale
+tasks (such as individual ID, call type, emotional indication) with wide
+variety in data characteristics, and most bioacoustic tasks do not come with
+strongly-labelled training data. The standard paradigm of supervised learning,
+focussed on a single large-scale dataset and/or a generic pre-trained
+algorithm, is insufficient. In this work we recast bioacoustic sound event
+detection within the AI framework of few-shot learning. We adapt this framework
+to sound event detection, such that a system can be given the annotated
+start/end times of as few as 5 events, and can then detect events in
+long-duration audio -- even when the sound category was not known at the time
+of algorithm training. We introduce a collection of open datasets designed to
+strongly test a system's ability to perform few-shot sound event detections,
+and we present the results of a public contest to address the task. We show
+that prototypical networks are a strong-performing method, when enhanced with
+adaptations for general characteristics of animal sounds. We demonstrate that
+widely-varying sound event durations are an important factor in performance, as
+well as non-stationarity, i.e. gradual changes in conditions throughout the
+duration of a recording. For fine-grained bioacoustic recognition tasks without
+massive annotated training data, our results demonstrate that few-shot sound
+event detection is a powerful new method, strongly outperforming traditional
+signal-processing detection methods in the fully automated scenario.
+"
+The Rise of AI Language Pathologists: Exploring Two-level Prompt  Learning for Few-shot Weakly-supervised Whole Slide Image Classification,Linhao Qu,http://arxiv.org/pdf/2305.17891v1.pdf,2023-05-29,['cs.cv'],2305.17891v1.pdf,"  This paper introduces the novel concept of few-shot weakly supervised
+learning for pathology Whole Slide Image (WSI) classification, denoted as FSWC.
+A solution is proposed based on prompt learning and the utilization of a large
+language model, GPT-4. Since a WSI is too large and needs to be divided into
+patches for processing, WSI classification is commonly approached as a Multiple
+Instance Learning (MIL) problem. In this context, each WSI is considered a bag,
+and the obtained patches are treated as instances. The objective of FSWC is to
+classify both bags and instances with only a limited number of labeled bags.
+Unlike conventional few-shot learning problems, FSWC poses additional
+challenges due to its weak bag labels within the MIL framework. Drawing
+inspiration from the recent achievements of vision-language models (V-L models)
+in downstream few-shot classification tasks, we propose a two-level prompt
+learning MIL framework tailored for pathology, incorporating language prior
+knowledge. Specifically, we leverage CLIP to extract instance features for each
+patch, and introduce a prompt-guided pooling strategy to aggregate these
+instance features into a bag feature. Subsequently, we employ a small number of
+labeled bags to facilitate few-shot prompt learning based on the bag features.
+Our approach incorporates the utilization of GPT-4 in a question-and-answer
+mode to obtain language prior knowledge at both the instance and bag levels,
+which are then integrated into the instance and bag level language prompts.
+Additionally, a learnable component of the language prompts is trained using
+the available few-shot labeled data. We conduct extensive experiments on three
+real WSI datasets encompassing breast cancer, lung cancer, and cervical cancer,
+demonstrating the notable performance of the proposed method in bag and
+instance classification. All codes will be made publicly accessible.
+"
+Effective Test Generation Using Pre-trained Large Language Models and  Mutation Testing,Arghavan Moradi Dakhel,http://arxiv.org/pdf/2308.16557v1.pdf,2023-08-31,['cs.se'],2308.16557v1.pdf,"  One of the critical phases in software development is software testing.
+Testing helps with identifying potential bugs and reducing maintenance costs.
+The goal of automated test generation tools is to ease the development of tests
+by suggesting efficient bug-revealing tests. Recently, researchers have
+leveraged Large Language Models (LLMs) of code to generate unit tests. While
+the code coverage of generated tests was usually assessed, the literature has
+acknowledged that the coverage is weakly correlated with the efficiency of
+tests in bug detection. To improve over this limitation, in this paper, we
+introduce MuTAP for improving the effectiveness of test cases generated by LLMs
+in terms of revealing bugs by leveraging mutation testing. Our goal is achieved
+by augmenting prompts with surviving mutants, as those mutants highlight the
+limitations of test cases in detecting bugs. MuTAP is capable of generating
+effective test cases in the absence of natural language descriptions of the
+Program Under Test (PUTs). We employ different LLMs within MuTAP and evaluate
+their performance on different benchmarks. Our results show that our proposed
+method is able to detect up to 28% more faulty human-written code snippets.
+Among these, 17% remained undetected by both the current state-of-the-art fully
+automated test generation tool (i.e., Pynguin) and zero-shot/few-shot learning
+approaches on LLMs. Furthermore, MuTAP achieves a Mutation Score (MS) of 93.57%
+on synthetic buggy code, outperforming all other approaches in our evaluation.
+Our findings suggest that although LLMs can serve as a useful tool to generate
+test cases, they require specific post-processing steps to enhance the
+effectiveness of the generated test cases which may suffer from syntactic or
+functional errors and may be ineffective in detecting certain types of bugs and
+testing corner cases PUTs.
+"
+LLM4SGG: Large Language Model for Weakly Supervised Scene Graph  Generation,Kibum Kim,http://arxiv.org/pdf/2310.10404v4.pdf,2023-10-16,['cs.cv'],2310.10404v4.pdf,"  Weakly-Supervised Scene Graph Generation (WSSGG) research has recently
+emerged as an alternative to the fully-supervised approach that heavily relies
+on costly annotations. In this regard, studies on WSSGG have utilized image
+captions to obtain unlocalized triplets while primarily focusing on grounding
+the unlocalized triplets over image regions. However, they have overlooked the
+two issues involved in the triplet formation process from the captions: 1)
+Semantic over-simplification issue arises when extracting triplets from
+captions, where fine-grained predicates in captions are undesirably converted
+into coarse-grained predicates, resulting in a long-tailed predicate
+distribution, and 2) Low-density scene graph issue arises when aligning the
+triplets in the caption with entity/predicate classes of interest, where many
+triplets are discarded and not used in training, leading to insufficient
+supervision. To tackle the two issues, we propose a new approach, i.e., Large
+Language Model for weakly-supervised SGG (LLM4SGG), where we mitigate the two
+issues by leveraging the LLM's in-depth understanding of language and reasoning
+ability during the extraction of triplets from captions and alignment of
+entity/predicate classes with target data. To further engage the LLM in these
+processes, we adopt the idea of Chain-of-Thought and the in-context few-shot
+learning strategy. To validate the effectiveness of LLM4SGG, we conduct
+extensive experiments on Visual Genome and GQA datasets, showing significant
+improvements in both Recall@K and mean Recall@K compared to the
+state-of-the-art WSSGG methods. A further appeal is that LLM4SGG is
+data-efficient, enabling effective model training with a small amount of
+training images.
+"
+Language Models are Few-Shot Learners,Tom B. Brown,http://arxiv.org/pdf/2005.14165v4.pdf,2020-05-28,['cs.cl'],2005.14165v4.pdf,"  Recent work has demonstrated substantial gains on many NLP tasks and
+benchmarks by pre-training on a large corpus of text followed by fine-tuning on
+a specific task. While typically task-agnostic in architecture, this method
+still requires task-specific fine-tuning datasets of thousands or tens of
+thousands of examples. By contrast, humans can generally perform a new language
+task from only a few examples or from simple instructions - something which
+current NLP systems still largely struggle to do. Here we show that scaling up
+language models greatly improves task-agnostic, few-shot performance, sometimes
+even reaching competitiveness with prior state-of-the-art fine-tuning
+approaches. Specifically, we train GPT-3, an autoregressive language model with
+175 billion parameters, 10x more than any previous non-sparse language model,
+and test its performance in the few-shot setting. For all tasks, GPT-3 is
+applied without any gradient updates or fine-tuning, with tasks and few-shot
+demonstrations specified purely via text interaction with the model. GPT-3
+achieves strong performance on many NLP datasets, including translation,
+question-answering, and cloze tasks, as well as several tasks that require
+on-the-fly reasoning or domain adaptation, such as unscrambling words, using a
+novel word in a sentence, or performing 3-digit arithmetic. At the same time,
+we also identify some datasets where GPT-3's few-shot learning still struggles,
+as well as some datasets where GPT-3 faces methodological issues related to
+training on large web corpora. Finally, we find that GPT-3 can generate samples
+of news articles which human evaluators have difficulty distinguishing from
+articles written by humans. We discuss broader societal impacts of this finding
+and of GPT-3 in general.
+"
+MasakhaNEWS: News Topic Classification for African languages,David Ifeoluwa Adelani,http://arxiv.org/pdf/2304.09972v2.pdf,2023-04-19,['cs.cl'],2304.09972v2.pdf,"  African languages are severely under-represented in NLP research due to lack
+of datasets covering several NLP tasks. While there are individual language
+specific datasets that are being expanded to different tasks, only a handful of
+NLP tasks (e.g. named entity recognition and machine translation) have
+standardized benchmark datasets covering several geographical and
+typologically-diverse African languages. In this paper, we develop MasakhaNEWS
+-- a new benchmark dataset for news topic classification covering 16 languages
+widely spoken in Africa. We provide an evaluation of baseline models by
+training classical machine learning models and fine-tuning several language
+models. Furthermore, we explore several alternatives to full fine-tuning of
+language models that are better suited for zero-shot and few-shot learning such
+as cross-lingual parameter-efficient fine-tuning (like MAD-X), pattern
+exploiting training (PET), prompting language models (like ChatGPT), and
+prompt-free sentence transformer fine-tuning (SetFit and Cohere Embedding API).
+Our evaluation in zero-shot setting shows the potential of prompting ChatGPT
+for news topic classification in low-resource African languages, achieving an
+average performance of 70 F1 points without leveraging additional supervision
+like MAD-X. In few-shot setting, we show that with as little as 10 examples per
+label, we achieved more than 90\% (i.e. 86.0 F1 points) of the performance of
+full supervised training (92.6 F1 points) leveraging the PET approach.
+"
+Exploring Effectiveness of GPT-3 in Grammatical Error Correction: A  Study on Performance and Controllability in Prompt-Based Methods,Mengsay Loem,http://arxiv.org/pdf/2305.18156v1.pdf,2023-05-29,"['cs.cl', 'cs.ai']",2305.18156v1.pdf,"  Large-scale pre-trained language models such as GPT-3 have shown remarkable
+performance across various natural language processing tasks. However, applying
+prompt-based methods with GPT-3 for Grammatical Error Correction (GEC) tasks
+and their controllability remains underexplored. Controllability in GEC is
+crucial for real-world applications, particularly in educational settings,
+where the ability to tailor feedback according to learner levels and specific
+error types can significantly enhance the learning process. This paper
+investigates the performance and controllability of prompt-based methods with
+GPT-3 for GEC tasks using zero-shot and few-shot setting. We explore the impact
+of task instructions and examples on GPT-3's output, focusing on controlling
+aspects such as minimal edits, fluency edits, and learner levels. Our findings
+demonstrate that GPT-3 could effectively perform GEC tasks, outperforming
+existing supervised and unsupervised approaches. We also showed that GPT-3
+could achieve controllability when appropriate task instructions and examples
+are given.
+"
+Causal Intervention-based Prompt Debiasing for Event Argument Extraction,Jiaju Lin,http://arxiv.org/pdf/2210.01561v1.pdf,2022-10-04,"['cs.cl', 'cs.ai']",2210.01561v1.pdf,"  Prompt-based methods have become increasingly popular among information
+extraction tasks, especially in low-data scenarios. By formatting a finetune
+task into a pre-training objective, prompt-based methods resolve the data
+scarce problem effectively. However, seldom do previous research investigate
+the discrepancy among different prompt formulating strategies. In this work, we
+compare two kinds of prompts, name-based prompt and ontology-base prompt, and
+reveal how ontology-base prompt methods exceed its counterpart in zero-shot
+event argument extraction (EAE) . Furthermore, we analyse the potential risk in
+ontology-base prompts via a causal view and propose a debias method by causal
+intervention. Experiments on two benchmarks demonstrate that modified by our
+debias method, the baseline model becomes both more effective and robust, with
+significant improvement in the resistance to adversarial attacks.
+"
+When Prompt-based Incremental Learning Does Not Meet Strong Pretraining,Yu-Ming Tang,http://arxiv.org/pdf/2308.10445v1.pdf,2023-08-21,['cs.cv'],2308.10445v1.pdf,"  Incremental learning aims to overcome catastrophic forgetting when learning
+deep networks from sequential tasks. With impressive learning efficiency and
+performance, prompt-based methods adopt a fixed backbone to sequential tasks by
+learning task-specific prompts. However, existing prompt-based methods heavily
+rely on strong pretraining (typically trained on ImageNet-21k), and we find
+that their models could be trapped if the potential gap between the pretraining
+task and unknown future tasks is large. In this work, we develop a learnable
+Adaptive Prompt Generator (APG). The key is to unify the prompt retrieval and
+prompt learning processes into a learnable prompt generator. Hence, the whole
+prompting process can be optimized to reduce the negative effects of the gap
+between tasks effectively. To make our APG avoid learning ineffective
+knowledge, we maintain a knowledge pool to regularize APG with the feature
+distribution of each class. Extensive experiments show that our method
+significantly outperforms advanced methods in exemplar-free incremental
+learning without (strong) pretraining. Besides, under strong retraining, our
+method also has comparable performance to existing prompt-based models, showing
+that our method can still benefit from pretraining. Codes can be found at
+https://github.com/TOM-tym/APG
+"
+Zero-shot Domain Adaptation for Neural Machine Translation with  Retrieved Phrase-level Prompts,Zewei Sun,http://arxiv.org/pdf/2209.11409v1.pdf,2022-09-23,['cs.cl'],2209.11409v1.pdf,"  Domain adaptation is an important challenge for neural machine translation.
+However, the traditional fine-tuning solution requires multiple extra training
+and yields a high cost. In this paper, we propose a non-tuning paradigm,
+resolving domain adaptation with a prompt-based method. Specifically, we
+construct a bilingual phrase-level database and retrieve relevant pairs from it
+as a prompt for the input sentences. By utilizing Retrieved Phrase-level
+Prompts (RePP), we effectively boost the translation quality. Experiments show
+that our method improves domain-specific machine translation for 6.2 BLEU
+scores and improves translation constraints for 11.5% accuracy without
+additional training.
+"
+NSP-BERT: A Prompt-based Few-Shot Learner Through an Original  Pre-training Task--Next Sentence Prediction,Yi Sun,http://arxiv.org/pdf/2109.03564v2.pdf,2021-09-08,"['cs.cl', 'cs.ai']",2109.03564v2.pdf,"  Using prompts to utilize language models to perform various downstream tasks,
+also known as prompt-based learning or prompt-learning, has lately gained
+significant success in comparison to the pre-train and fine-tune paradigm.
+Nonetheless, virtually all prompt-based methods are token-level, meaning they
+all utilize GPT's left-to-right language model or BERT's masked language model
+to perform cloze-style tasks. In this paper, we attempt to accomplish several
+NLP tasks in the zero-shot scenario using a BERT original pre-training task
+abandoned by RoBERTa and other models--Next Sentence Prediction (NSP). Unlike
+token-level techniques, our sentence-level prompt-based method NSP-BERT does
+not need to fix the length of the prompt or the position to be predicted,
+allowing it to handle tasks such as entity linking with ease. Based on the
+characteristics of NSP-BERT, we offer several quick building templates for
+various downstream tasks. We suggest a two-stage prompt method for word sense
+disambiguation tasks in particular. Our strategies for mapping the labels
+significantly enhance the model's performance on sentence pair tasks. On the
+FewCLUE benchmark, our NSP-BERT outperforms other zero-shot methods on most of
+these tasks and comes close to the few-shot methods.
+"
+Introducing Language Guidance in Prompt-based Continual Learning,Muhammad Gul Zain Ali Khan,http://arxiv.org/pdf/2308.15827v1.pdf,2023-08-30,['cs.cv'],2308.15827v1.pdf,"  Continual Learning aims to learn a single model on a sequence of tasks
+without having access to data from previous tasks. The biggest challenge in the
+domain still remains catastrophic forgetting: a loss in performance on seen
+classes of earlier tasks. Some existing methods rely on an expensive replay
+buffer to store a chunk of data from previous tasks. This, while promising,
+becomes expensive when the number of tasks becomes large or data can not be
+stored for privacy reasons. As an alternative, prompt-based methods have been
+proposed that store the task information in a learnable prompt pool. This
+prompt pool instructs a frozen image encoder on how to solve each task. While
+the model faces a disjoint set of classes in each task in this setting, we
+argue that these classes can be encoded to the same embedding space of a
+pre-trained language encoder. In this work, we propose Language Guidance for
+Prompt-based Continual Learning (LGCL) as a plug-in for prompt-based methods.
+LGCL is model agnostic and introduces language guidance at the task level in
+the prompt pool and at the class level on the output feature of the vision
+encoder. We show with extensive experimentation that LGCL consistently improves
+the performance of prompt-based continual learning methods to set a new
+state-of-the art. LGCL achieves these performance improvements without needing
+any additional learnable parameters.
+"
+Enable Language Models to Implicitly Learn Self-Improvement From Data,Ziqi Wang,http://arxiv.org/pdf/2310.00898v2.pdf,2023-10-02,['cs.cl'],2310.00898v2.pdf,"  Large Language Models (LLMs) have demonstrated remarkable capabilities in
+open-ended text generation tasks. However, the inherent open-ended nature of
+these tasks implies that there is always room for improvement in the quality of
+model responses. To address this challenge, various approaches have been
+proposed to enhance the performance of LLMs. There has been a growing focus on
+enabling LLMs to self-improve their response quality, thereby reducing the
+reliance on extensive human annotation efforts for collecting diverse and
+high-quality training data. Recently, prompting-based methods have been widely
+explored among self-improvement methods owing to their effectiveness,
+efficiency, and convenience. However, those methods usually require explicitly
+and thoroughly written rubrics as inputs to LLMs. It is expensive and
+challenging to manually derive and provide all necessary rubrics with a
+real-world complex goal for improvement (e.g., being more helpful and less
+harmful). To this end, we propose an ImPlicit Self-ImprovemenT (PIT) framework
+that implicitly learns the improvement goal from human preference data. PIT
+only requires preference data that are used to train reward models without
+extra human efforts. Specifically, we reformulate the training objective of
+reinforcement learning from human feedback (RLHF) -- instead of maximizing
+response quality for a given input, we maximize the quality gap of the response
+conditioned on a reference response. In this way, PIT is implicitly trained
+with the improvement goal of better aligning with human preferences.
+Experiments on two real-world datasets and one synthetic dataset show that our
+method significantly outperforms prompting-based methods.
+"
+MEmoBERT: Pre-training Model with Prompt-based Learning for Multimodal  Emotion Recognition,Jinming Zhao,http://arxiv.org/pdf/2111.00865v1.pdf,2021-10-27,"['cs.cv', 'eess.iv']",2111.00865v1.pdf,"  Multimodal emotion recognition study is hindered by the lack of labelled
+corpora in terms of scale and diversity, due to the high annotation cost and
+label ambiguity. In this paper, we propose a pre-training model
+\textbf{MEmoBERT} for multimodal emotion recognition, which learns multimodal
+joint representations through self-supervised learning from large-scale
+unlabeled video data that come in sheer volume. Furthermore, unlike the
+conventional ""pre-train, finetune"" paradigm, we propose a prompt-based method
+that reformulates the downstream emotion classification task as a masked text
+prediction one, bringing the downstream task closer to the pre-training.
+Extensive experiments on two benchmark datasets, IEMOCAP and MSP-IMPROV, show
+that our proposed MEmoBERT significantly enhances emotion recognition
+performance.
+"
+PSG: Prompt-based Sequence Generation for Acronym Extraction,Bin Li,http://arxiv.org/pdf/2111.14301v2.pdf,2021-11-29,"['cs.cl', 'cs.ai']",2111.14301v2.pdf,"  Acronym extraction aims to find acronyms (i.e., short-forms) and their
+meanings (i.e., long-forms) from the documents, which is important for
+scientific document understanding (SDU@AAAI-22) tasks. Previous works are
+devoted to modeling this task as a paragraph-level sequence labeling problem.
+However, it lacks the effective use of the external knowledge, especially when
+the datasets are in a low-resource setting. Recently, the prompt-based method
+with the vast pre-trained language model can significantly enhance the
+performance of the low-resourced downstream tasks. In this paper, we propose a
+Prompt-based Sequence Generation (PSG) method for the acronym extraction task.
+Specifically, we design a template for prompting the extracted acronym texts
+with auto-regression. A position extraction algorithm is designed for
+extracting the position of the generated answers. The results on the acronym
+extraction of Vietnamese and Persian in a low-resource setting show that the
+proposed method outperforms all other competitive state-of-the-art (SOTA)
+methods.
+"
+Chemical Identification and Indexing in PubMed Articles via BERT and  Text-to-Text Approaches,Virginia Adams,http://arxiv.org/pdf/2111.15622v1.pdf,2021-11-30,['cs.cl'],2111.15622v1.pdf,"  The Biocreative VII Track-2 challenge consists of named entity recognition,
+entity-linking (or entity-normalization), and topic indexing tasks -- with
+entities and topics limited to chemicals for this challenge. Named entity
+recognition is a well-established problem and we achieve our best performance
+with BERT-based BioMegatron models. We extend our BERT-based approach to the
+entity linking task. After the second stage of pretraining BioBERT with a
+metric-learning loss strategy called self-alignment pretraining (SAP), we link
+entities based on the cosine similarity between their SAP-BioBERT word
+embeddings. Despite the success of our named entity recognition experiments, we
+find the chemical indexing task generally more challenging.
+  In addition to conventional NER methods, we attempt both named entity
+recognition and entity linking with a novel text-to-text or ""prompt"" based
+method that uses generative language models such as T5 and GPT. We achieve
+encouraging results with this new approach.
+"
+AdaPrompt: Adaptive Model Training for Prompt-based NLP,Yulong Chen,http://arxiv.org/pdf/2202.04824v2.pdf,2022-02-10,['cs.cl'],2202.04824v2.pdf,"  Prompt-based learning, with its capability to tackle zero-shot and few-shot
+NLP tasks, has gained much attention in community. The main idea is to bridge
+the gap between NLP downstream tasks and language modeling (LM), by mapping
+these tasks into natural language prompts, which are then filled by pre-trained
+language models (PLMs). However, for prompt learning, there are still two
+salient gaps between NLP tasks and pretraining. First, prompt information is
+not necessarily sufficiently present during LM pretraining. Second,
+task-specific data are not necessarily well represented during pretraining. We
+address these two issues by proposing AdaPrompt, adaptively retrieving external
+data for continual pretraining of PLMs by making use of both task and prompt
+characteristics. In addition, we make use of knowledge in Natural Language
+Inference models for deriving adaptive verbalizers. Experimental results on
+five NLP benchmarks show that AdaPrompt can improve over standard PLMs in
+few-shot settings. In addition, in zero-shot settings, our method outperforms
+standard prompt-based methods by up to 26.35\% relative error reduction.
+"
+Prompting to Distill: Boosting Data-Free Knowledge Distillation via  Reinforced Prompt,Xinyin Ma,http://arxiv.org/pdf/2205.07523v1.pdf,2022-05-16,['cs.cl'],2205.07523v1.pdf,"  Data-free knowledge distillation (DFKD) conducts knowledge distillation via
+eliminating the dependence of original training data, and has recently achieved
+impressive results in accelerating pre-trained language models. At the heart of
+DFKD is to reconstruct a synthetic dataset by inverting the parameters of the
+uncompressed model. Prior DFKD approaches, however, have largely relied on
+hand-crafted priors of the target data distribution for the reconstruction,
+which can be inevitably biased and often incompetent to capture the intrinsic
+distributions. To address this problem, we propose a prompt-based method,
+termed as PromptDFD, that allows us to take advantage of learned language
+priors, which effectively harmonizes the synthetic sentences to be semantically
+and grammatically correct. Specifically, PromptDFD leverages a pre-trained
+generative model to provide language priors and introduces a reinforced topic
+prompter to control data synthesis, making the generated samples thematically
+relevant and semantically plausible, and thus friendly to downstream tasks. As
+shown in our experiments, the proposed method substantially improves the
+synthesis quality and achieves considerable improvements on distillation
+performance. In some cases, PromptDFD even gives rise to results on par with
+those from the data-driven knowledge distillation with access to the original
+training data.
+"
+"Fewer Errors, but More Stereotypes? The Effect of Model Size on Gender  Bias",Yarden Tal,http://arxiv.org/pdf/2206.09860v1.pdf,2022-06-20,['cs.cl'],2206.09860v1.pdf,"  The size of pretrained models is increasing, and so is their performance on a
+variety of NLP tasks. However, as their memorization capacity grows, they might
+pick up more social biases. In this work, we examine the connection between
+model size and its gender bias (specifically, occupational gender bias). We
+measure bias in three masked language model families (RoBERTa, DeBERTa, and T5)
+in two setups: directly using prompt based method, and using a downstream task
+(Winogender). We find on the one hand that larger models receive higher bias
+scores on the former task, but when evaluated on the latter, they make fewer
+gender errors. To examine these potentially conflicting results, we carefully
+investigate the behavior of the different models on Winogender. We find that
+while larger models outperform smaller ones, the probability that their
+mistakes are caused by gender bias is higher. Moreover, we find that the
+proportion of stereotypical errors compared to anti-stereotypical ones grows
+with the model size. Our findings highlight the potential risks that can arise
+from increasing model size.
+"
+PromptAttack: Prompt-based Attack for Language Models via Gradient  Search,Yundi Shi,http://arxiv.org/pdf/2209.01882v1.pdf,2022-09-05,"['cs.cl', 'cs.ai', 'cs.cr']",2209.01882v1.pdf,"  As the pre-trained language models (PLMs) continue to grow, so do the
+hardware and data requirements for fine-tuning PLMs. Therefore, the researchers
+have come up with a lighter method called \textit{Prompt Learning}. However,
+during the investigations, we observe that the prompt learning methods are
+vulnerable and can easily be attacked by some illegally constructed prompts,
+resulting in classification errors, and serious security problems for PLMs.
+Most of the current research ignores the security issue of prompt-based
+methods. Therefore, in this paper, we propose a malicious prompt template
+construction method (\textbf{PromptAttack}) to probe the security performance
+of PLMs. Several unfriendly template construction approaches are investigated
+to guide the model to misclassify the task. Extensive experiments on three
+datasets and three PLMs prove the effectiveness of our proposed approach
+PromptAttack. We also conduct experiments to verify that our method is
+applicable in few-shot scenarios.
+"
+ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational  Finance Question Answering,Zhiyu Chen,http://arxiv.org/pdf/2210.03849v1.pdf,2022-10-07,['cs.cl'],2210.03849v1.pdf,"  With the recent advance in large pre-trained language models, researchers
+have achieved record performances in NLP tasks that mostly focus on language
+pattern matching. The community is experiencing the shift of the challenge from
+how to model language to the imitation of complex reasoning abilities like
+human beings. In this work, we investigate the application domain of finance
+that involves real-world, complex numerical reasoning. We propose a new
+large-scale dataset, ConvFinQA, aiming to study the chain of numerical
+reasoning in conversational question answering. Our dataset poses great
+challenge in modeling long-range, complex numerical reasoning paths in
+real-world conversations. We conduct comprehensive experiments and analyses
+with both the neural symbolic methods and the prompting-based methods, to
+provide insights into the reasoning mechanisms of these two divisions. We
+believe our new dataset should serve as a valuable resource to push forward the
+exploration of real-world, complex reasoning tasks as the next research focus.
+Our dataset and code is publicly available at
+https://github.com/czyssrs/ConvFinQA.
+"
+Can Language Models Be Specific? How?,Jie Huang,http://arxiv.org/pdf/2210.05159v2.pdf,2022-10-11,"['cs.cl', 'cs.ai']",2210.05159v2.pdf,"  ""He is a person"", ""Paris is located on the earth"". Both statements are
+correct but meaningless - due to lack of specificity. In this paper, we propose
+to measure how specific the language of pre-trained language models (PLMs) is.
+To achieve this, we introduce a novel approach to build a benchmark for
+specificity testing by forming masked token prediction tasks with prompts. For
+instance, given ""Toronto is located in [MASK]."", we want to test whether a more
+specific answer will be better filled in by PLMs, e.g., Ontario instead of
+Canada. From our evaluations, we show that existing PLMs have only a slight
+preference for more specific answers. We identify underlying factors affecting
+the specificity and design two prompt-based methods to improve the specificity.
+Results show that the specificity of the models can be improved by the proposed
+methods without additional training. We hope this work can bring to awareness
+the notion of specificity of language models and encourage the research
+community to further explore this important but understudied problem.
+"
+Multilingual Relation Classification via Efficient and Effective  Prompting,Yuxuan Chen,http://arxiv.org/pdf/2210.13838v2.pdf,2022-10-25,"['cs.cl', 'cs.lg']",2210.13838v2.pdf,"  Prompting pre-trained language models has achieved impressive performance on
+various NLP tasks, especially in low data regimes. Despite the success of
+prompting in monolingual settings, applying prompt-based methods in
+multilingual scenarios has been limited to a narrow set of tasks, due to the
+high cost of handcrafting multilingual prompts. In this paper, we present the
+first work on prompt-based multilingual relation classification (RC), by
+introducing an efficient and effective method that constructs prompts from
+relation triples and involves only minimal translation for the class labels. We
+evaluate its performance in fully supervised, few-shot and zero-shot scenarios,
+and analyze its effectiveness across 14 languages, prompt variants, and
+English-task training in cross-lingual settings. We find that in both fully
+supervised and few-shot scenarios, our prompt method beats competitive
+baselines: fine-tuning XLM-R_EM and null prompts. It also outperforms the
+random baseline by a large margin in zero-shot experiments. Our method requires
+little in-language knowledge and can be used as a strong baseline for similar
+multilingual classification tasks.
+"
+Steps towards prompt-based creation of virtual worlds,Jasmine Roberts,http://arxiv.org/pdf/2211.05875v1.pdf,2022-11-10,"['cs.hc', 'cs.ai', 'cs.lg', 'cs.mm']",2211.05875v1.pdf,"  Large language models trained for code generation can be applied to speaking
+virtual worlds into existence (creating virtual worlds). In this work we show
+that prompt-based methods can both accelerate in-VR level editing, as well as
+can become part of gameplay rather than just part of game development. As an
+example, we present Codex VR Pong which shows non-deterministic game mechanics
+using generative processes to not only create static content but also
+non-trivial interactions between 3D objects. This demonstration naturally leads
+to an integral discussion on how one would evaluate and benchmark experiences
+created by generative models - as there are no qualitative or quantitative
+metrics that apply in these scenarios. We conclude by discussing impending
+challenges of AI-assisted co-creation in VR.
+"
+SPE: Symmetrical Prompt Enhancement for Fact Probing,Yiyuan Li,http://arxiv.org/pdf/2211.07078v1.pdf,2022-11-14,"['cs.cl', 'cs.ai', 'cs.lg']",2211.07078v1.pdf,"  Pretrained language models (PLMs) have been shown to accumulate factual
+knowledge during pretrainingng (Petroni et al., 2019). Recent works probe PLMs
+for the extent of this knowledge through prompts either in discrete or
+continuous forms. However, these methods do not consider symmetry of the task:
+object prediction and subject prediction. In this work, we propose Symmetrical
+Prompt Enhancement (SPE), a continuous prompt-based method for factual probing
+in PLMs that leverages the symmetry of the task by constructing symmetrical
+prompts for subject and object prediction. Our results on a popular factual
+probing dataset, LAMA, show significant improvement of SPE over previous
+probing methods.
+"
+Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual  Conditional Generation with Interaction,Jonathan Pilault,http://arxiv.org/pdf/2301.10309v1.pdf,2023-01-24,"['cs.lg', 'cs.ai', 'cs.cl']",2301.10309v1.pdf,"  Crosslingual conditional generation (e.g., machine translation) has long
+enjoyed the benefits of scaling. Nonetheless, there are still issues that scale
+alone may not overcome. A source query in one language, for instance, may yield
+several translation options in another language without any extra context. Only
+one translation could be acceptable however, depending on the translator's
+preferences and goals. Choosing the incorrect option might significantly affect
+translation usefulness and quality. We propose a novel method interactive-chain
+prompting -- a series of question, answering and generation intermediate steps
+between a Translator model and a User model -- that reduces translations into a
+list of subproblems addressing ambiguities and then resolving such subproblems
+before producing the final text to be translated. To check ambiguity resolution
+capabilities and evaluate translation quality, we create a dataset exhibiting
+different linguistic phenomena which leads to ambiguities at inference for four
+languages. To encourage further exploration in this direction, we release all
+datasets. We note that interactive-chain prompting, using eight interactions as
+exemplars, consistently surpasses prompt-based methods with direct access to
+background information to resolve ambiguities.
+"
+Evaluating the Robustness of Discrete Prompts,Yoichi Ishibashi,http://arxiv.org/pdf/2302.05619v1.pdf,2023-02-11,"['cs.cl', 'cs.ai']",2302.05619v1.pdf,"  Discrete prompts have been used for fine-tuning Pre-trained Language Models
+for diverse NLP tasks. In particular, automatic methods that generate discrete
+prompts from a small set of training instances have reported superior
+performance. However, a closer look at the learnt prompts reveals that they
+contain noisy and counter-intuitive lexical constructs that would not be
+encountered in manually-written prompts. This raises an important yet
+understudied question regarding the robustness of automatically learnt discrete
+prompts when used in downstream tasks. To address this question, we conduct a
+systematic study of the robustness of discrete prompts by applying carefully
+designed perturbations into an application using AutoPrompt and then measure
+their performance in two Natural Language Inference (NLI) datasets. Our
+experimental results show that although the discrete prompt-based method
+remains relatively robust against perturbations to NLI inputs, they are highly
+sensitive to other types of perturbations such as shuffling and deletion of
+prompt tokens. Moreover, they generalize poorly across different NLI datasets.
+We hope our findings will inspire future work on robust discrete prompt
+learning.
+"
+Stabilized In-Context Learning with Pre-trained Language Models for Few  Shot Dialogue State Tracking,Derek Chen,http://arxiv.org/pdf/2302.05932v1.pdf,2023-02-12,['cs.cl'],2302.05932v1.pdf,"  Prompt-based methods with large pre-trained language models (PLMs) have shown
+impressive unaided performance across many NLP tasks. These models improve even
+further with the addition of a few labeled in-context exemplars to guide output
+generation. However, for more complex tasks such as dialogue state tracking
+(DST), designing prompts that reliably convey the desired intent is nontrivial,
+leading to unstable results. Furthermore, building in-context exemplars for
+dialogue tasks is difficult because conversational contexts are long while
+model input lengths are relatively short. To overcome these issues we first
+adapt a meta-learning scheme to the dialogue domain which stabilizes the
+ability of the model to perform well under various prompts. We additionally
+design a novel training method to improve upon vanilla retrieval mechanisms to
+find ideal in-context examples. Finally, we introduce a saliency model to limit
+dialogue text length, allowing us to include more exemplars per query. In
+effect, we are able to achieve highly competitive results for few-shot DST on
+MultiWOZ.
+"
+Zero-Shot Information Extraction via Chatting with ChatGPT,Xiang Wei,http://arxiv.org/pdf/2302.10205v1.pdf,2023-02-20,['cs.cl'],2302.10205v1.pdf,"  Zero-shot information extraction (IE) aims to build IE systems from the
+unannotated text. It is challenging due to involving little human intervention.
+Challenging but worthwhile, zero-shot IE reduces the time and effort that data
+labeling takes. Recent efforts on large language models (LLMs, e.g., GPT-3,
+ChatGPT) show promising performance on zero-shot settings, thus inspiring us to
+explore prompt-based methods. In this work, we ask whether strong IE models can
+be constructed by directly prompting LLMs. Specifically, we transform the
+zero-shot IE task into a multi-turn question-answering problem with a two-stage
+framework (ChatIE). With the power of ChatGPT, we extensively evaluate our
+framework on three IE tasks: entity-relation triple extract, named entity
+recognition, and event extraction. Empirical results on six datasets across two
+languages show that ChatIE achieves impressive performance and even surpasses
+some full-shot models on several datasets (e.g., NYT11-HRL). We believe that
+our work could shed light on building IE models with limited resources.
+"
+Divide and Prompt: Chain of Thought Prompting for Text-to-SQL,Xiping Liu,http://arxiv.org/pdf/2304.11556v1.pdf,2023-04-23,"['cs.cl', 'cs.ai']",2304.11556v1.pdf,"  Chain-of-thought (CoT) prompting combined with large language models (LLMs)
+have achieved encouraging results on complex reasoning tasks. Text-to-SQL is a
+critical semantic parsing task that converts natural language questions into
+SQL statements, involving a complex reasoning process. However, there is little
+work about using CoT prompting to activate LLM's reasoning capabilities on
+Text-to-SQL tasks. In this work, we propose a new paradigm for prompting
+Text-to-SQL tasks, called Divide-and-Prompt, which first divides the task into
+subtasks, and then approach each subtask through CoT. We present 3
+prompting-based methods to enhance the Text-to-SQL ability of LLMs. Experiments
+show that these prompts guide LLMs to generate Text-to-SQL with higher
+execution accuracy.
+"
+Few-shot Event Detection: An Empirical Study and a Unified View,Yubo Ma,http://arxiv.org/pdf/2305.01901v2.pdf,2023-05-03,"['cs.cl', 'cs.ai']",2305.01901v2.pdf,"  Few-shot event detection (ED) has been widely studied, while this brings
+noticeable discrepancies, e.g., various motivations, tasks, and experimental
+settings, that hinder the understanding of models for future progress.This
+paper presents a thorough empirical study, a unified view of ED models, and a
+better unified baseline. For fair evaluation, we compare 12 representative
+methods on three datasets, which are roughly grouped into prompt-based and
+prototype-based models for detailed analysis. Experiments consistently
+demonstrate that prompt-based methods, including ChatGPT, still significantly
+trail prototype-based methods in terms of overall performance. To investigate
+their superior performance, we break down their design elements along several
+dimensions and build a unified framework on prototype-based methods. Under such
+unified view, each prototype-method can be viewed a combination of different
+modules from these design elements. We further combine all advantageous modules
+and propose a simple yet effective baseline, which outperforms existing methods
+by a large margin (e.g., 2.7% F1 gains under low-resource setting).
+"
+PURR: Efficiently Editing Language Model Hallucinations by Denoising  Language Model Corruptions,Anthony Chen,http://arxiv.org/pdf/2305.14908v1.pdf,2023-05-24,['cs.cl'],2305.14908v1.pdf,"  The remarkable capabilities of large language models have been accompanied by
+a persistent drawback: the generation of false and unsubstantiated claims
+commonly known as ""hallucinations"". To combat this issue, recent research has
+introduced approaches that involve editing and attributing the outputs of
+language models, particularly through prompt-based editing. However, the
+inference cost and speed of using large language models for editing currently
+bottleneck prompt-based methods. These bottlenecks motivate the training of
+compact editors, which is challenging due to the scarcity of training data for
+this purpose. To overcome these challenges, we exploit the power of large
+language models to introduce corruptions (i.e., noise) into text and
+subsequently fine-tune compact editors to denoise the corruptions by
+incorporating relevant evidence. Our methodology is entirely unsupervised and
+provides us with faux hallucinations for training in any domain. Our Petite
+Unsupervised Research and Revision model, PURR, not only improves attribution
+over existing editing methods based on fine-tuning and prompting, but also
+achieves faster execution times by orders of magnitude.
+"
+Syntax-aware Hybrid prompt model for Few-shot multi-modal sentiment  analysis,Zikai Zhou,http://arxiv.org/pdf/2306.01312v2.pdf,2023-06-02,['cs.cl'],2306.01312v2.pdf,"  Multimodal Sentiment Analysis (MSA) has been a popular topic in natural
+language processing nowadays, at both sentence and aspect level. However, the
+existing approaches almost require large-size labeled datasets, which bring
+about large consumption of time and resources. Therefore, it is practical to
+explore the method for few-shot sentiment analysis in cross-modalities.
+Previous works generally execute on textual modality, using the prompt-based
+methods, mainly two types: hand-crafted prompts and learnable prompts. The
+existing approach in few-shot multi-modality sentiment analysis task has
+utilized both methods, separately. We further design a hybrid pattern that can
+combine one or more fixed hand-crafted prompts and learnable prompts and
+utilize the attention mechanisms to optimize the prompt encoder. The
+experiments on both sentence-level and aspect-level datasets prove that we get
+a significant outperformance.
+"
+Scaling Sentence Embeddings with Large Language Models,Ting Jiang,http://arxiv.org/pdf/2307.16645v1.pdf,2023-07-31,['cs.cl'],2307.16645v1.pdf,"  Large language models (LLMs) have recently garnered significant interest.
+With in-context learning, LLMs achieve impressive results in various natural
+language tasks. However, the application of LLMs to sentence embeddings remains
+an area of ongoing research. In this work, we propose an in-context
+learning-based method aimed at improving sentence embeddings performance. Our
+approach involves adapting the previous prompt-based representation method for
+autoregressive models, constructing a demonstration set that enables LLMs to
+perform in-context learning, and scaling up the LLMs to different model sizes.
+Through extensive experiments, in-context learning enables LLMs to generate
+high-quality sentence embeddings without any fine-tuning. It helps LLMs achieve
+performance comparable to current contrastive learning methods. By scaling
+model size, we find scaling to more than tens of billion parameters harms the
+performance on semantic textual similarity (STS) tasks. However, the largest
+model outperforms other counterparts and achieves the new state-of-the-art
+result on transfer tasks. We also fine-tune LLMs with current contrastive
+learning approach, and the 2.7B OPT model, incorporating our prompt-based
+method, surpasses the performance of 4.8B ST5, achieving the new
+state-of-the-art results on STS tasks. Our code is available at
+https://github.com/kongds/scaling_sentemb.
+"
+Unified Multimodal Pre-training and Prompt-based Tuning for  Vision-Language Understanding and Generation,Tianyi Liu,http://arxiv.org/pdf/2112.05587v2.pdf,2021-12-10,"['cs.cv', 'cs.cl', 'cs.lg']",2112.05587v2.pdf,"  Most existing vision-language pre-training methods focus on understanding
+tasks and use BERT-like objectives (masked language modeling and image-text
+matching) during pretraining. Although they perform well in many understanding
+downstream tasks, e.g., visual question answering, image-text retrieval and
+visual entailment, they do not possess the ability to generate. To tackle this
+problem, we propose Unified multimodal pre-training for both Vision-Language
+understanding and generation (UniVL). The proposed UniVL is capable of handling
+both understanding tasks and generative tasks. We augment existing pretraining
+paradigms that only use random masks with causal masks, i.e., triangular masks
+that mask out future tokens, such that the pre-trained models can have
+autoregressive generation abilities by design. We formulate several previous
+understanding tasks as a text generation task and propose to use prompt-based
+method for fine-tuning on different downstream tasks. Our experiments show that
+there is a trade-off between understanding tasks and generation tasks while
+using the same model, and a feasible way to improve both tasks is to use more
+data. Our UniVL framework attains comparable performance to recent
+vision-language pre-training methods on both understanding tasks and generation
+tasks. Moreover, we demostrate that prompt-based finetuning is more
+data-efficient - it outperforms discriminative methods in few-shot scenarios.
+"
+Learning to Transfer Prompts for Text Generation,Junyi Li,http://arxiv.org/pdf/2205.01543v2.pdf,2022-05-03,['cs.cl'],2205.01543v2.pdf,"  Pretrained language models (PLMs) have made remarkable progress in text
+generation tasks via fine-tuning. While, it is challenging to fine-tune PLMs in
+a data-scarce situation. Therefore, it is non-trivial to develop a general and
+lightweight model that can adapt to various text generation tasks based on
+PLMs. To fulfill this purpose, the recent prompt-based learning offers a
+potential solution. In this paper, we improve this technique and propose a
+novel prompt-based method (PTG) for text generation in a transferable setting.
+First, PTG learns a set of source prompts for various source generation tasks
+and then transfers these prompts as target prompts to perform target generation
+tasks. To consider both task- and instance-level information, we design an
+adaptive attention mechanism to derive the target prompts. For each data
+instance, PTG learns a specific target prompt by attending to highly relevant
+source prompts. In extensive experiments, PTG yields competitive or better
+results than fine-tuning methods. We release our source prompts as an open
+resource, where users can add or reuse them to improve new text generation
+tasks for future research. Code and data can be available at
+https://github.com/RUCAIBox/Transfer-Prompts-for-Text-Generation.
+"
+On the Robustness of Dialogue History Representation in Conversational  Question Answering: A Comprehensive Study and a New Prompt-based Method,Zorik Gekhman,http://arxiv.org/pdf/2206.14796v2.pdf,2022-06-29,"['cs.cl', 'cs.ai', 'cs.lg']",2206.14796v2.pdf,"  Most works on modeling the conversation history in Conversational Question
+Answering (CQA) report a single main result on a common CQA benchmark. While
+existing models show impressive results on CQA leaderboards, it remains unclear
+whether they are robust to shifts in setting (sometimes to more realistic
+ones), training data size (e.g. from large to small sets) and domain. In this
+work, we design and conduct the first large-scale robustness study of history
+modeling approaches for CQA. We find that high benchmark scores do not
+necessarily translate to strong robustness, and that various methods can
+perform extremely differently under different settings. Equipped with the
+insights from our study, we design a novel prompt-based history modeling
+approach, and demonstrate its strong robustness across various settings. Our
+approach is inspired by existing methods that highlight historic answers in the
+passage. However, instead of highlighting by modifying the passage token
+embeddings, we add textual prompts directly in the passage text. Our approach
+is simple, easy-to-plug into practically any model, and highly effective, thus
+we recommend it as a starting point for future model developers. We also hope
+that our study and insights will raise awareness to the importance of
+robustness-focused evaluation, in addition to obtaining high leaderboard
+scores, leading to better CQA systems.
+"
+GPTs at Factify 2022: Prompt Aided Fact-Verification,Pawan Kumar Sahu,http://arxiv.org/pdf/2206.14913v1.pdf,2022-06-29,['cs.cl'],2206.14913v1.pdf,"  One of the most pressing societal issues is the fight against false news. The
+false claims, as difficult as they are to expose, create a lot of damage. To
+tackle the problem, fact verification becomes crucial and thus has been a topic
+of interest among diverse research communities. Using only the textual form of
+data we propose our solution to the problem and achieve competitive results
+with other approaches. We present our solution based on two approaches - PLM
+(pre-trained language model) based method and Prompt based method. The
+PLM-based approach uses the traditional supervised learning, where the model is
+trained to take 'x' as input and output prediction 'y' as P(y|x). Whereas,
+Prompt-based learning reflects the idea to design input to fit the model such
+that the original objective may be re-framed as a problem of (masked) language
+modeling. We may further stimulate the rich knowledge provided by PLMs to
+better serve downstream tasks by employing extra prompts to fine-tune PLMs. Our
+experiments showed that the proposed method performs better than just
+fine-tuning PLMs. We achieved an F1 score of 0.6946 on the FACTIFY dataset and
+a 7th position on the competition leader-board.
+"
+Towards Realistic Low-resource Relation Extraction: A Benchmark with  Empirical Baseline Study,Xin Xu,http://arxiv.org/pdf/2210.10678v3.pdf,2022-10-19,"['cs.cl', 'cs.ai', 'cs.ir', 'cs.lg']",2210.10678v3.pdf,"  This paper presents an empirical study to build relation extraction systems
+in low-resource settings. Based upon recent pre-trained language models, we
+comprehensively investigate three schemes to evaluate the performance in
+low-resource settings: (i) different types of prompt-based methods with
+few-shot labeled data; (ii) diverse balancing methods to address the
+long-tailed distribution issue; (iii) data augmentation technologies and
+self-training to generate more labeled in-domain data. We create a benchmark
+with 8 relation extraction (RE) datasets covering different languages, domains
+and contexts and perform extensive comparisons over the proposed schemes with
+combinations. Our experiments illustrate: (i) Though prompt-based tuning is
+beneficial in low-resource RE, there is still much potential for improvement,
+especially in extracting relations from cross-sentence contexts with multiple
+relational triples; (ii) Balancing methods are not always helpful for RE with
+long-tailed distribution; (iii) Data augmentation complements existing
+baselines and can bring much performance gain, while self-training may not
+consistently achieve advancement to low-resource RE. Code and datasets are in
+https://github.com/zjunlp/LREBench.
+"
+PromptFusion: Decoupling Stability and Plasticity for Continual Learning,Haoran Chen,http://arxiv.org/pdf/2303.07223v1.pdf,2023-03-13,['cs.cv'],2303.07223v1.pdf,"  Continual learning refers to the capability of continuously learning from a
+stream of data. Current research mainly focuses on relieving catastrophic
+forgetting, and most of their success is at the cost of limiting the
+performance of newly incoming tasks. Such a trade-off is referred to as the
+stabilityplasticity dilemma and is a more general and challenging problem for
+continual learning. However, the inherent conflict between these two concepts
+makes it seemingly impossible to devise a satisfactory solution to both of them
+simultaneously. Therefore, we ask, ""is it possible to divide them into two
+problems to conquer independently?"" To this end, we propose a
+prompt-tuning-based method termed PromptFusion to enable the decoupling of
+stability and plasticity. Specifically, PromptFusion consists of a carefully
+designed Stabilizer module that deals with catastrophic forgetting and a
+Booster module to learn new knowledge concurrently. During training,
+PromptFusion first passes an input image to the two modules separately. Then
+the resulting logits are further fused with a learnable weight parameter.
+Finally, a weight mask is applied to the derived logits to balance between old
+and new classes. Extensive experiments show that our method achieves promising
+results on popular continual learning datasets for both class-incremental and
+domain incremental settings. Especially on Split-Imagenet-R, one of the most
+challenging datasets for class-incremental learning, our method exceeds
+state-of-the-art prompt-based methods L2P and DualPrompt by more than 10%.
+"
+Progressive Visual Prompt Learning with Contrastive Feature Re-formation,Chen Xu,http://arxiv.org/pdf/2304.08386v1.pdf,2023-04-17,['cs.cv'],2304.08386v1.pdf,"  Prompt learning has been designed as an alternative to fine-tuning for
+adapting Vision-language (V-L) models to the downstream tasks. Previous works
+mainly focus on text prompt while visual prompt works are limited for V-L
+models. The existing visual prompt methods endure either mediocre performance
+or unstable training process, indicating the difficulty of visual prompt
+learning. In this paper, we propose a new Progressive Visual Prompt (ProVP)
+structure to strengthen the interactions among prompts of different layers.
+More importantly, our ProVP could effectively propagate the image embeddings to
+deep layers and behave partially similar to an instance adaptive prompt method.
+To alleviate generalization deterioration, we further propose a new contrastive
+feature re-formation, which prevents the serious deviation of the prompted
+visual feature from the fixed CLIP visual feature distribution. Combining both,
+our method (ProVP-Ref) is evaluated on 11 image benchmark datasets and achieves
+7/11 state-of-theart results on both few-shot and base-to-novel settings. To
+the best of our knowledge, we are the first to demonstrate the superior
+performance of visual prompts in V-L models to previous prompt-based methods in
+downstream tasks. Meanwhile, it implies that our ProVP-Ref shows the best
+capability to adapt and to generalize.
+"
+SelfEvolve: A Code Evolution Framework via Large Language Models,Shuyang Jiang,http://arxiv.org/pdf/2306.02907v1.pdf,2023-06-05,"['cs.cl', 'cs.se']",2306.02907v1.pdf,"  Large language models (LLMs) have already revolutionized code generation,
+after being pretrained on publicly available code data. However, while various
+methods have been proposed to augment LLMs with retrieved knowledge and enhance
+the quality of code generation, the performance of these retrieval-based
+methods is limited by the strength of the retrievers used. In addition, while
+LLMs show great emergent ability, they still struggle to produce the correct
+code in one turn. To address these challenges, we propose a novel two-step
+pipeline, called \autoknow, that leverages LLMs as both knowledge providers and
+self-reflective programmers. Unlike retrieval-based methods, \autoknow~obtains
+the knowledge from input prompts and generates intermediate code based on the
+generated knowledge. After that, \autoknow~asks LLM to act as an expert
+programmer to perform debugging for the generated code. This is achieved by
+receiving the error message from the interpreter, without requiring special
+test cases for correctness verification. We evaluate \autoknow~on three code
+generation datasets, including DS-1000 for data science code, HumanEval for
+software engineering code, and TransCoder for C++-to-Python translation. Our
+empirical experiments show that \autoknow~outperforms strong baselines by a
+significant margin on all datasets. We also conduct exhaustive analytical
+experiments to validate the effectiveness of the two stages of \autoknow, and
+find that both are superior to other prompting-based methods. Further
+scalability analysis demonstrates that \autoknow~can be adapted to other more
+advanced models, such as GPT-4, and bring consistent efficacy improvement.
+"
+Quantifying Language Models' Sensitivity to Spurious Features in Prompt  Design or: How I learned to start worrying about prompt formatting,Melanie Sclar,http://arxiv.org/pdf/2310.11324v1.pdf,2023-10-17,"['cs.cl', 'cs.ai', 'cs.lg']",2310.11324v1.pdf,"  As large language models (LLMs) are adopted as a fundamental component of
+language technologies, it is crucial to accurately characterize their
+performance. Because choices in prompt design can strongly influence model
+behavior, this design process is critical in effectively using any modern
+pre-trained generative language model. In this work, we focus on LLM
+sensitivity to a quintessential class of meaning-preserving design choices:
+prompt formatting. We find that several widely used open-source LLMs are
+extremely sensitive to subtle changes in prompt formatting in few-shot
+settings, with performance differences of up to 76 accuracy points when
+evaluated using LLaMA-2-13B. Sensitivity remains even when increasing model
+size, the number of few-shot examples, or performing instruction tuning. Our
+analysis suggests that work evaluating LLMs with prompting-based methods would
+benefit from reporting a range of performance across plausible prompt formats,
+instead of the currently-standard practice of reporting performance on a single
+format. We also show that format performance only weakly correlates between
+models, which puts into question the methodological validity of comparing
+models with an arbitrarily chosen, fixed prompt format. To facilitate
+systematic analysis we propose FormatSpread, an algorithm that rapidly
+evaluates a sampled set of plausible prompt formats for a given task, and
+reports the interval of expected performance without accessing model weights.
+Furthermore, we present a suite of analyses that characterize the nature of
+this sensitivity, including exploring the influence of particular atomic
+perturbations and the internal representation of particular formats.
+"
+GPT-3-driven pedagogical agents for training children's curious  question-asking skills,Rania Abdelghani,http://arxiv.org/pdf/2211.14228v6.pdf,2022-11-25,"['cs.cl', 'cs.hc']",2211.14228v6.pdf,"  In order to train children's ability to ask curiosity-driven questions,
+previous research has explored designing specific exercises relying on
+providing semantic and linguistic cues to help formulate such questions. But
+despite showing pedagogical efficiency, this method is still limited as it
+relies on generating the said cues by hand, which can be a very costly process.
+In this context, we propose to leverage advances in the natural language
+processing field (NLP) and investigate the efficiency of using a large language
+model (LLM) for automating the production of the pedagogical content of a
+curious question-asking (QA) training. We study generating the said content
+using the ""prompt-based"" method that consists of explaining the task to the LLM
+in natural text. We evaluate the output using human experts annotations and
+comparisons with hand-generated content. Results suggested indeed the relevance
+and usefulness of this content. We also conduct a field study in primary school
+(75 children aged 9-10), where we evaluate children's QA performance when
+having this training. We compare 3 types of content : 1) hand-generated content
+that proposes ""closed"" cues leading to predefined questions; 2) GPT-3-generated
+content that proposes the same type of cues; 3) GPT-3-generated content that
+proposes ""open"" cues leading to several possible questions. We see a similar QA
+performance between the two ""closed"" trainings (showing the scalability of the
+approach using GPT-3), and a better one for participants with the ""open""
+training. These results suggest the efficiency of using LLMs to support
+children in generating more curious questions, using a natural language
+prompting approach that affords usability by teachers and other users not
+specialists of AI techniques. Furthermore, results also show that open-ended
+content may be more suitable for training curious question-asking skills.
+"
+Towards using Few-Shot Prompt Learning for Automating Model Completion,Meriem Ben Chaaben,http://arxiv.org/pdf/2212.03404v1.pdf,2022-12-07,"['cs.se', 'cs.cl']",2212.03404v1.pdf,"  We propose a simple yet a novel approach to improve completion in domain
+modeling activities. Our approach exploits the power of large language models
+by using few-shot prompt learning without the need to train or fine-tune those
+models with large datasets that are scarce in this field. We implemented our
+approach and tested it on the completion of static and dynamic domain diagrams.
+Our initial evaluation shows that such an approach is effective and can be
+integrated in different ways during the modeling activities.
+"
+Are Prompt-based Models Clueless?,Pride Kavumba,http://arxiv.org/pdf/2205.09295v2.pdf,2022-05-19,['cs.cl'],2205.09295v2.pdf,"  Finetuning large pre-trained language models with a task-specific head has
+advanced the state-of-the-art on many natural language understanding
+benchmarks. However, models with a task-specific head require a lot of training
+data, making them susceptible to learning and exploiting dataset-specific
+superficial cues that do not generalize to other datasets. Prompting has
+reduced the data requirement by reusing the language model head and formatting
+the task input to match the pre-training objective. Therefore, it is expected
+that few-shot prompt-based models do not exploit superficial cues. This paper
+presents an empirical examination of whether few-shot prompt-based models also
+exploit superficial cues. Analyzing few-shot prompt-based models on MNLI, SNLI,
+HANS, and COPA has revealed that prompt-based models also exploit superficial
+cues. While the models perform well on instances with superficial cues, they
+often underperform or only marginally outperform random accuracy on instances
+without superficial cues.
+"
+Decomposed Prompting for Machine Translation Between Related Languages  using Large Language Models,Ratish Puduppully,http://arxiv.org/pdf/2305.13085v2.pdf,2023-05-22,['cs.cl'],2305.13085v2.pdf,"  This study investigates machine translation between related languages i.e.,
+languages within the same family that share linguistic characteristics such as
+word order and lexical similarity. Machine translation through few-shot
+prompting leverages a small set of translation pair examples to generate
+translations for test sentences. This procedure requires the model to learn how
+to generate translations while simultaneously ensuring that token ordering is
+maintained to produce a fluent and accurate translation. We propose that for
+related languages, the task of machine translation can be simplified by
+leveraging the monotonic alignment characteristic of such languages. We
+introduce DecoMT, a novel approach of few-shot prompting that decomposes the
+translation process into a sequence of word chunk translations. Through
+automatic and human evaluation conducted on multiple related language pairs
+across various language families, we demonstrate that our proposed approach of
+decomposed prompting surpasses multiple established few-shot baseline
+approaches. For example, DecoMT outperforms the strong few-shot prompting BLOOM
+model with an average improvement of 8 chrF++ scores across the examined
+languages.
+"
+Multilingual Large Language Models Are Not (Yet) Code-Switchers,Ruochen Zhang,http://arxiv.org/pdf/2305.14235v2.pdf,2023-05-23,"['cs.cl', 'cs.ai']",2305.14235v2.pdf,"  Multilingual Large Language Models (LLMs) have recently shown great
+capabilities in a wide range of tasks, exhibiting state-of-the-art performance
+through zero-shot or few-shot prompting methods. While there have been
+extensive studies on their abilities in monolingual tasks, the investigation of
+their potential in the context of code-switching (CSW), the practice of
+alternating languages within an utterance, remains relatively uncharted. In
+this paper, we provide a comprehensive empirical analysis of various
+multilingual LLMs, benchmarking their performance across four tasks: sentiment
+analysis, machine translation, summarization and word-level language
+identification. Our results indicate that despite multilingual LLMs exhibiting
+promising outcomes in certain tasks using zero or few-shot prompting, they
+still underperform in comparison to fine-tuned models of much smaller scales.
+We argue that current ""multilingualism"" in LLMs does not inherently imply
+proficiency with code-switching texts, calling for future research to bridge
+this discrepancy.
+"
+"Text and Patterns: For Effective Chain of Thought, It Takes Two to Tango",Aman Madaan,http://arxiv.org/pdf/2209.07686v2.pdf,2022-09-16,"['cs.cl', 'cs.ai', 'cs.lg']",2209.07686v2.pdf,"  The past decade has witnessed dramatic gains in natural language processing
+and an unprecedented scaling of large language models. These developments have
+been accelerated by the advent of few-shot techniques such as chain of thought
+(CoT) prompting. Specifically, CoT pushes the performance of large language
+models in a few-shot setup by augmenting the prompts with intermediate steps.
+Despite impressive results across various tasks, the reasons behind their
+success have not been explored. This work uses counterfactual prompting to
+develop a deeper understanding of CoT-based few-shot prompting mechanisms in
+large language models. We first systematically identify and define the key
+components of a prompt: symbols, patterns, and text. Then, we devise and
+conduct an exhaustive set of experiments across four different tasks, by
+querying the model with counterfactual prompts where only one of these
+components is altered. Our experiments across three models (PaLM, GPT-3, and
+CODEX) reveal several surprising findings and brings into question the
+conventional wisdom around few-shot prompting. First, the presence of factual
+patterns in a prompt is practically immaterial to the success of CoT. Second,
+our results conclude that the primary role of intermediate steps may not be to
+facilitate learning how to solve a task. The intermediate steps are rather a
+beacon for the model to realize what symbols to replicate in the output to form
+a factual answer. Further, text imbues patterns with commonsense knowledge and
+meaning. Our empirical and qualitative analysis reveals that a symbiotic
+relationship between text and patterns explains the success of few-shot
+prompting: text helps extract commonsense from the question to help patterns,
+and patterns enforce task understanding and direct text generation.
+"
+Understanding How Model Size Affects Few-shot Instruction Prompting,Ayrton San Joaquin,http://arxiv.org/pdf/2212.01907v1.pdf,2022-12-04,"['cs.cl', 'cs.lg', 'stat.ml']",2212.01907v1.pdf,"  Large Language Models are affected by the phenomena of memorizing and
+forgetting their training data. But how do these vary by model size? We work
+towards this question by investigating how the model size affects the model's
+ability to discriminate a word's meaning in a given context. We introduce a
+dataset called DeltaWords, which evaluates a model's ability to follow
+instructions to select a sentence which replaces the target word with its
+antonym. We show a weak inverse scaling trend, where task accuracy degrades as
+model size increase, under extremely few-shot prompting regimes. We show that
+increasing the number of examples tend to disproportionately benefit larger
+models than smaller models.
+"
+Prompted LLMs as Chatbot Modules for Long Open-domain Conversation,Gibbeum Lee,http://arxiv.org/pdf/2305.04533v1.pdf,2023-05-08,"['cs.cl', 'cs.ai', 'cs.lg']",2305.04533v1.pdf,"  In this paper, we propose MPC (Modular Prompted Chatbot), a new approach for
+creating high-quality conversational agents without the need for fine-tuning.
+Our method utilizes pre-trained large language models (LLMs) as individual
+modules for long-term consistency and flexibility, by using techniques such as
+few-shot prompting, chain-of-thought (CoT), and external memory. Our human
+evaluation results show that MPC is on par with fine-tuned chatbot models in
+open-domain conversations, making it an effective solution for creating
+consistent and engaging chatbots.
+"
+Internet-augmented language models through few-shot prompting for  open-domain question answering,Angeliki Lazaridou,http://arxiv.org/pdf/2203.05115v2.pdf,2022-03-10,"['cs.cl', 'cs.lg']",2203.05115v2.pdf,"  In this work, we aim to capitalize on the unique few-shot capabilities of
+large-scale language models (LSLMs) to overcome some of their challenges with
+respect to grounding to factual and up-to-date information. Motivated by
+semi-parametric language models (LMs), which ground their decisions in external
+retrieved evidence, we use few-shot prompting to learn to condition LMs on
+information returned from the web using Google Search, a broad and constantly
+updated knowledge source. Our approach does not involve fine-tuning or learning
+additional parameters, thus making it applicable to any LM, offering therefore
+a strong baseline. Indeed, we find that LMs conditioned on the web surpass
+performance of closed-book models of similar, or even larger, model sizes in
+open-domain question answering. Finally, we find that increasing the
+inference-time compute of models, achieved via using multiple retrieved
+evidences to generate multiple answers followed by a reranking stage that uses
+scores generated by the same LMs, leads to better performance and alleviates
+lower performance of smaller few-shot LMs. All in all, our findings suggest
+that it might be beneficial to slow down the race towards the biggest model and
+instead shift attention towards finding more effective ways to use models,
+including but not limited to, better prompting or increasing inference-time
+compute.
+"
+Decomposed Prompting: A Modular Approach for Solving Complex Tasks,Tushar Khot,http://arxiv.org/pdf/2210.02406v2.pdf,2022-10-05,['cs.cl'],2210.02406v2.pdf,"  Few-shot prompting is a surprisingly powerful way to use Large Language
+Models (LLMs) to solve various tasks. However, this approach struggles as the
+task complexity increases or when the individual reasoning steps of the task
+themselves are hard to learn, especially when embedded in more complex tasks.
+To address this, we propose Decomposed Prompting, a new approach to solve
+complex tasks by decomposing them (via prompting) into simpler sub-tasks that
+can be delegated to a library of prompting-based LLMs dedicated to these
+sub-tasks. This modular structure allows each prompt to be optimized for its
+specific sub-task, further decomposed if necessary, and even easily replaced
+with more effective prompts, trained models, or symbolic functions if desired.
+We show that the flexibility and modularity of Decomposed Prompting allows it
+to outperform prior work on few-shot prompting using GPT3. On symbolic
+reasoning tasks, we can further decompose sub-tasks that are hard for LLMs into
+even simpler solvable sub-tasks. When the complexity comes from the input
+length, we can recursively decompose the task into the same task but with
+smaller inputs. We also evaluate our approach on textual multi-step reasoning
+tasks: on long-context multi-hop QA task, we can more effectively teach the
+sub-tasks via our separate sub-tasks prompts; and on open-domain multi-hop QA,
+we can incorporate a symbolic information retrieval within our decomposition
+framework, leading to improved performance on both tasks. Datasets, Code and
+Prompts available at https://github.com/allenai/DecomP.
+"
+Language Model Crossover: Variation through Few-Shot Prompting,Elliot Meyerson,http://arxiv.org/pdf/2302.12170v2.pdf,2023-02-23,['cs.ne'],2302.12170v2.pdf,"  This paper pursues the insight that language models naturally enable an
+intelligent variation operator similar in spirit to evolutionary crossover. In
+particular, language models of sufficient scale demonstrate in-context
+learning, i.e. they can learn from associations between a small number of input
+patterns to generate outputs incorporating such associations (also called
+few-shot prompting). This ability can be leveraged to form a simple but
+powerful variation operator, i.e. to prompt a language model with a few
+text-based genotypes (such as code, plain-text sentences, or equations), and to
+parse its corresponding output as those genotypes' offspring. The promise of
+such language model crossover (which is simple to implement and can leverage
+many different open-source language models) is that it enables a simple
+mechanism to evolve semantically-rich text representations (with few
+domain-specific tweaks), and naturally benefits from current progress in
+language models. Experiments in this paper highlight the versatility of
+language-model crossover, through evolving binary bit-strings, sentences,
+equations, text-to-image prompts, and Python code. The conclusion is that
+language model crossover is a promising method for evolving genomes
+representable as text.
+"
+Distilling Step-by-Step! Outperforming Larger Language Models with Less  Training Data and Smaller Model Sizes,Cheng-Yu Hsieh,http://arxiv.org/pdf/2305.02301v2.pdf,2023-05-03,"['cs.cl', 'cs.ai', 'cs.lg']",2305.02301v2.pdf,"  Deploying large language models (LLMs) is challenging because they are memory
+inefficient and compute-intensive for practical applications. In reaction,
+researchers train smaller task-specific models by either finetuning with human
+labels or distilling using LLM-generated labels. However, finetuning and
+distillation require large amounts of training data to achieve comparable
+performance to LLMs. We introduce Distilling step-by-step, a new mechanism that
+(a) trains smaller models that outperform LLMs, and (b) achieves so by
+leveraging less training data needed by finetuning or distillation. Our method
+extracts LLM rationales as additional supervision for training small models
+within a multi-task framework. We present three findings across 4 NLP
+benchmarks: First, compared to both finetuning and distillation, our mechanism
+achieves better performance with much fewer labeled/unlabeled training
+examples. Second, compared to few-shot prompted LLMs, we achieve better
+performance using substantially smaller model sizes. Third, we reduce both the
+model size and the amount of data required to outperform LLMs; our finetuned
+770M T5 model outperforms the few-shot prompted 540B PaLM model using only 80%
+of available data on a benchmark, whereas standard finetuning the same T5 model
+struggles to match even by using 100% of the dataset. We release the code at:
+https://github.com/google-research/distilling-step-by-step .
+"
+Leveraging Training Data in Few-Shot Prompting for Numerical Reasoning,Zhanming Jie,http://arxiv.org/pdf/2305.18170v2.pdf,2023-05-29,['cs.cl'],2305.18170v2.pdf,"  Chain-of-thought (CoT) prompting with large language models has proven
+effective in numerous natural language processing tasks, but designing prompts
+that generalize well to diverse problem types can be challenging, especially in
+the context of math word problem (MWP) solving. Additionally, it is common to
+have a large amount of training data that have a better diversity coverage but
+CoT annotations are not available, which limits the use of supervised learning
+techniques. To address these issues, we investigate two approaches to leverage
+the training data in a few-shot prompting scenario: dynamic program prompting
+and program distillation. Our approach is largely inspired by Gao et al.,
+(2022), where they proposed to replace the CoT with the programs as the
+intermediate reasoning step. Such a prompting strategy allows us to accurately
+verify the answer correctness through program execution in MWP solving. Our
+dynamic program prompting involves annotating the training data by sampling
+correct programs from a large language model, while program distillation
+involves adapting a smaller model to the program-annotated training data. Our
+experiments on three standard MWP datasets demonstrate the effectiveness of
+these approaches, yielding significant improvements over previous baselines for
+prompting and fine-tuning. Our results suggest that leveraging a large amount
+of training data can improve the generalization ability of prompts and boost
+the performance of fine-tuned small models in MWP solving.
+"
+Zero- and Few-Shot Prompting with LLMs: A Comparative Study with  Fine-tuned Models for Bangla Sentiment Analysis,Md. Arid Hasan,http://arxiv.org/pdf/2308.10783v1.pdf,2023-08-21,"['cs.cl', 'cs.lg', '68t50', 'i.2.7']",2308.10783v1.pdf,"  The rapid expansion of the digital world has propelled sentiment analysis
+into a critical tool across diverse sectors such as marketing, politics,
+customer service, and healthcare. While there have been significant
+advancements in sentiment analysis for widely spoken languages, low-resource
+languages, such as Bangla, remain largely under-researched due to resource
+constraints. Furthermore, the recent unprecedented performance of Large
+Language Models (LLMs) in various applications highlights the need to evaluate
+them in the context of low-resource languages. In this study, we present a
+sizeable manually annotated dataset encompassing 33,605 Bangla news tweets and
+Facebook comments. We also investigate zero- and few-shot in-context learning
+with several language models, including Flan-T5, GPT-4, and Bloomz, offering a
+comparative analysis against fine-tuned models. Our findings suggest that
+monolingual transformer-based models consistently outperform other models, even
+in zero and few-shot scenarios. To foster continued exploration, we intend to
+make this dataset and our research tools publicly available to the broader
+research community. In the spirit of further research, we plan to make this
+dataset and our experimental resources publicly accessible to the wider
+research community.
+"
+FOLIO: Natural Language Reasoning with First-Order Logic,Simeng Han,http://arxiv.org/pdf/2209.00840v1.pdf,2022-09-02,['cs.cl'],2209.00840v1.pdf,"  We present FOLIO, a human-annotated, open-domain, and logically complex and
+diverse dataset for reasoning in natural language (NL), equipped with first
+order logic (FOL) annotations. FOLIO consists of 1,435 examples (unique
+conclusions), each paired with one of 487 sets of premises which serve as rules
+to be used to deductively reason for the validity of each conclusion. The
+logical correctness of premises and conclusions is ensured by their parallel
+FOL annotations, which are automatically verified by our FOL inference engine.
+In addition to the main NL reasoning task, NL-FOL pairs in FOLIO automatically
+constitute a new NL-FOL translation dataset using FOL as the logical form. Our
+experiments on FOLIO systematically evaluate the FOL reasoning ability of
+supervised fine-tuning on medium-sized language models (BERT, RoBERTa) and
+few-shot prompting on large language models (GPT-NeoX, OPT, GPT-3, Codex). For
+NL-FOL translation, we experiment with GPT-3 and Codex. Our results show that
+one of the most capable Large Language Model (LLM) publicly available, GPT-3
+davinci, achieves only slightly better than random results with few-shot
+prompting on a subset of FOLIO, and the model is especially bad at predicting
+the correct truth values for False and Unknown conclusions. Our dataset and
+code are available at https://github.com/Yale-LILY/FOLIO.
+"
+Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them,Mirac Suzgun,http://arxiv.org/pdf/2210.09261v1.pdf,2022-10-17,"['cs.cl', 'cs.ai']",2210.09261v1.pdf,"  BIG-Bench (Srivastava et al., 2022) is a diverse evaluation suite that
+focuses on tasks believed to be beyond the capabilities of current language
+models. Language models have already made good progress on this benchmark, with
+the best model in the BIG-Bench paper outperforming average reported
+human-rater results on 65% of the BIG-Bench tasks via few-shot prompting. But
+on what tasks do language models fall short of average human-rater performance,
+and are those tasks actually unsolvable by current language models?
+  In this work, we focus on a suite of 23 challenging BIG-Bench tasks which we
+call BIG-Bench Hard (BBH). These are the task for which prior language model
+evaluations did not outperform the average human-rater. We find that applying
+chain-of-thought (CoT) prompting to BBH tasks enables PaLM to surpass the
+average human-rater performance on 10 of the 23 tasks, and Codex
+(code-davinci-002) to surpass the average human-rater performance on 17 of the
+23 tasks. Since many tasks in BBH require multi-step reasoning, few-shot
+prompting without CoT, as done in the BIG-Bench evaluations (Srivastava et al.,
+2022), substantially underestimates the best performance and capabilities of
+language models, which is better captured via CoT prompting. As further
+analysis, we explore the interaction between CoT and model scale on BBH,
+finding that CoT enables emergent task performance on several BBH tasks with
+otherwise flat scaling curves.
+"
+Mental-LLM: Leveraging Large Language Models for Mental Health  Prediction via Online Text Data,Xuhai Xu,http://arxiv.org/pdf/2307.14385v3.pdf,2023-07-26,"['cs.cl', '68u35', 'h.5.2; i.2.m']",2307.14385v3.pdf,"  Advances in large language models (LLMs) have empowered a variety of
+applications. However, there is still a significant gap in research when it
+comes to understanding and enhancing the capabilities of LLMs in the field of
+mental health. In this work, we present the first comprehensive evaluation of
+multiple LLMs, including Alpaca, Alpaca-LoRA, FLAN-T5, GPT-3.5, and GPT-4, on
+various mental health prediction tasks via online text data. We conduct a broad
+range of experiments, covering zero-shot prompting, few-shot prompting, and
+instruction fine-tuning. The results indicate a promising yet limited
+performance of LLMs with zero-shot and few-shot prompt designs for the mental
+health tasks. More importantly, our experiments show that instruction
+finetuning can significantly boost the performance of LLMs for all tasks
+simultaneously. Our best-finetuned models, Mental-Alpaca and Mental-FLAN-T5,
+outperform the best prompt design of GPT-3.5 (25 and 15 times bigger) by 10.9%
+on balanced accuracy and the best of GPT-4 (250 and 150 times bigger) by 4.8%.
+They further perform on par with the state-of-the-art task-specific language
+model. We also conduct an exploratory case study on LLMs' capability on the
+mental health reasoning tasks, illustrating the promising capability of certain
+models such as GPT-4. We summarize our findings into a set of action guidelines
+for potential methods to enhance LLMs' capability for mental health tasks.
+Meanwhile, we also emphasize the important limitations before achieving
+deployability in real-world mental health settings, such as known racial and
+gender bias. We highlight the important ethical risks accompanying this line of
+research.
+"
+Prompt Programming for Large Language Models: Beyond the Few-Shot  Paradigm,Laria Reynolds,http://arxiv.org/pdf/2102.07350v1.pdf,2021-02-15,"['cs.cl', 'cs.ai']",2102.07350v1.pdf,"  Prevailing methods for mapping large generative language models to supervised
+tasks may fail to sufficiently probe models' novel capabilities. Using GPT-3 as
+a case study, we show that 0-shot prompts can significantly outperform few-shot
+prompts. We suggest that the function of few-shot examples in these cases is
+better described as locating an already learned task rather than meta-learning.
+This analysis motivates rethinking the role of prompts in controlling and
+evaluating powerful language models. In this work, we discuss methods of prompt
+programming, emphasizing the usefulness of considering prompts through the lens
+of natural language. We explore techniques for exploiting the capacity of
+narratives and cultural anchors to encode nuanced intentions and techniques for
+encouraging deconstruction of a problem into components before producing a
+verdict. Informed by this more encompassing theory of prompt programming, we
+also introduce the idea of a metaprompt that seeds the model to generate its
+own natural language prompts for a range of tasks. Finally, we discuss how
+these more general methods of interacting with language models can be
+incorporated into existing and future benchmarks and practical applications.
+"
+Fantastically Ordered Prompts and Where to Find Them: Overcoming  Few-Shot Prompt Order Sensitivity,Yao Lu,http://arxiv.org/pdf/2104.08786v2.pdf,2021-04-18,"['cs.cl', 'cs.ai']",2104.08786v2.pdf,"  When primed with only a handful of training samples, very large, pretrained
+language models such as GPT-3 have shown competitive results when compared to
+fully-supervised, fine-tuned, large, pretrained language models. We demonstrate
+that the order in which the samples are provided can make the difference
+between near state-of-the-art and random guess performance: essentially some
+permutations are ""fantastic"" and some not. We analyse this phenomenon in
+detail, establishing that: it is present across model sizes (even for the
+largest current models), it is not related to a specific subset of samples, and
+that a given good permutation for one model is not transferable to another.
+While one could use a development set to determine which permutations are
+performant, this would deviate from the true few-shot setting as it requires
+additional annotated data. Instead, we use the generative nature of language
+models to construct an artificial development set and based on entropy
+statistics of the candidate permutations on this set, we identify performant
+prompts. Our method yields a 13% relative improvement for GPT-family models
+across eleven different established text classification tasks.
+"
+Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning,Prasetya Ajie Utama,http://arxiv.org/pdf/2109.04144v1.pdf,2021-09-09,"['cs.cl', 'cs.ai']",2109.04144v1.pdf,"  Recent prompt-based approaches allow pretrained language models to achieve
+strong performances on few-shot finetuning by reformulating downstream tasks as
+a language modeling problem. In this work, we demonstrate that, despite its
+advantages on low data regimes, finetuned prompt-based models for sentence pair
+classification tasks still suffer from a common pitfall of adopting inference
+heuristics based on lexical overlap, e.g., models incorrectly assuming a
+sentence pair is of the same meaning because they consist of the same set of
+words. Interestingly, we find that this particular inference heuristic is
+significantly less present in the zero-shot evaluation of the prompt-based
+model, indicating how finetuning can be destructive to useful knowledge learned
+during the pretraining. We then show that adding a regularization that
+preserves pretraining weights is effective in mitigating this destructive
+tendency of few-shot finetuning. Our evaluation on three datasets demonstrates
+promising improvements on the three corresponding challenge datasets used to
+diagnose the inference heuristics.
+"
+Towards Zero-Label Language Learning,Zirui Wang,http://arxiv.org/pdf/2109.09193v1.pdf,2021-09-19,"['cs.cl', 'cs.lg']",2109.09193v1.pdf,"  This paper explores zero-label learning in Natural Language Processing (NLP),
+whereby no human-annotated data is used anywhere during training and models are
+trained purely on synthetic data. At the core of our framework is a novel
+approach for better leveraging the powerful pretrained language models.
+Specifically, inspired by the recent success of few-shot inference on GPT-3, we
+present a training data creation procedure named Unsupervised Data Generation
+(UDG), which leverages few-shot prompts to synthesize high-quality training
+data without real human annotations. Our method enables zero-label learning as
+we train task-specific models solely on the synthetic data, yet we achieve
+better or comparable results from strong baseline models trained on
+human-labeled data. Furthermore, when mixed with labeled data, our approach
+serves as a highly effective data augmentation procedure, achieving new
+state-of-the-art results on the SuperGLUE benchmark.
+"
+P4E: Few-Shot Event Detection as Prompt-Guided Identification and  Localization,Sha Li,http://arxiv.org/pdf/2202.07615v3.pdf,2022-02-15,['cs.cl'],2202.07615v3.pdf,"  We propose P4E, an identify-and-localize event detection framework that
+integrates the best of few-shot prompting and structured prediction. Our
+framework decomposes event detection into an identification task and a
+localization task. For the identification task, which we formulate as
+multi-label classification, we leverage cloze-based prompting to align our
+objective with the pre-training task of language models, allowing our model to
+quickly adapt to new event types. We then employ an event type-agnostic
+sequence labeling model to localize the event trigger conditioned on the
+identification output. This heterogeneous model design allows P4E to quickly
+learn new event types without sacrificing the ability to make structured
+predictions. Our experiments demonstrate the effectiveness of our proposed
+design, and P4E shows superior performance for few-shot event detection on
+benchmark datasets FewEvent and MAVEN and comparable performance to SOTA for
+fully-supervised event detection on ACE.
+"
+Prompt-and-Rerank: A Method for Zero-Shot and Few-Shot Arbitrary Textual  Style Transfer with Small Language Models,Mirac Suzgun,http://arxiv.org/pdf/2205.11503v1.pdf,2022-05-23,['cs.cl'],2205.11503v1.pdf,"  We propose a method for arbitrary textual style transfer (TST)--the task of
+transforming a text into any given style--utilizing general-purpose pre-trained
+language models. Our method, Prompt-and-Rerank, is based on a mathematical
+formulation of the TST task, decomposing it into three constituent components:
+textual similarity, target style strength, and fluency. Specifically, our
+method first uses zero-shot or few-shot prompting to obtain a set of candidate
+generations in the target style, and then re-ranks these candidates according
+to a combination of the three components above. Empirically, our method enables
+small pre-trained language models to perform on par with state-of-the-art
+large-scale models while consuming two orders of magnitude less compute and
+memory. Finally, we conduct a systematic investigation of the effect of model
+size and prompt design (e.g., prompt paraphrasing and delimiter-pair choice) on
+style transfer quality across seven diverse textual style transfer datasets.
+"
+Bootstrapping Multilingual Semantic Parsers using Large Language Models,Abhijeet Awasthi,http://arxiv.org/pdf/2210.07313v2.pdf,2022-10-13,"['cs.cl', 'cs.lg']",2210.07313v2.pdf,"  Despite cross-lingual generalization demonstrated by pre-trained multilingual
+models, the translate-train paradigm of transferring English datasets across
+multiple languages remains to be a key mechanism for training task-specific
+multilingual models. However, for many low-resource languages, the availability
+of a reliable translation service entails significant amounts of costly
+human-annotated translation pairs. Further, translation services may continue
+to be brittle due to domain mismatch between task-specific input text and
+general-purpose text used for training translation models. For multilingual
+semantic parsing, we demonstrate the effectiveness and flexibility offered by
+large language models (LLMs) for translating English datasets into several
+languages via few-shot prompting. Through extensive comparisons on two public
+datasets, MTOP and MASSIVE, spanning 50 languages and several domains, we show
+that our method of translating data using LLMs outperforms a strong
+translate-train baseline on 41 out of 50 languages. We study the key design
+choices that enable more effective multilingual data translation via prompted
+LLMs.
+"
+Prompting GPT-3 To Be Reliable,Chenglei Si,http://arxiv.org/pdf/2210.09150v2.pdf,2022-10-17,['cs.cl'],2210.09150v2.pdf,"  Large language models (LLMs) show impressive abilities via few-shot
+prompting. Commercialized APIs such as OpenAI GPT-3 further increase their use
+in real-world language applications. However, the crucial problem of how to
+improve the reliability of GPT-3 is still under-explored. While reliability is
+a broad and vaguely defined term, we decompose reliability into four main
+facets that correspond to the existing framework of ML safety and are
+well-recognized to be important: generalizability, social biases, calibration,
+and factuality. Our core contribution is to establish simple and effective
+prompts that improve GPT-3's reliability as it: 1) generalizes
+out-of-distribution, 2) balances demographic distribution and uses natural
+language instructions to reduce social biases, 3) calibrates output
+probabilities, and 4) updates the LLM's factual knowledge and reasoning chains.
+With appropriate prompts, GPT-3 is more reliable than smaller-scale supervised
+models on all these facets. We release all processed datasets, evaluation
+scripts, and model predictions. Our systematic empirical study not only sheds
+new insights on the reliability of prompting LLMs, but more importantly, our
+prompting strategies can help practitioners more reliably use LLMs like GPT-3.
+"
+Exploring The Landscape of Distributional Robustness for Question  Answering Models,Anas Awadalla,http://arxiv.org/pdf/2210.12517v1.pdf,2022-10-22,"['cs.cl', 'cs.lg']",2210.12517v1.pdf,"  We conduct a large empirical evaluation to investigate the landscape of
+distributional robustness in question answering. Our investigation spans over
+350 models and 16 question answering datasets, including a diverse set of
+architectures, model sizes, and adaptation methods (e.g., fine-tuning, adapter
+tuning, in-context learning, etc.). We find that, in many cases, model
+variations do not affect robustness and in-distribution performance alone
+determines out-of-distribution performance. Moreover, our findings indicate
+that i) zero-shot and in-context learning methods are more robust to
+distribution shifts than fully fine-tuned models; ii) few-shot prompt
+fine-tuned models exhibit better robustness than few-shot fine-tuned span
+prediction models; iii) parameter-efficient and robustness enhancing training
+methods provide no significant robustness improvements. In addition, we
+publicly release all evaluations to encourage researchers to further analyze
+robustness trends for question answering models.
+"
+"""Covid vaccine is against Covid but Oxford vaccine is made at Oxford!""  Semantic Interpretation of Proper Noun Compounds",Keshav Kolluru,http://arxiv.org/pdf/2210.13039v1.pdf,2022-10-24,['cs.cl'],2210.13039v1.pdf,"  Proper noun compounds, e.g., ""Covid vaccine"", convey information in a
+succinct manner (a ""Covid vaccine"" is a ""vaccine that immunizes against the
+Covid disease""). These are commonly used in short-form domains, such as news
+headlines, but are largely ignored in information-seeking applications. To
+address this limitation, we release a new manually annotated dataset, ProNCI,
+consisting of 22.5K proper noun compounds along with their free-form semantic
+interpretations. ProNCI is 60 times larger than prior noun compound datasets
+and also includes non-compositional examples, which have not been previously
+explored. We experiment with various neural models for automatically generating
+the semantic interpretations from proper noun compounds, ranging from few-shot
+prompting to supervised learning, with varying degrees of knowledge about the
+constituent nouns. We find that adding targeted knowledge, particularly about
+the common noun, results in performance gains of upto 2.8%. Finally, we
+integrate our model generated interpretations with an existing Open IE system
+and observe an 7.5% increase in yield at a precision of 85%. The dataset and
+code are available at https://github.com/dair-iitd/pronci.
+"
+Prompting PaLM for Translation: Assessing Strategies and Performance,David Vilar,http://arxiv.org/pdf/2211.09102v3.pdf,2022-11-16,['cs.cl'],2211.09102v3.pdf,"  Large language models (LLMs) that have been trained on multilingual but not
+parallel text exhibit a remarkable ability to translate between languages. We
+probe this ability in an in-depth study of the pathways language model (PaLM),
+which has demonstrated the strongest machine translation (MT) performance among
+similarly-trained LLMs to date. We investigate various strategies for choosing
+translation examples for few-shot prompting, concluding that example quality is
+the most important factor. Using optimized prompts, we revisit previous
+assessments of PaLM's MT capabilities with more recent test sets, modern MT
+metrics, and human evaluation, and find that its performance, while impressive,
+still lags that of state-of-the-art supervised systems. We conclude by
+providing an analysis of PaLM's MT output which reveals some interesting
+properties and prospects for future work.
+"
+PartSLIP: Low-Shot Part Segmentation for 3D Point Clouds via Pretrained  Image-Language Models,Minghua Liu,http://arxiv.org/pdf/2212.01558v2.pdf,2022-12-03,"['cs.cv', 'cs.ro']",2212.01558v2.pdf,"  Generalizable 3D part segmentation is important but challenging in vision and
+robotics. Training deep models via conventional supervised methods requires
+large-scale 3D datasets with fine-grained part annotations, which are costly to
+collect. This paper explores an alternative way for low-shot part segmentation
+of 3D point clouds by leveraging a pretrained image-language model, GLIP, which
+achieves superior performance on open-vocabulary 2D detection. We transfer the
+rich knowledge from 2D to 3D through GLIP-based part detection on point cloud
+rendering and a novel 2D-to-3D label lifting algorithm. We also utilize
+multi-view 3D priors and few-shot prompt tuning to boost performance
+significantly. Extensive evaluation on PartNet and PartNet-Mobility datasets
+shows that our method enables excellent zero-shot 3D part segmentation. Our
+few-shot version not only outperforms existing few-shot approaches by a large
+margin but also achieves highly competitive results compared to the fully
+supervised counterpart. Furthermore, we demonstrate that our method can be
+directly applied to iPhone-scanned point clouds without significant domain
+gaps.
+"
+Natural Language to Code Generation in Interactive Data Science  Notebooks,Pengcheng Yin,http://arxiv.org/pdf/2212.09248v1.pdf,2022-12-19,"['cs.cl', 'cs.se']",2212.09248v1.pdf,"  Computational notebooks, such as Jupyter notebooks, are interactive computing
+environments that are ubiquitous among data scientists to perform data
+wrangling and analytic tasks. To measure the performance of AI pair programmers
+that automatically synthesize programs for those tasks given natural language
+(NL) intents from users, we build ARCADE, a benchmark of 1082 code generation
+problems using the pandas data analysis framework in data science notebooks.
+ARCADE features multiple rounds of NL-to-code problems from the same notebook.
+It requires a model to understand rich multi-modal contexts, such as existing
+notebook cells and their execution states as well as previous turns of
+interaction. To establish a strong baseline on this challenging task, we
+develop PaChiNCo, a 62B code language model (LM) for Python computational
+notebooks, which significantly outperforms public code LMs. Finally, we explore
+few-shot prompting strategies to elicit better code with step-by-step
+decomposition and NL explanation, showing the potential to improve the
+diversity and explainability of model predictions.
+"
+LAMBADA: Backward Chaining for Automated Reasoning in Natural Language,Mehran Kazemi,http://arxiv.org/pdf/2212.13894v2.pdf,2022-12-20,"['cs.ai', 'cs.lg']",2212.13894v2.pdf,"  Remarkable progress has been made on automated reasoning with natural text,
+by using Language Models (LMs) and methods such as Chain-of-Thought and
+Selection-Inference. These techniques search for proofs in the forward
+direction from axioms to the conclusion, which suffers from a combinatorial
+explosion of the search space, and thus high failure rates for problems
+requiring longer chains of reasoning. The classical automated reasoning
+literature has shown that reasoning in the backward direction (i.e. from the
+intended conclusion to supporting axioms) is significantly more efficient at
+proof-finding. Importing this intuition into the LM setting, we develop a
+Backward Chaining algorithm, called LAMBADA, that decomposes reasoning into
+four sub-modules. These sub-modules are simply implemented by few-shot prompted
+LM inference. We show that LAMBADA achieves sizable accuracy boosts over
+state-of-the-art forward reasoning methods on challenging logical reasoning
+datasets, particularly when deep and accurate proof chains are required.
+"
+Can GPT-3 Perform Statutory Reasoning?,Andrew Blair-Stanek,http://arxiv.org/pdf/2302.06100v2.pdf,2023-02-13,"['cs.cl', 'cs.ai']",2302.06100v2.pdf,"  Statutory reasoning is the task of reasoning with facts and statutes, which
+are rules written in natural language by a legislature. It is a basic legal
+skill. In this paper we explore the capabilities of the most capable GPT-3
+model, text-davinci-003, on an established statutory-reasoning dataset called
+SARA. We consider a variety of approaches, including dynamic few-shot
+prompting, chain-of-thought prompting, and zero-shot prompting. While we
+achieve results with GPT-3 that are better than the previous best published
+results, we also identify several types of clear errors it makes. We
+investigate why these errors happen. We discover that GPT-3 has imperfect prior
+knowledge of the actual U.S. statutes on which SARA is based. More importantly,
+we create simple synthetic statutes, which GPT-3 is guaranteed not to have seen
+during training. We find GPT-3 performs poorly at answering straightforward
+questions about these simple synthetic statutes.
+"
+STREET: A Multi-Task Structured Reasoning and Explanation Benchmark,Danilo Ribeiro,http://arxiv.org/pdf/2302.06729v1.pdf,2023-02-13,"['cs.cl', 'cs.ai', 'i.2.7; i.2.6']",2302.06729v1.pdf,"  We introduce STREET, a unified multi-task and multi-domain natural language
+reasoning and explanation benchmark. Unlike most existing question-answering
+(QA) datasets, we expect models to not only answer questions, but also produce
+step-by-step structured explanations describing how premises in the question
+are used to produce intermediate conclusions that can prove the correctness of
+a certain answer. We perform extensive evaluation with popular language models
+such as few-shot prompting GPT-3 and fine-tuned T5. We find that these models
+still lag behind human performance when producing such structured reasoning
+steps. We believe this work will provide a way for the community to better
+train and test systems on multi-step reasoning and explanations in natural
+language.
+"
+ADELT: Transpilation Between Deep Learning Frameworks,Linyuan Gong,http://arxiv.org/pdf/2303.03593v1.pdf,2023-03-07,"['cs.cl', 'cs.lg']",2303.03593v1.pdf,"  We propose Adversarial DEep Learning Transpiler (ADELT) for source-to-source
+transpilation between deep learning frameworks. Unlike prior approaches, we
+decouple the transpilation of code skeletons and the mapping of API keywords
+(an API function name or a parameter name). ADELT transpile code skeletons
+using few-shot prompting on big language models. Based on contextual embeddings
+extracted by a BERT for code, we train aligned API embeddings in a
+domain-adversarial setup, upon which we generate a dictionary for keyword
+translation. The model is trained on our unlabeled DL corpus from web crawl
+data, without using any hand-crafted rules and parallel data. Our method
+outperforms state-of-the-art transpilers on multiple transpilation pairs
+including PyTorch-Keras and PyTorch-MXNet by 15.9pts and 12.0pts in exact match
+scores respectively.
+"
+Query2doc: Query Expansion with Large Language Models,Liang Wang,http://arxiv.org/pdf/2303.07678v2.pdf,2023-03-14,"['cs.ir', 'cs.cl']",2303.07678v2.pdf,"  This paper introduces a simple yet effective query expansion approach,
+denoted as query2doc, to improve both sparse and dense retrieval systems. The
+proposed method first generates pseudo-documents by few-shot prompting large
+language models (LLMs), and then expands the query with generated
+pseudo-documents. LLMs are trained on web-scale text corpora and are adept at
+knowledge memorization. The pseudo-documents from LLMs often contain highly
+relevant information that can aid in query disambiguation and guide the
+retrievers. Experimental results demonstrate that query2doc boosts the
+performance of BM25 by 3% to 15% on ad-hoc IR datasets, such as MS-MARCO and
+TREC DL, without any model fine-tuning. Furthermore, our method also benefits
+state-of-the-art dense retrievers in terms of both in-domain and out-of-domain
+results.
+"
+How to Design Translation Prompts for ChatGPT: An Empirical Study,Yuan Gao,http://arxiv.org/pdf/2304.02182v2.pdf,2023-04-05,['cs.cl'],2304.02182v2.pdf,"  The recently released ChatGPT has demonstrated surprising abilities in
+natural language understanding and natural language generation. Machine
+translation relies heavily on the abilities of language understanding and
+generation. Thus, in this paper, we explore how to assist machine translation
+with ChatGPT. We adopt several translation prompts on a wide range of
+translations. Our experimental results show that ChatGPT with designed
+translation prompts can achieve comparable or better performance over
+commercial translation systems for high-resource language translations. We
+further evaluate the translation quality using multiple references, and ChatGPT
+achieves superior performance compared to commercial systems. We also conduct
+experiments on domain-specific translations, the final results show that
+ChatGPT is able to comprehend the provided domain keyword and adjust
+accordingly to output proper translations. At last, we perform few-shot prompts
+that show consistent improvement across different base prompts. Our work
+provides empirical evidence that ChatGPT still has great potential in
+translations.
+"
+Boosted Prompt Ensembles for Large Language Models,Silviu Pitis,http://arxiv.org/pdf/2304.05970v1.pdf,2023-04-12,"['cs.cl', 'cs.lg']",2304.05970v1.pdf,"  Methods such as chain-of-thought prompting and self-consistency have pushed
+the frontier of language model reasoning performance with no additional
+training. To further improve performance, we propose a prompt ensembling method
+for large language models, which uses a small dataset to construct a set of few
+shot prompts that together comprise a ``boosted prompt ensemble''. The few shot
+examples for each prompt are chosen in a stepwise fashion to be ``hard''
+examples on which the previous step's ensemble is uncertain. We show that this
+outperforms single-prompt output-space ensembles and bagged prompt-space
+ensembles on the GSM8k and AQuA datasets, among others. We propose both
+train-time and test-time versions of boosted prompting that use different
+levels of available annotation and conduct a detailed empirical study of our
+algorithm.
+"
+Multi-Party Chat: Conversational Agents in Group Settings with Humans  and Models,Jimmy Wei,http://arxiv.org/pdf/2304.13835v3.pdf,2023-04-26,"['cs.cl', 'cs.lg']",2304.13835v3.pdf,"  Current dialogue research primarily studies pairwise (two-party)
+conversations, and does not address the everyday setting where more than two
+speakers converse together. In this work, we both collect and evaluate
+multi-party conversations to study this more general case. We use the LIGHT
+environment to construct grounded conversations, where each participant has an
+assigned character to role-play. We thus evaluate the ability of language
+models to act as one or more characters in such conversations. Models require
+two skills that pairwise-trained models appear to lack: (1) being able to
+decide when to talk; (2) producing coherent utterances grounded on multiple
+characters. We compare models trained on our new dataset to existing
+pairwise-trained dialogue models, as well as large language models with
+few-shot prompting. We find that our new dataset, MultiLIGHT, which we will
+publicly release, can help bring significant improvements in the group setting.
+"
+Transferring Procedural Knowledge across Commonsense Tasks,Yifan Jiang,http://arxiv.org/pdf/2304.13867v2.pdf,2023-04-26,['cs.cl'],2304.13867v2.pdf,"  Stories about everyday situations are an essential part of human
+communication, motivating the need to develop AI agents that can reliably
+understand these stories. Despite the long list of supervised methods for story
+completion and procedural understanding, current AI has no mechanisms to
+automatically track and explain procedures in unseen stories. To bridge this
+gap, we study the ability of AI models to transfer procedural knowledge to
+novel narrative tasks in a transparent manner. We design LEAP: a comprehensive
+framework that integrates state-of-the-art modeling architectures, training
+regimes, and augmentation strategies based on both natural and synthetic
+stories. To address the lack of densely annotated training data, we devise a
+robust automatic labeler based on few-shot prompting to enhance the augmented
+data. Our experiments with in- and out-of-domain tasks reveal insights into the
+interplay of different architectures, training regimes, and augmentation
+strategies. LEAP's labeler has a clear positive impact on out-of-domain
+datasets, while the resulting dense annotation provides native explainability.
+"
+Explainable Verbal Reasoner Plus (EVR+): A Natural Language Reasoning  Framework that Supports Diverse Compositional Reasoning,Zhengzhong Liang,http://arxiv.org/pdf/2305.00061v1.pdf,2023-04-28,"['cs.cl', 'cs.ai']",2305.00061v1.pdf,"  Languages models have been successfully applied to a variety of reasoning
+tasks in NLP, yet the language models still suffer from compositional
+generalization. In this paper we present Explainable Verbal Reasoner Plus
+(EVR+), a reasoning framework that enhances language models' compositional
+reasoning ability by (1) allowing the model to explicitly generate and execute
+symbolic operators, and (2) allowing the model to decompose a complex task into
+several simpler ones in a flexible manner. Compared with its predecessor
+Explainable Verbal Reasoner (EVR) and other previous approaches adopting
+similar ideas, our framework supports more diverse types of reasoning such as
+nested loops and different types of recursion. To evaluate our reasoning
+framework, we build a synthetic dataset with five tasks that require
+compositional reasoning. Results show that our reasoning framework can enhance
+the language model's compositional generalization performance on the five
+tasks, using a fine-tuned language model. We also discussed the possibility and
+the challenges to combine our reasoning framework with a few-shot prompted
+language model.
+"
+Revisiting Relation Extraction in the era of Large Language Models,Somin Wadhwa,http://arxiv.org/pdf/2305.05003v1.pdf,2023-05-08,['cs.cl'],2305.05003v1.pdf,"  Relation extraction (RE) is the core NLP task of inferring semantic
+relationships between entities from text. Standard supervised RE techniques
+entail training modules to tag tokens comprising entity spans and then predict
+the relationship between them. Recent work has instead treated the problem as a
+\emph{sequence-to-sequence} task, linearizing relations between entities as
+target strings to be generated conditioned on the input. Here we push the
+limits of this approach, using larger language models (GPT-3 and Flan-T5 large)
+than considered in prior work and evaluating their performance on standard RE
+tasks under varying levels of supervision. We address issues inherent to
+evaluating generative approaches to RE by doing human evaluations, in lieu of
+relying on exact matching. Under this refined evaluation, we find that: (1)
+Few-shot prompting with GPT-3 achieves near SOTA performance, i.e., roughly
+equivalent to existing fully supervised models; (2) Flan-T5 is not as capable
+in the few-shot setting, but supervising and fine-tuning it with
+Chain-of-Thought (CoT) style explanations (generated via GPT-3) yields SOTA
+results. We release this model as a new baseline for RE tasks.
+"
+Generating medically-accurate summaries of patient-provider dialogue: A  multi-stage approach using large language models,Varun Nair,http://arxiv.org/pdf/2305.05982v1.pdf,2023-05-10,"['cs.cl', 'cs.ai', 'cs.lg']",2305.05982v1.pdf,"  A medical provider's summary of a patient visit serves several critical
+purposes, including clinical decision-making, facilitating hand-offs between
+providers, and as a reference for the patient. An effective summary is required
+to be coherent and accurately capture all the medically relevant information in
+the dialogue, despite the complexity of patient-generated language. Even minor
+inaccuracies in visit summaries (for example, summarizing ""patient does not
+have a fever"" when a fever is present) can be detrimental to the outcome of
+care for the patient.
+  This paper tackles the problem of medical conversation summarization by
+discretizing the task into several smaller dialogue-understanding tasks that
+are sequentially built upon. First, we identify medical entities and their
+affirmations within the conversation to serve as building blocks. We study
+dynamically constructing few-shot prompts for tasks by conditioning on relevant
+patient information and use GPT-3 as the backbone for our experiments. We also
+develop GPT-derived summarization metrics to measure performance against
+reference summaries quantitatively. Both our human evaluation study and metrics
+for medical correctness show that summaries generated using this approach are
+clinically accurate and outperform the baseline approach of summarizing the
+dialog in a zero-shot, single-prompt setting.
+"
+ZARA: Improving Few-Shot Self-Rationalization for Small Language Models,Wei-Lin Chen,http://arxiv.org/pdf/2305.07355v2.pdf,2023-05-12,['cs.cl'],2305.07355v2.pdf,"  Language models (LMs) that jointly generate end-task answers as well as
+free-text rationales are known as self-rationalization models. Recent works
+demonstrate great performance gain for self-rationalization by few-shot
+prompting LMs with rationale-augmented exemplars. However, the ability to
+benefit from explanations only emerges with large-scale LMs, which have poor
+accessibility. In this work, we explore the less-studied setting of leveraging
+explanations for small LMs to improve few-shot self-rationalization. We first
+revisit the relationship between rationales and answers. Inspired by the
+implicit mental process of how human beings assess explanations, we present a
+novel approach, Zero-shot Augmentation of Rationale-Answer pairs (ZARA), to
+automatically construct pseudo-parallel data for self-training by reducing the
+problem of plausibility judgement to natural language inference. Experimental
+results show ZARA achieves SOTA performance on the FEB benchmark, for both the
+task accuracy and the explanation metric. In addition, we conduct human and
+quantitative evaluation validating ZARA's ability to automatically identify
+plausible and accurate rationale-answer pairs.
+"
+Natural Language Decomposition and Interpretation of Complex Utterances,Harsh Jhamtani,http://arxiv.org/pdf/2305.08677v1.pdf,2023-05-15,['cs.cl'],2305.08677v1.pdf,"  Natural language interfaces often require supervised data to translate user
+requests into programs, database queries, or other structured intent
+representations. During data collection, it can be difficult to anticipate and
+formalize the full range of user needs -- for example, in a system designed to
+handle simple requests (like $\textit{find my meetings tomorrow}$ or
+$\textit{move my meeting with my manager to noon})$, users may also express
+more elaborate requests (like $\textit{swap all my calls on Monday and
+Tuesday}$). We introduce an approach for equipping a simple language-to-code
+model to handle complex utterances via a process of hierarchical natural
+language decomposition. Our approach uses a pre-trained language model to
+decompose a complex utterance into a sequence of smaller natural language
+steps, then interprets each step using the language-to-code model. To test our
+approach, we collect and release DeCU -- a new NL-to-program benchmark to
+evaluate Decomposition of Complex Utterances. Experiments show that the
+proposed approach enables the interpretation of complex utterances with almost
+no complex training data, while outperforming standard few-shot prompting
+approaches.
+"
+Visualizing Linguistic Diversity of Text Datasets Synthesized by Large  Language Models,Emily Reif,http://arxiv.org/pdf/2305.11364v2.pdf,2023-05-19,"['cs.cl', 'cs.ai']",2305.11364v2.pdf,"  Large language models (LLMs) can be used to generate smaller, more refined
+datasets via few-shot prompting for benchmarking, fine-tuning or other use
+cases. However, understanding and evaluating these datasets is difficult, and
+the failure modes of LLM-generated data are still not well understood.
+Specifically, the data can be repetitive in surprising ways, not only
+semantically but also syntactically and lexically. We present LinguisticLens, a
+novel inter-active visualization tool for making sense of and analyzing
+syntactic diversity of LLM-generated datasets. LinguisticLens clusters text
+along syntactic, lexical, and semantic axes. It supports hierarchical
+visualization of a text dataset, allowing users to quickly scan for an overview
+and inspect individual examples. The live demo is available at
+shorturl.at/zHOUV.
+"
+Improved Compositional Generalization by Generating Demonstrations for  Meta-Learning,Sam Spilsbury,http://arxiv.org/pdf/2305.13092v1.pdf,2023-05-22,['cs.cl'],2305.13092v1.pdf,"  Meta-learning and few-shot prompting are viable methods to induce certain
+types of compositional behaviour. However, these methods can be very sensitive
+to the choice of support examples used. Choosing good supports from the
+training data for a given test query is already a difficult problem, but in
+some cases solving this may not even be enough. We consider a grounded language
+learning problem (gSCAN) where good support examples for certain test splits
+might not even exist in the training data, or would be infeasible to search
+for. We design an agent which instead generates possible supports which are
+relevant to the test query and current state of the world, then uses these
+supports via meta-learning to solve the test query. We show substantially
+improved performance on a previously unsolved compositional behaviour split
+without a loss of performance on other splits. Further experiments show that in
+this case, searching for relevant demonstrations even with an oracle function
+is not sufficient to attain good performance when using meta-learning.
+"
+SPARSEFIT: Few-shot Prompting with Sparse Fine-tuning for Jointly  Generating Predictions and Natural Language Explanations,Jesus Solano,http://arxiv.org/pdf/2305.13235v2.pdf,2023-05-22,"['cs.cl', 'cs.ai']",2305.13235v2.pdf,"  Explaining the decisions of neural models is crucial for ensuring their
+trustworthiness at deployment time. Using Natural Language Explanations (NLEs)
+to justify a model's predictions has recently gained increasing interest.
+However, this approach usually demands large datasets of human-written NLEs for
+the ground-truth answers, which are expensive and potentially infeasible for
+some applications. For models to generate high-quality NLEs when only a few
+NLEs are available, the fine-tuning of Pre-trained Language Models (PLMs) in
+conjunction with prompt-based learning recently emerged. However, PLMs
+typically have billions of parameters, making fine-tuning expensive. We propose
+SparseFit, a sparse few-shot fine-tuning strategy that leverages discrete
+prompts to jointly generate predictions and NLEs. We experiment with SparseFit
+on the T5 model and four datasets and compare it against state-of-the-art
+parameter-efficient fine-tuning techniques. We perform automatic and human
+evaluations to assess the quality of the model-generated NLEs, finding that
+fine-tuning only 6.8% of the model parameters leads to competitive results for
+both the task performance and the quality of the NLEs.
+"
+Towards Legally Enforceable Hate Speech Detection for Public Forums,Chu Fei Luo,http://arxiv.org/pdf/2305.13677v2.pdf,2023-05-23,['cs.cl'],2305.13677v2.pdf,"  Hate speech causes widespread and deep-seated societal issues. Proper
+enforcement of hate speech laws is key for protecting groups of people against
+harmful and discriminatory language. However, determining what constitutes hate
+speech is a complex task that is highly open to subjective interpretations.
+Existing works do not align their systems with enforceable definitions of hate
+speech, which can make their outputs inconsistent with the goals of regulators.
+This research introduces a new perspective and task for enforceable hate speech
+detection centred around legal definitions, and a dataset annotated on
+violations of eleven possible definitions by legal experts. Given the challenge
+of identifying clear, legally enforceable instances of hate speech, we augment
+the dataset with expert-generated samples and an automatically mined challenge
+set. We experiment with grounding the model decision in these definitions using
+zero-shot and few-shot prompting. We then report results on several large
+language models (LLMs). With this task definition, automatic hate speech
+detection can be more closely aligned to enforceable laws, and hence assist in
+more rigorous enforcement of legal protections against harmful speech in public
+forums.
+"
+PEARL: Prompting Large Language Models to Plan and Execute Actions Over  Long Documents,Simeng Sun,http://arxiv.org/pdf/2305.14564v1.pdf,2023-05-23,['cs.cl'],2305.14564v1.pdf,"  Strategies such as chain-of-thought prompting improve the performance of
+large language models (LLMs) on complex reasoning tasks by decomposing input
+examples into intermediate steps. However, it remains unclear how to apply such
+methods to reason over long input documents, in which both the decomposition
+and the output of each intermediate step are non-trivial to obtain. In this
+work, we propose PEARL, a prompting framework to improve reasoning over long
+documents, which consists of three stages: action mining, plan formulation, and
+plan execution. More specifically, given a question about a long document,
+PEARL decomposes the question into a sequence of actions (e.g., SUMMARIZE,
+FIND_EVENT, FIND_RELATION) and then executes them over the document to obtain
+the answer. Each stage of PEARL is implemented via zero-shot or few-shot
+prompting of LLMs (in our work, GPT-4) with minimal human input. We evaluate
+PEARL on a challenging subset of the QuALITY dataset, which contains questions
+that require complex reasoning over long narrative texts. PEARL outperforms
+zero-shot and chain-of-thought prompting on this dataset, and ablation
+experiments show that each stage of PEARL is critical to its performance.
+Overall, PEARL is a first step towards leveraging LLMs to reason over long
+documents.
+"
+Large Language Model Distillation Doesn't Need a Teacher,Ananya Harsh Jha,http://arxiv.org/pdf/2305.14864v1.pdf,2023-05-24,['cs.cl'],2305.14864v1.pdf,"  Knowledge distillation trains a smaller student model to match the output
+distribution of a larger teacher to maximize the end-task performance under
+computational constraints. However, existing literature on language model
+distillation primarily focuses on compressing encoder-only models that are then
+specialized by task-specific supervised finetuning. We need to rethink this
+setup for more recent large language models with tens to hundreds of billions
+of parameters. Task-specific finetuning is impractical at this scale, and model
+performance is often measured using zero/few-shot prompting. Thus, in this
+work, we advocate for task-agnostic zero-shot evaluated distillation for large
+language models without access to end-task finetuning data. We propose a
+teacher-free task-agnostic distillation method, which uses a truncated version
+of the larger model for initialization, and continues pretraining this model
+using a language modeling objective. Our teacher-free method shines in a
+distillation regime where it is infeasible to fit both the student and teacher
+into the GPU memory. Despite its simplicity, our method can effectively reduce
+the model size by 50\%, matching or outperforming the vanilla distillation
+method on perplexity and accuracy on 13 zero-shot end-tasks while being 1.5x
+computationally efficient.
+"
+Revisiting non-English Text Simplification: A Unified Multilingual  Benchmark,Michael J. Ryan,http://arxiv.org/pdf/2305.15678v1.pdf,2023-05-25,"['cs.cl', 'cs.ai']",2305.15678v1.pdf,"  Recent advancements in high-quality, large-scale English resources have
+pushed the frontier of English Automatic Text Simplification (ATS) research.
+However, less work has been done on multilingual text simplification due to the
+lack of a diverse evaluation benchmark that covers complex-simple sentence
+pairs in many languages. This paper introduces the MultiSim benchmark, a
+collection of 27 resources in 12 distinct languages containing over 1.7 million
+complex-simple sentence pairs. This benchmark will encourage research in
+developing more effective multilingual text simplification models and
+evaluation metrics. Our experiments using MultiSim with pre-trained
+multilingual language models reveal exciting performance improvements from
+multilingual training in non-English settings. We observe strong performance
+from Russian in zero-shot cross-lingual transfer to low-resource languages. We
+further show that few-shot prompting with BLOOM-176b achieves comparable
+quality to reference simplifications outperforming fine-tuned models in most
+languages. We validate these findings through human evaluation.
+"
+Do GPTs Produce Less Literal Translations?,Vikas Raunak,http://arxiv.org/pdf/2305.16806v4.pdf,2023-05-26,"['cs.cl', 'cs.ai']",2305.16806v4.pdf,"  Large Language Models (LLMs) such as GPT-3 have emerged as general-purpose
+language models capable of addressing many natural language generation or
+understanding tasks. On the task of Machine Translation (MT), multiple works
+have investigated few-shot prompting mechanisms to elicit better translations
+from LLMs. However, there has been relatively little investigation on how such
+translations differ qualitatively from the translations generated by standard
+Neural Machine Translation (NMT) models. In this work, we investigate these
+differences in terms of the literalness of translations produced by the two
+systems. Using literalness measures involving word alignment and monotonicity,
+we find that translations out of English (E-X) from GPTs tend to be less
+literal, while exhibiting similar or better scores on MT quality metrics. We
+demonstrate that this finding is borne out in human evaluations as well. We
+then show that these differences are especially pronounced when translating
+sentences that contain idiomatic expressions.
+"
+Log Parsing: How Far Can ChatGPT Go?,Van-Hoang Le,http://arxiv.org/pdf/2306.01590v2.pdf,2023-06-02,"['cs.se', 'cs.ai']",2306.01590v2.pdf,"  Software logs play an essential role in ensuring the reliability and
+maintainability of large-scale software systems, as they are often the sole
+source of runtime information. Log parsing, which converts raw log messages
+into structured data, is an important initial step towards downstream log
+analytics. In recent studies, ChatGPT, the current cutting-edge large language
+model (LLM), has been widely applied to a wide range of software engineering
+tasks. However, its performance in automated log parsing remains unclear. In
+this paper, we evaluate ChatGPT's ability to undertake log parsing by
+addressing two research questions. (1) Can ChatGPT effectively parse logs? (2)
+How does ChatGPT perform with different prompting methods? Our results show
+that ChatGPT can achieve promising results for log parsing with appropriate
+prompts, especially with few-shot prompting. Based on our findings, we outline
+several challenges and opportunities for ChatGPT-based log parsing.
+"
+Large Language Model Augmented Narrative Driven Recommendations,Sheshera Mysore,http://arxiv.org/pdf/2306.02250v2.pdf,2023-06-04,"['cs.ir', 'cs.cl']",2306.02250v2.pdf,"  Narrative-driven recommendation (NDR) presents an information access problem
+where users solicit recommendations with verbose descriptions of their
+preferences and context, for example, travelers soliciting recommendations for
+points of interest while describing their likes/dislikes and travel
+circumstances. These requests are increasingly important with the rise of
+natural language-based conversational interfaces for search and recommendation
+systems. However, NDR lacks abundant training data for models, and current
+platforms commonly do not support these requests. Fortunately, classical
+user-item interaction datasets contain rich textual data, e.g., reviews, which
+often describe user preferences and context - this may be used to bootstrap
+training for NDR models. In this work, we explore using large language models
+(LLMs) for data augmentation to train NDR models. We use LLMs for authoring
+synthetic narrative queries from user-item interactions with few-shot prompting
+and train retrieval models for NDR on synthetic queries and user-item
+interaction data. Our experiments demonstrate that this is an effective
+strategy for training small-parameter retrieval models that outperform other
+retrieval and LLM baselines for narrative-driven recommendation.
+"
+Enhancing In-Context Learning with Answer Feedback for Multi-Span  Question Answering,Zixian Huang,http://arxiv.org/pdf/2306.04508v1.pdf,2023-06-07,"['cs.cl', 'cs.ai']",2306.04508v1.pdf,"  Whereas the recent emergence of large language models (LLMs) like ChatGPT has
+exhibited impressive general performance, it still has a large gap with
+fully-supervised models on specific tasks such as multi-span question
+answering. Previous researches found that in-context learning is an effective
+approach to exploiting LLM, by using a few task-related labeled data as
+demonstration examples to construct a few-shot prompt for answering new
+questions. A popular implementation is to concatenate a few questions and their
+correct answers through simple templates, informing LLM of the desired output.
+In this paper, we propose a novel way of employing labeled data such that it
+also informs LLM of some undesired output, by extending demonstration examples
+with feedback about answers predicted by an off-the-shelf model, e.g., correct,
+incorrect, or incomplete. Experiments on three multi-span question answering
+datasets as well as a keyphrase extraction dataset show that our new prompting
+strategy consistently improves LLM's in-context learning performance.
+"
+Product Information Extraction using ChatGPT,Alexander Brinkmann,http://arxiv.org/pdf/2306.14921v1.pdf,2023-06-23,"['cs.cl', 'cs.ir']",2306.14921v1.pdf,"  Structured product data in the form of attribute/value pairs is the
+foundation of many e-commerce applications such as faceted product search,
+product comparison, and product recommendation. Product offers often only
+contain textual descriptions of the product attributes in the form of titles or
+free text. Hence, extracting attribute/value pairs from textual product
+descriptions is an essential enabler for e-commerce applications. In order to
+excel, state-of-the-art product information extraction methods require large
+quantities of task-specific training data. The methods also struggle with
+generalizing to out-of-distribution attributes and attribute values that were
+not a part of the training data. Due to being pre-trained on huge amounts of
+text as well as due to emergent effects resulting from the model size, Large
+Language Models like ChatGPT have the potential to address both of these
+shortcomings. This paper explores the potential of ChatGPT for extracting
+attribute/value pairs from product descriptions. We experiment with different
+zero-shot and few-shot prompt designs. Our results show that ChatGPT achieves a
+performance similar to a pre-trained language model but requires much smaller
+amounts of training data and computation for fine-tuning.
+"
+SummQA at MEDIQA-Chat 2023:In-Context Learning with GPT-4 for Medical  Summarization,Yash Mathur,http://arxiv.org/pdf/2306.17384v1.pdf,2023-06-30,['cs.cl'],2306.17384v1.pdf,"  Medical dialogue summarization is challenging due to the unstructured nature
+of medical conversations, the use of medical terminology in gold summaries, and
+the need to identify key information across multiple symptom sets. We present a
+novel system for the Dialogue2Note Medical Summarization tasks in the MEDIQA
+2023 Shared Task. Our approach for section-wise summarization (Task A) is a
+two-stage process of selecting semantically similar dialogues and using the
+top-k similar dialogues as in-context examples for GPT-4. For full-note
+summarization (Task B), we use a similar solution with k=1. We achieved 3rd
+place in Task A (2nd among all teams), 4th place in Task B Division Wise
+Summarization (2nd among all teams), 15th place in Task A Section Header
+Classification (9th among all teams), and 8th place among all teams in Task B.
+Our results highlight the effectiveness of few-shot prompting for this task,
+though we also identify several weaknesses of prompting-based approaches. We
+compare GPT-4 performance with several finetuned baselines. We find that GPT-4
+summaries are more abstractive and shorter. We make our code publicly
+available.
+"
+Building Cooperative Embodied Agents Modularly with Large Language  Models,Hongxin Zhang,http://arxiv.org/pdf/2307.02485v1.pdf,2023-07-05,"['cs.ai', 'cs.cl', 'cs.cv']",2307.02485v1.pdf,"  Large Language Models (LLMs) have demonstrated impressive planning abilities
+in single-agent embodied tasks across various domains. However, their capacity
+for planning and communication in multi-agent cooperation remains unclear, even
+though these are crucial skills for intelligent embodied agents. In this paper,
+we present a novel framework that utilizes LLMs for multi-agent cooperation and
+tests it in various embodied environments. Our framework enables embodied
+agents to plan, communicate, and cooperate with other embodied agents or humans
+to accomplish long-horizon tasks efficiently. We demonstrate that recent LLMs,
+such as GPT-4, can surpass strong planning-based methods and exhibit emergent
+effective communication using our framework without requiring fine-tuning or
+few-shot prompting. We also discover that LLM-based agents that communicate in
+natural language can earn more trust and cooperate more effectively with
+humans. Our research underscores the potential of LLMs for embodied AI and lays
+the foundation for future research in multi-agent cooperation. Videos can be
+found on the project website https://vis-www.cs.umass.edu/Co-LLM-Agents/.
+"
+MultiQG-TI: Towards Question Generation from Multi-modal Sources,Zichao Wang,http://arxiv.org/pdf/2307.04643v1.pdf,2023-07-07,"['cs.cl', 'cs.ai']",2307.04643v1.pdf,"  We study the new problem of automatic question generation (QG) from
+multi-modal sources containing images and texts, significantly expanding the
+scope of most of the existing work that focuses exclusively on QG from only
+textual sources. We propose a simple solution for our new problem, called
+MultiQG-TI, which enables a text-only question generator to process visual
+input in addition to textual input. Specifically, we leverage an image-to-text
+model and an optical character recognition model to obtain the textual
+description of the image and extract any texts in the image, respectively, and
+then feed them together with the input texts to the question generator. We only
+fine-tune the question generator while keeping the other components fixed. On
+the challenging ScienceQA dataset, we demonstrate that MultiQG-TI significantly
+outperforms ChatGPT with few-shot prompting, despite having hundred-times less
+trainable parameters. Additional analyses empirically confirm the necessity of
+both visual and textual signals for QG and show the impact of various modeling
+choices.
+"
+Why Is Prompt Tuning for Vision-Language Models Robust to Noisy Labels?,Cheng-En Wu,http://arxiv.org/pdf/2307.11978v1.pdf,2023-07-22,"['cs.cv', 'cs.ai', 'cs.lg']",2307.11978v1.pdf,"  Vision-language models such as CLIP learn a generic text-image embedding from
+large-scale training data. A vision-language model can be adapted to a new
+classification task through few-shot prompt tuning. We find that such a prompt
+tuning process is highly robust to label noises. This intrigues us to study the
+key reasons contributing to the robustness of the prompt tuning paradigm. We
+conducted extensive experiments to explore this property and find the key
+factors are: 1) the fixed classname tokens provide a strong regularization to
+the optimization of the model, reducing gradients induced by the noisy samples;
+2) the powerful pre-trained image-text embedding that is learned from diverse
+and generic web data provides strong prior knowledge for image classification.
+Further, we demonstrate that noisy zero-shot predictions from CLIP can be used
+to tune its own prompt, significantly enhancing prediction accuracy in the
+unsupervised setting. The code is available at https://github.com/CEWu/PTNL.
+"
+Analyzing Chain-of-Thought Prompting in Large Language Models via  Gradient-based Feature Attributions,Skyler Wu,http://arxiv.org/pdf/2307.13339v1.pdf,2023-07-25,"['cs.cl', 'cs.ai']",2307.13339v1.pdf,"  Chain-of-thought (CoT) prompting has been shown to empirically improve the
+accuracy of large language models (LLMs) on various question answering tasks.
+While understanding why CoT prompting is effective is crucial to ensuring that
+this phenomenon is a consequence of desired model behavior, little work has
+addressed this; nonetheless, such an understanding is a critical prerequisite
+for responsible model deployment. We address this question by leveraging
+gradient-based feature attribution methods which produce saliency scores that
+capture the influence of input tokens on model output. Specifically, we probe
+several open-source LLMs to investigate whether CoT prompting affects the
+relative importances they assign to particular input tokens. Our results
+indicate that while CoT prompting does not increase the magnitude of saliency
+scores attributed to semantically relevant tokens in the prompt compared to
+standard few-shot prompting, it increases the robustness of saliency scores to
+question perturbations and variations in model output.
+"
+Low-Parameter Federated Learning with Large Language Models,Jingang Jiang,http://arxiv.org/pdf/2307.13896v1.pdf,2023-07-26,['cs.dc'],2307.13896v1.pdf,"  We study few-shot Natural Language Understanding (NLU) tasks with Large
+Language Models (LLMs) in federated learning (FL) scenarios. It is a
+challenging task due to limited labeled data and communication capacities in
+FL, especially with mobile devices. Recent studies show LLMs can be prompted to
+perform few-shot NLU tasks like sentiment analysis and arithmetic reasoning.
+However, the huge sizes of LLMs result in high computation and communication
+costs, making classical FL schemes impractical. To address these challenges, we
+propose Low-Parameter Federated Learning (LP-FL). LP-FL combines few-shot
+prompt learning from LLMs with efficient communication and federating
+techniques. Our approach enables federated clients to assign soft labels to
+unlabeled data using gradually learned knowledge from the global model. Through
+iterative soft-label assigning, we continually expand the labeled set during
+the FL process. Additionally, to reduce computation and communication costs,
+LP-FL utilizes the Low-Rank Adaptation (LoRA) technique for compact learnable
+parameter construction, efficient local model fine-tuning, and affordable
+global model federation. LP-FL consistently outperforms Full-Parameter
+Federated Learning (FP-FL) in sentiment analysis tasks across various FL
+settings. Its resistance to overfitting allows LP-FL to equal or surpass
+centralized training in few-shot scenarios.
+"
+Large Language Model Prompt Chaining for Long Legal Document  Classification,Dietrich Trautmann,http://arxiv.org/pdf/2308.04138v1.pdf,2023-08-08,['cs.cl'],2308.04138v1.pdf,"  Prompting is used to guide or steer a language model in generating an
+appropriate response that is consistent with the desired outcome. Chaining is a
+strategy used to decompose complex tasks into smaller, manageable components.
+In this study, we utilize prompt chaining for extensive legal document
+classification tasks, which present difficulties due to their intricate
+domain-specific language and considerable length. Our approach begins with the
+creation of a concise summary of the original document, followed by a semantic
+search for related exemplar texts and their corresponding annotations from a
+training corpus. Finally, we prompt for a label - based on the task - to
+assign, by leveraging the in-context learning from the few-shot prompt. We
+demonstrate that through prompt chaining, we can not only enhance the
+performance over zero-shot, but also surpass the micro-F1 score achieved by
+larger models, such as ChatGPT zero-shot, using smaller models.
+"
+FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for  Large Language Models,Liwen Zhang,http://arxiv.org/pdf/2308.09975v1.pdf,2023-08-19,['cs.cl'],2308.09975v1.pdf,"  Large language models (LLMs) have demonstrated exceptional performance in
+various natural language processing tasks, yet their efficacy in more
+challenging and domain-specific tasks remains largely unexplored. This paper
+presents FinEval, a benchmark specifically designed for the financial domain
+knowledge in the LLMs. FinEval is a collection of high-quality multiple-choice
+questions covering Finance, Economy, Accounting, and Certificate. It includes
+4,661 questions spanning 34 different academic subjects. To ensure a
+comprehensive model performance evaluation, FinEval employs a range of prompt
+types, including zero-shot and few-shot prompts, as well as answer-only and
+chain-of-thought prompts. Evaluating state-of-the-art Chinese and English LLMs
+on FinEval, the results show that only GPT-4 achieved an accuracy close to 70%
+in different prompt settings, indicating significant growth potential for LLMs
+in the financial domain knowledge. Our work offers a more comprehensive
+financial knowledge evaluation benchmark, utilizing data of mock exams and
+covering a wide range of evaluated LLMs.
+"
+Diversity Measures: Domain-Independent Proxies for Failure in Language  Model Queries,Noel Ngu,http://arxiv.org/pdf/2308.11189v1.pdf,2023-08-22,"['cs.cl', 'cs.ai', 'cs.lg']",2308.11189v1.pdf,"  Error prediction in large language models often relies on domain-specific
+information. In this paper, we present measures for quantification of error in
+the response of a large language model based on the diversity of responses to a
+given prompt - hence independent of the underlying application. We describe how
+three such measures - based on entropy, Gini impurity, and centroid distance -
+can be employed. We perform a suite of experiments on multiple datasets and
+temperature settings to demonstrate that these measures strongly correlate with
+the probability of failure. Additionally, we present empirical results
+demonstrating how these measures can be applied to few-shot prompting,
+chain-of-thought reasoning, and error detection.
+"
+Evaluating Large Language Models on Graphs: Performance Insights and  Comparative Analysis,Chang Liu,http://arxiv.org/pdf/2308.11224v2.pdf,2023-08-22,"['cs.ai', 'cs.cl']",2308.11224v2.pdf,"  Large Language Models (LLMs) have garnered considerable interest within both
+academic and industrial. Yet, the application of LLMs to graph data remains
+under-explored. In this study, we evaluate the capabilities of four LLMs in
+addressing several analytical problems with graph data. We employ four distinct
+evaluation metrics: Comprehension, Correctness, Fidelity, and Rectification.
+Our results show that: 1) LLMs effectively comprehend graph data in natural
+language and reason with graph topology. 2) GPT models can generate logical and
+coherent results, outperforming alternatives in correctness. 3) All examined
+LLMs face challenges in structural reasoning, with techniques like zero-shot
+chain-of-thought and few-shot prompting showing diminished efficacy. 4) GPT
+models often produce erroneous answers in multi-answer tasks, raising concerns
+in fidelity. 5) GPT models exhibit elevated confidence in their outputs,
+potentially hindering their rectification capacities. Notably, GPT-4 has
+demonstrated the capacity to rectify responses from GPT-3.5-turbo and its own
+previous iterations. The code is available at:
+https://github.com/Ayame1006/LLMtoGraph.
+"
+Prompt2Model: Generating Deployable Models from Natural Language  Instructions,Vijay Viswanathan,http://arxiv.org/pdf/2308.12261v1.pdf,2023-08-23,['cs.cl'],2308.12261v1.pdf,"  Large language models (LLMs) enable system builders today to create competent
+NLP systems through prompting, where they only need to describe the task in
+natural language and provide a few examples. However, in other ways, LLMs are a
+step backward from traditional special-purpose NLP models; they require
+extensive computational resources for deployment and can be gated behind APIs.
+In this paper, we propose Prompt2Model, a general-purpose method that takes a
+natural language task description like the prompts provided to LLMs, and uses
+it to train a special-purpose model that is conducive to deployment. This is
+done through a multi-step process of retrieval of existing datasets and
+pretrained models, dataset generation using LLMs, and supervised fine-tuning on
+these retrieved and generated datasets. Over three tasks, we demonstrate that
+given the same few-shot prompt as input, Prompt2Model trains models that
+outperform the results of a strong LLM, gpt-3.5-turbo, by an average of 20%
+while being up to 700 times smaller. We also show that this data can be used to
+obtain reliable performance estimates of model performance, enabling model
+developers to assess model reliability before deployment. Prompt2Model is
+available open-source at https://github.com/neulab/prompt2model.
+"
+Prompt a Robot to Walk with Large Language Models,Yen-Jen Wang,http://arxiv.org/pdf/2309.09969v1.pdf,2023-09-18,"['cs.ro', 'cs.lg', 'cs.sy', 'eess.sy']",2309.09969v1.pdf,"  Large language models (LLMs) pre-trained on vast internet-scale data have
+showcased remarkable capabilities across diverse domains. Recently, there has
+been escalating interest in deploying LLMs for robotics, aiming to harness the
+power of foundation models in real-world settings. However, this approach faces
+significant challenges, particularly in grounding these models in the physical
+world and in generating dynamic robot motions. To address these issues, we
+introduce a novel paradigm in which we use few-shot prompts collected from the
+physical environment, enabling the LLM to autoregressively generate low-level
+control commands for robots without task-specific fine-tuning. Experiments
+across various robots and environments validate that our method can effectively
+prompt a robot to walk. We thus illustrate how LLMs can proficiently function
+as low-level feedback controllers for dynamic motion control even in
+high-dimensional robotic systems. The project website and source code can be
+found at: https://prompt2walk.github.io/ .
+"
+SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language  Models,Shyam Sundar Kannan,http://arxiv.org/pdf/2309.10062v1.pdf,2023-09-18,['cs.ro'],2309.10062v1.pdf,"  In this work, we introduce SMART-LLM, an innovative framework designed for
+embodied multi-robot task planning. SMART-LLM: Smart Multi-Agent Robot Task
+Planning using Large Language Models (LLMs), harnesses the power of LLMs to
+convert high-level task instructions provided as input into a multi-robot task
+plan. It accomplishes this by executing a series of stages, including task
+decomposition, coalition formation, and task allocation, all guided by
+programmatic LLM prompts within the few-shot prompting paradigm. We create a
+benchmark dataset designed for validating the multi-robot task planning
+problem, encompassing four distinct categories of high-level instructions that
+vary in task complexity. Our evaluation experiments span both simulation and
+real-world scenarios, demonstrating that the proposed model can achieve
+promising results for generating multi-robot task plans. The experimental
+videos, code, and datasets from the work can be found at
+https://sites.google.com/view/smart-llm/.
+"
+EchoPrompt: Instructing the Model to Rephrase Queries for Improved  In-context Learning,Rajasekhar Reddy Mekala,http://arxiv.org/pdf/2309.10687v2.pdf,2023-09-16,['cs.cl'],2309.10687v2.pdf,"  Language models are achieving impressive performance on various tasks by
+aggressively adopting inference-time prompting techniques, such as zero-shot
+and few-shot prompting. In this work, we introduce EchoPrompt, a simple yet
+effective approach that prompts the model to rephrase its queries before
+answering them. EchoPrompt is adapted for both zero-shot and few-shot
+in-context learning with standard and chain-of-thought prompting. Experimental
+results show that EchoPrompt yields substantial improvements across all these
+settings for four families of causal language models. These improvements are
+observed across various numerical reasoning (e.g. GSM8K, SVAMP), reading
+comprehension (e.g. DROP), and logical reasoning (e.g. Coin Flipping) tasks. On
+average, EchoPrompt improves the Zero-shot-CoT performance of code-davinci-002
+by 5% in numerical tasks and 13% in reading comprehension tasks. We investigate
+the factors contributing to EchoPrompt's effectiveness through ablation
+studies, which reveal that both the original query and the model-generated
+rephrased version are instrumental in its performance gains. Our empirical
+results indicate that EchoPrompt is an effective technique that enhances
+in-context learning performance. We recommend incorporating EchoPrompt into
+various baseline prompting strategies to achieve performance boosts.
+"
+Self-Explanation Prompting Improves Dialogue Understanding in Large  Language Models,Haoyu Gao,http://arxiv.org/pdf/2309.12940v1.pdf,2023-09-22,"['cs.cl', 'cs.ai']",2309.12940v1.pdf,"  Task-oriented dialogue (TOD) systems facilitate users in executing various
+activities via multi-turn dialogues, but Large Language Models (LLMs) often
+struggle to comprehend these intricate contexts. In this study, we propose a
+novel ""Self-Explanation"" prompting strategy to enhance the comprehension
+abilities of LLMs in multi-turn dialogues. This task-agnostic approach requires
+the model to analyze each dialogue utterance before task execution, thereby
+improving performance across various dialogue-centric tasks. Experimental
+results from six benchmark datasets confirm that our method consistently
+outperforms other zero-shot prompts and matches or exceeds the efficacy of
+few-shot prompts, demonstrating its potential as a powerful tool in enhancing
+LLMs' comprehension in complex dialogue tasks.
+"
+Language Models as Knowledge Bases for Visual Word Sense Disambiguation,Anastasia Kritharoula,http://arxiv.org/pdf/2310.01960v1.pdf,2023-10-03,"['cs.cl', 'cs.ai']",2310.01960v1.pdf,"  Visual Word Sense Disambiguation (VWSD) is a novel challenging task that lies
+between linguistic sense disambiguation and fine-grained multimodal retrieval.
+The recent advancements in the development of visiolinguistic (VL) transformers
+suggest some off-the-self implementations with encouraging results, which
+however we argue that can be further improved. To this end, we propose some
+knowledge-enhancement techniques towards improving the retrieval performance of
+VL transformers via the usage of Large Language Models (LLMs) as Knowledge
+Bases. More specifically, knowledge stored in LLMs is retrieved with the help
+of appropriate prompts in a zero-shot manner, achieving performance
+advancements. Moreover, we convert VWSD to a purely textual question-answering
+(QA) problem by considering generated image captions as multiple-choice
+candidate answers. Zero-shot and few-shot prompting strategies are leveraged to
+explore the potential of such a transformation, while Chain-of-Thought (CoT)
+prompting in the zero-shot setting is able to reveal the internal reasoning
+steps an LLM follows to select the appropriate candidate. In total, our
+presented approach is the first one to analyze the merits of exploiting
+knowledge stored in LLMs in different ways to solve WVSD.
+"
+Can Large Language Models be Good Path Planners? A Benchmark and  Investigation on Spatial-temporal Reasoning,Mohamed Aghzal,http://arxiv.org/pdf/2310.03249v1.pdf,2023-10-05,['cs.cl'],2310.03249v1.pdf,"  Large language models (LLMs) have achieved remarkable success across a wide
+spectrum of tasks; however, they still face limitations in scenarios that
+demand long-term planning and spatial reasoning. To facilitate this line of
+research, in this work, we propose a new benchmark, termed $\textbf{P}$ath
+$\textbf{P}$lanning from $\textbf{N}$atural $\textbf{L}$anguage
+($\textbf{PPNL}$). Our benchmark evaluates LLMs' spatial-temporal reasoning by
+formulating ''path planning'' tasks that require an LLM to navigate to target
+locations while avoiding obstacles and adhering to constraints. Leveraging this
+benchmark, we systematically investigate LLMs including GPT-4 via different
+few-shot prompting methodologies and BART and T5 of various sizes via
+fine-tuning. Our experimental results show the promise of few-shot GPT-4 in
+spatial reasoning, when it is prompted to reason and act interleavedly,
+although it still fails to make long-term temporal reasoning. In contrast,
+while fine-tuned LLMs achieved impressive results on in-distribution reasoning
+tasks, they struggled to generalize to larger environments or environments with
+more obstacles.
+"
+Towards Informative Few-Shot Prompt with Maximum Information Gain for  In-Context Learning,Hongfu Liu,http://arxiv.org/pdf/2310.08923v1.pdf,2023-10-13,['cs.cl'],2310.08923v1.pdf,"  Large Language models (LLMs) possess the capability to engage In-context
+Learning (ICL) by leveraging a few demonstrations pertaining to a new
+downstream task as conditions. However, this particular learning paradigm
+suffers from high instability stemming from substantial variances induced by
+factors such as the input distribution of selected examples, their ordering,
+and prompt formats. In this work, we demonstrate that even when all these
+factors are held constant, the random selection of examples still results in
+high variance. Consequently, we aim to explore the informative ability of data
+examples by quantifying the Information Gain (IG) obtained in prediction after
+observing a given example candidate. Then we propose to sample those with
+maximum IG. Additionally, we identify the presence of template bias, which can
+lead to unfair evaluations of IG during the sampling process. To mitigate this
+bias, we introduce Calibration Before Sampling strategy. The experimental
+results illustrate that our proposed method can yield an average relative
+improvement of 14.3% across six classification tasks using three LLMs.
+"
+Ecologically Valid Explanations for Label Variation in NLI,Nan-Jiang Jiang,http://arxiv.org/pdf/2310.13850v1.pdf,2023-10-20,['cs.cl'],2310.13850v1.pdf,"  Human label variation, or annotation disagreement, exists in many natural
+language processing (NLP) tasks, including natural language inference (NLI). To
+gain direct evidence of how NLI label variation arises, we build LiveNLI, an
+English dataset of 1,415 ecologically valid explanations (annotators explain
+the NLI labels they chose) for 122 MNLI items (at least 10 explanations per
+item). The LiveNLI explanations confirm that people can systematically vary on
+their interpretation and highlight within-label variation: annotators sometimes
+choose the same label for different reasons. This suggests that explanations
+are crucial for navigating label interpretations in general. We few-shot prompt
+large language models to generate explanations but the results are
+inconsistent: they sometimes produces valid and informative explanations, but
+it also generates implausible ones that do not support the label, highlighting
+directions for improvement.
+"
+API-Assisted Code Generation for Question Answering on Varied Table  Structures,Yihan Cao,http://arxiv.org/pdf/2310.14687v1.pdf,2023-10-23,"['cs.cl', 'cs.ai']",2310.14687v1.pdf,"  A persistent challenge to table question answering (TableQA) by generating
+executable programs has been adapting to varied table structures, typically
+requiring domain-specific logical forms. In response, this paper introduces a
+unified TableQA framework that: (1) provides a unified representation for
+structured tables as multi-index Pandas data frames, (2) uses Python as a
+powerful querying language, and (3) uses few-shot prompting to translate NL
+questions into Python programs, which are executable on Pandas data frames.
+Furthermore, to answer complex relational questions with extended program
+functionality and external knowledge, our framework allows customized APIs that
+Python programs can call. We experiment with four TableQA datasets that involve
+tables of different structures -- relational, multi-table, and hierarchical
+matrix shapes -- and achieve prominent improvements over past state-of-the-art
+systems. In ablation studies, we (1) show benefits from our multi-index
+representation and APIs over baselines that use only an LLM, and (2)
+demonstrate that our approach is modular and can incorporate additional APIs.
+"
+Tree of Clarifications: Answering Ambiguous Questions with  Retrieval-Augmented Large Language Models,Gangwoo Kim,http://arxiv.org/pdf/2310.14696v1.pdf,2023-10-23,['cs.cl'],2310.14696v1.pdf,"  Questions in open-domain question answering are often ambiguous, allowing
+multiple interpretations. One approach to handling them is to identify all
+possible interpretations of the ambiguous question (AQ) and to generate a
+long-form answer addressing them all, as suggested by Stelmakh et al., (2022).
+While it provides a comprehensive response without bothering the user for
+clarification, considering multiple dimensions of ambiguity and gathering
+corresponding knowledge remains a challenge. To cope with the challenge, we
+propose a novel framework, Tree of Clarifications (ToC): It recursively
+constructs a tree of disambiguations for the AQ -- via few-shot prompting
+leveraging external knowledge -- and uses it to generate a long-form answer.
+ToC outperforms existing baselines on ASQA in a few-shot setup across the
+metrics, while surpassing fully-supervised baselines trained on the whole
+training set in terms of Disambig-F1 and Disambig-ROUGE. Code is available at
+https://github.com/gankim/tree-of-clarifications.
+"
+Dissecting In-Context Learning of Translations in GPTs,Vikas Raunak,http://arxiv.org/pdf/2310.15987v1.pdf,2023-10-24,"['cs.cl', 'cs.ai']",2310.15987v1.pdf,"  Most of the recent work in leveraging Large Language Models (LLMs) such as
+GPT-3 for Machine Translation (MT) has focused on selecting the few-shot
+samples for prompting. In this work, we try to better understand the role of
+demonstration attributes for the in-context learning of translations through
+perturbations of high-quality, in-domain demonstrations. We find that
+asymmetric perturbation of the source-target mappings yield vastly different
+results. We show that the perturbation of the source side has surprisingly
+little impact, while target perturbation can drastically reduce translation
+quality, suggesting that it is the output text distribution that provides the
+most important learning signal during in-context learning of translations. We
+propose a method named Zero-Shot-Context to add this signal automatically in
+Zero-Shot prompting. We demonstrate that it improves upon the zero-shot
+translation performance of GPT-3, even making it competitive with few-shot
+prompted translations.
+"
+Extraction of Atypical Aspects from Customer Reviews: Datasets and  Experiments with Language Models,Smita Nannaware,http://arxiv.org/pdf/2311.02702v1.pdf,2023-11-05,"['cs.cl', 'cs.ai']",2311.02702v1.pdf,"  A restaurant dinner may become a memorable experience due to an unexpected
+aspect enjoyed by the customer, such as an origami-making station in the
+waiting area. If aspects that are atypical for a restaurant experience were
+known in advance, they could be leveraged to make recommendations that have the
+potential to engender serendipitous experiences, further increasing user
+satisfaction. Although relatively rare, whenever encountered, atypical aspects
+often end up being mentioned in reviews due to their memorable quality.
+Correspondingly, in this paper we introduce the task of detecting atypical
+aspects in customer reviews. To facilitate the development of extraction
+models, we manually annotate benchmark datasets of reviews in three domains -
+restaurants, hotels, and hair salons, which we use to evaluate a number of
+language models, ranging from fine-tuning the instruction-based text-to-text
+transformer Flan-T5 to zero-shot and few-shot prompting of GPT-3.5.
+"
+SQLPrompt: In-Context Text-to-SQL with Minimal Labeled Data,Ruoxi Sun,http://arxiv.org/pdf/2311.02883v1.pdf,2023-11-06,['cs.cl'],2311.02883v1.pdf,"  Text-to-SQL aims to automate the process of generating SQL queries on a
+database from natural language text. In this work, we propose ""SQLPrompt"",
+tailored to improve the few-shot prompting capabilities of Text-to-SQL for
+Large Language Models (LLMs). Our methods include innovative prompt design,
+execution-based consistency decoding strategy which selects the SQL with the
+most consistent execution outcome among other SQL proposals, and a method that
+aims to improve performance by diversifying the SQL proposals during
+consistency selection with different prompt designs (""MixPrompt"") and
+foundation models (""MixLLMs""). We show that \emph{SQLPrompt} outperforms
+previous approaches for in-context learning with few labeled data by a large
+margin, closing the gap with finetuning state-of-the-art with thousands of
+labeled data.
+"
+OLaLa: Ontology Matching with Large Language Models,Sven Hertling,http://arxiv.org/pdf/2311.03837v1.pdf,2023-11-07,"['cs.ir', 'cs.cl']",2311.03837v1.pdf,"  Ontology (and more generally: Knowledge Graph) Matching is a challenging task
+where information in natural language is one of the most important signals to
+process. With the rise of Large Language Models, it is possible to incorporate
+this knowledge in a better way into the matching pipeline. A number of
+decisions still need to be taken, e.g., how to generate a prompt that is useful
+to the model, how information in the KG can be formulated in prompts, which
+Large Language Model to choose, how to provide existing correspondences to the
+model, how to generate candidates, etc. In this paper, we present a prototype
+that explores these questions by applying zero-shot and few-shot prompting with
+multiple open Large Language Models to different tasks of the Ontology
+Alignment Evaluation Initiative (OAEI). We show that with only a handful of
+examples and a well-designed prompt, it is possible to achieve results that are
+en par with supervised matching systems which use a much larger portion of the
+ground truth.
+"
+Jurassic is (almost) All You Need: Few-Shot Meaning-to-Text Generation  for Open-Domain Dialogue,Lena Reed,http://arxiv.org/pdf/2110.08094v2.pdf,2021-10-15,['cs.cl'],2110.08094v2.pdf,"  One challenge with open-domain dialogue systems is the need to produce
+truthful, high-quality responses on any topic. We aim to improve the quality
+and coverage of Athena, an Alexa Prize dialogue system. We experiment with
+few-shot prompt-based learning, comparing GPT-Neo to Jurassic-1, for the
+movies, music, TV, sports, and video game domains, both within and
+cross-domain, with different prompt set sizes (2, 3, 10), formats, and meaning
+representations consisting of either sets of WikiData KG triples, or dialogue
+acts. Our evaluation uses BLEURT and human metrics, and shows that with 10-shot
+prompting, Athena-Jurassic's performance is significantly better for coherence
+and semantic accuracy. Experiments with 2-shot cross-domain prompts results in
+a huge performance drop for Athena-GPT-Neo, whose semantic accuracy falls to
+0.41, and whose untrue hallucination rate increases to 12%. Experiments with
+dialogue acts for video games show that with 10-shot prompting, both models
+learn to control dialogue acts, but Athena-Jurassic has significantly higher
+coherence, and only 4% untrue hallucinations. Our results suggest that
+Athena-Jurassic produces high enough quality outputs to be useful in live
+systems with real users. To our knowledge, these are the first results
+demonstrating that few-shot semantic prompt-based learning can create NLGs that
+generalize to new domains, and produce high-quality, semantically-controlled,
+conversational responses directly from meaning representations.
+"
+Code as Policies: Language Model Programs for Embodied Control,Jacky Liang,http://arxiv.org/pdf/2209.07753v4.pdf,2022-09-16,['cs.ro'],2209.07753v4.pdf,"  Large language models (LLMs) trained on code completion have been shown to be
+capable of synthesizing simple Python programs from docstrings [1]. We find
+that these code-writing LLMs can be re-purposed to write robot policy code,
+given natural language commands. Specifically, policy code can express
+functions or feedback loops that process perception outputs (e.g.,from object
+detectors [2], [3]) and parameterize control primitive APIs. When provided as
+input several example language commands (formatted as comments) followed by
+corresponding policy code (via few-shot prompting), LLMs can take in new
+commands and autonomously re-compose API calls to generate new policy code
+respectively. By chaining classic logic structures and referencing third-party
+libraries (e.g., NumPy, Shapely) to perform arithmetic, LLMs used in this way
+can write robot policies that (i) exhibit spatial-geometric reasoning, (ii)
+generalize to new instructions, and (iii) prescribe precise values (e.g.,
+velocities) to ambiguous descriptions (""faster"") depending on context (i.e.,
+behavioral commonsense). This paper presents code as policies: a robot-centric
+formulation of language model generated programs (LMPs) that can represent
+reactive policies (e.g., impedance controllers), as well as waypoint-based
+policies (vision-based pick and place, trajectory-based control), demonstrated
+across multiple real robot platforms. Central to our approach is prompting
+hierarchical code-gen (recursively defining undefined functions), which can
+write more complex code and also improves state-of-the-art to solve 39.8% of
+problems on the HumanEval [1] benchmark. Code and videos are available at
+https://code-as-policies.github.io
+"
+Spotlight: Mobile UI Understanding using Vision-Language Models with a  Focus,Gang Li,http://arxiv.org/pdf/2209.14927v4.pdf,2022-09-29,"['cs.cv', 'cs.hc', 'cs.lg']",2209.14927v4.pdf,"  Mobile UI understanding is important for enabling various interaction tasks
+such as UI automation and accessibility. Previous mobile UI modeling often
+depends on the view hierarchy information of a screen, which directly provides
+the structural data of the UI, with the hope to bypass challenging tasks of
+visual modeling from screen pixels. However, view hierarchies are not always
+available, and are often corrupted with missing object descriptions or
+misaligned structure information. As a result, despite the use of view
+hierarchies could offer short-term gains, it may ultimately hinder the
+applicability and performance of the model. In this paper, we propose
+Spotlight, a vision-only approach for mobile UI understanding. Specifically, we
+enhance a vision-language model that only takes the screenshot of the UI and a
+region of interest on the screen -- the focus -- as the input. This general
+architecture of Spotlight is easily scalable and capable of performing a range
+of UI modeling tasks. Our experiments show that our model establishes SoTA
+results on several representative UI tasks and outperforms previous methods
+that use both screenshots and view hierarchies as inputs. Furthermore, we
+explore multi-task learning and few-shot prompting capacities of the proposed
+models, demonstrating promising results in the multi-task learning direction.
+"
+Grounding Language with Visual Affordances over Unstructured Data,Oier Mees,http://arxiv.org/pdf/2210.01911v3.pdf,2022-10-04,"['cs.ro', 'cs.ai', 'cs.cl', 'cs.cv', 'cs.lg']",2210.01911v3.pdf,"  Recent works have shown that Large Language Models (LLMs) can be applied to
+ground natural language to a wide variety of robot skills. However, in
+practice, learning multi-task, language-conditioned robotic skills typically
+requires large-scale data collection and frequent human intervention to reset
+the environment or help correcting the current policies. In this work, we
+propose a novel approach to efficiently learn general-purpose
+language-conditioned robot skills from unstructured, offline and reset-free
+data in the real world by exploiting a self-supervised visuo-lingual affordance
+model, which requires annotating as little as 1% of the total data with
+language. We evaluate our method in extensive experiments both in simulated and
+real-world robotic tasks, achieving state-of-the-art performance on the
+challenging CALVIN benchmark and learning over 25 distinct visuomotor
+manipulation tasks with a single policy in the real world. We find that when
+paired with LLMs to break down abstract natural language instructions into
+subgoals via few-shot prompting, our method is capable of completing
+long-horizon, multi-tier tasks in the real world, while requiring an order of
+magnitude less data than previous approaches. Code and videos are available at
+http://hulc2.cs.uni-freiburg.de
+"
+MAPL: Parameter-Efficient Adaptation of Unimodal Pre-Trained Models for  Vision-Language Few-Shot Prompting,Oscar Mañas,http://arxiv.org/pdf/2210.07179v2.pdf,2022-10-13,"['cs.cv', 'cs.ai', 'cs.cl', 'cs.lg']",2210.07179v2.pdf,"  Large pre-trained models have proved to be remarkable zero- and
+(prompt-based) few-shot learners in unimodal vision and language tasks. We
+propose MAPL, a simple and parameter-efficient method that reuses frozen
+pre-trained unimodal models and leverages their strong generalization
+capabilities in multimodal vision-language (VL) settings. MAPL learns a
+lightweight mapping between the representation spaces of unimodal models using
+aligned image-text data, and can generalize to unseen VL tasks from just a few
+in-context examples. The small number of trainable parameters makes MAPL
+effective at low-data and in-domain learning. Moreover, MAPL's modularity
+enables easy extension to other pre-trained models. Extensive experiments on
+several visual question answering and image captioning benchmarks show that
+MAPL achieves superior or competitive performance compared to similar methods
+while training orders of magnitude fewer parameters. MAPL can be trained in
+just a few hours using modest computational resources and public datasets. We
+release our code and pre-trained model weights at
+https://github.com/mair-lab/mapl.
+"
+Model ensemble instead of prompt fusion: a sample-specific knowledge  transfer method for few-shot prompt tuning,Xiangyu Peng,http://arxiv.org/pdf/2210.12587v3.pdf,2022-10-23,['cs.cl'],2210.12587v3.pdf,"  Prompt tuning approaches, which learn task-specific soft prompts for a
+downstream task conditioning on frozen pre-trained models, have attracted
+growing interest due to its parameter efficiency. With large language models
+and sufficient training data, prompt tuning performs comparably to full-model
+tuning. However, with limited training samples in few-shot settings, prompt
+tuning fails to match the performance of full-model fine-tuning. In this work,
+we focus on improving the few-shot performance of prompt tuning by transferring
+knowledge from soft prompts of source tasks. Recognizing the good
+generalization capabilities of ensemble methods in low-data regime, we first
+experiment and show that a simple ensemble of model predictions based on
+different source prompts, outperforms existing multi-prompt knowledge transfer
+approaches such as source prompt fusion in the few-shot setting. Motivated by
+this observation, we further investigate model ensembles and propose
+Sample-specific Ensemble of Source Models (SESoM). SESoM learns to adjust the
+contribution of each source model for each target sample separately when
+ensembling source model outputs. Through this way, SESoM inherits the superior
+generalization of model ensemble approaches and simultaneously captures the
+sample-specific competence of each source prompt. We conduct experiments across
+a diverse set of eight NLP tasks using models of different scales (T5-{base,
+large, XL}) and find that SESoM consistently outperforms the existing models of
+the same as well as larger parametric scale by a large margin.
+"
+Are Hard Examples also Harder to Explain? A Study with Human and  Model-Generated Explanations,Swarnadeep Saha,http://arxiv.org/pdf/2211.07517v1.pdf,2022-11-14,"['cs.cl', 'cs.ai']",2211.07517v1.pdf,"  Recent work on explainable NLP has shown that few-shot prompting can enable
+large pretrained language models (LLMs) to generate grammatical and factual
+natural language explanations for data labels. In this work, we study the
+connection between explainability and sample hardness by investigating the
+following research question - ""Are LLMs and humans equally good at explaining
+data labels for both easy and hard samples?"" We answer this question by first
+collecting human-written explanations in the form of generalizable commonsense
+rules on the task of Winograd Schema Challenge (Winogrande dataset). We compare
+these explanations with those generated by GPT-3 while varying the hardness of
+the test samples as well as the in-context samples. We observe that (1) GPT-3
+explanations are as grammatical as human explanations regardless of the
+hardness of the test samples, (2) for easy examples, GPT-3 generates highly
+supportive explanations but human explanations are more generalizable, and (3)
+for hard examples, human explanations are significantly better than GPT-3
+explanations both in terms of label-supportiveness and generalizability
+judgements. We also find that hardness of the in-context examples impacts the
+quality of GPT-3 explanations. Finally, we show that the supportiveness and
+generalizability aspects of human explanations are also impacted by sample
+hardness, although by a much smaller margin than models. Supporting code and
+data are available at https://github.com/swarnaHub/ExplanationHardness
+"
+Crowd Score: A Method for the Evaluation of Jokes using Large Language  Model AI Voters as Judges,Fabricio Goes,http://arxiv.org/pdf/2212.11214v1.pdf,2022-12-21,['cs.ai'],2212.11214v1.pdf,"  This paper presents the Crowd Score, a novel method to assess the funniness
+of jokes using large language models (LLMs) as AI judges. Our method relies on
+inducing different personalities into the LLM and aggregating the votes of the
+AI judges into a single score to rate jokes. We validate the votes using an
+auditing technique that checks if the explanation for a particular vote is
+reasonable using the LLM. We tested our methodology on 52 jokes in a crowd of
+four AI voters with different humour types: affiliative, self-enhancing,
+aggressive and self-defeating. Our results show that few-shot prompting leads
+to better results than zero-shot for the voting question. Personality induction
+showed that aggressive and self-defeating voters are significantly more
+inclined to find more jokes funny of a set of aggressive/self-defeating jokes
+than the affiliative and self-enhancing voters. The Crowd Score follows the
+same trend as human judges by assigning higher scores to jokes that are also
+considered funnier by human judges. We believe that our methodology could be
+applied to other creative domains such as story, poetry, slogans, etc. It could
+both help the adoption of a flexible and accurate standard approach to compare
+different work in the CC community under a common metric and by minimizing
+human participation in assessing creative artefacts, it could accelerate the
+prototyping of creative artefacts and reduce the cost of hiring human
+participants to rate creative artefacts.
+"
+CodeLMSec Benchmark: Systematically Evaluating and Finding Security  Vulnerabilities in Black-Box Code Language Models,Hossein Hajipour,http://arxiv.org/pdf/2302.04012v2.pdf,2023-02-08,"['cs.cr', 'cs.ai', 'cs.cl', 'cs.lg', 'cs.se']",2302.04012v2.pdf,"  Large language models (LLMs) for automatic code generation have achieved
+breakthroughs in several programming tasks. Their advances in competition-level
+programming problems have made them an essential pillar of AI-assisted pair
+programming, and tools such as GitHub Copilot have emerged as part of the daily
+programming workflow used by millions of developers. The training data for
+these models is usually collected from the Internet (e.g., from open-source
+repositories) and is likely to contain faults and security vulnerabilities.
+This unsanitized training data can cause the language models to learn these
+vulnerabilities and propagate them during the code generation procedure. While
+these models have been extensively assessed for their ability to produce
+functionally correct programs, there remains a lack of comprehensive
+investigations and benchmarks addressing the security aspects of these models.
+  In this work, we propose a method to systematically study the security issues
+of code language models to assess their susceptibility to generating vulnerable
+code. To this end, we introduce the first approach to automatically find
+generated code that contains vulnerabilities in black-box code generation
+models. To achieve this, we present an approach to approximate inversion of the
+black-box code generation models based on few-shot prompting. We evaluate the
+effectiveness of our approach by examining code language models in generating
+high-risk security weaknesses. Furthermore, we establish a collection of
+diverse non-secure prompts for various vulnerability scenarios using our
+method. This dataset forms a benchmark for evaluating and comparing the
+security weaknesses in code language models.
+"
+ART: Automatic multi-step reasoning and tool-use for large language  models,Bhargavi Paranjape,http://arxiv.org/pdf/2303.09014v1.pdf,2023-03-16,['cs.cl'],2303.09014v1.pdf,"  Large language models (LLMs) can perform complex reasoning in few- and
+zero-shot settings by generating intermediate chain of thought (CoT) reasoning
+steps. Further, each reasoning step can rely on external tools to support
+computation beyond the core LLM capabilities (e.g. search/running code). Prior
+work on CoT prompting and tool use typically requires hand-crafting
+task-specific demonstrations and carefully scripted interleaving of model
+generations with tool use. We introduce Automatic Reasoning and Tool-use (ART),
+a framework that uses frozen LLMs to automatically generate intermediate
+reasoning steps as a program. Given a new task to solve, ART selects
+demonstrations of multi-step reasoning and tool use from a task library. At
+test time, ART seamlessly pauses generation whenever external tools are called,
+and integrates their output before resuming generation. ART achieves a
+substantial improvement over few-shot prompting and automatic CoT on unseen
+tasks in the BigBench and MMLU benchmarks, and matches performance of
+hand-crafted CoT prompts on a majority of these tasks. ART is also extensible,
+and makes it easy for humans to improve performance by correcting errors in
+task-specific programs or incorporating new tools, which we demonstrate by
+drastically improving performance on select tasks with minimal human
+intervention.
+"
+Fairness-guided Few-shot Prompting for Large Language Models,Huan Ma,http://arxiv.org/pdf/2303.13217v3.pdf,2023-03-23,"['cs.cl', 'cs.ai']",2303.13217v3.pdf,"  Large language models have demonstrated surprising ability to perform
+in-context learning, i.e., these models can be directly applied to solve
+numerous downstream tasks by conditioning on a prompt constructed by a few
+input-output examples. However, prior research has shown that in-context
+learning can suffer from high instability due to variations in training
+examples, example order, and prompt formats. Therefore, the construction of an
+appropriate prompt is essential for improving the performance of in-context
+learning. In this paper, we revisit this problem from the view of predictive
+bias. Specifically, we introduce a metric to evaluate the predictive bias of a
+fixed prompt against labels or a given attributes. Then we empirically show
+that prompts with higher bias always lead to unsatisfactory predictive quality.
+Based on this observation, we propose a novel search strategy based on the
+greedy search to identify the near-optimal prompt for improving the performance
+of in-context learning. We perform comprehensive experiments with
+state-of-the-art mainstream models such as GPT-3 on various downstream tasks.
+Our results indicate that our method can enhance the model's in-context
+learning performance in an effective and interpretable manner.
+"
+Is ChatGPT a Good Recommender? A Preliminary Study,Junling Liu,http://arxiv.org/pdf/2304.10149v3.pdf,2023-04-20,['cs.ir'],2304.10149v3.pdf,"  Recommendation systems have witnessed significant advancements and have been
+widely used over the past decades. However, most traditional recommendation
+methods are task-specific and therefore lack efficient generalization ability.
+Recently, the emergence of ChatGPT has significantly advanced NLP tasks by
+enhancing the capabilities of conversational models. Nonetheless, the
+application of ChatGPT in the recommendation domain has not been thoroughly
+investigated. In this paper, we employ ChatGPT as a general-purpose
+recommendation model to explore its potential for transferring extensive
+linguistic and world knowledge acquired from large-scale corpora to
+recommendation scenarios. Specifically, we design a set of prompts and evaluate
+ChatGPT's performance on five recommendation scenarios. Unlike traditional
+recommendation methods, we do not fine-tune ChatGPT during the entire
+evaluation process, relying only on the prompts themselves to convert
+recommendation tasks into natural language tasks. Further, we explore the use
+of few-shot prompting to inject interaction information that contains user
+potential interest to help ChatGPT better understand user needs and interests.
+Comprehensive experimental results on Amazon Beauty dataset show that ChatGPT
+has achieved promising results in certain tasks and is capable of reaching the
+baseline level in others. We conduct human evaluations on two
+explainability-oriented tasks to more accurately evaluate the quality of
+contents generated by different models. And the human evaluations show ChatGPT
+can truly understand the provided information and generate clearer and more
+reasonable results. We hope that our study can inspire researchers to further
+explore the potential of language models like ChatGPT to improve recommendation
+performance and contribute to the advancement of the recommendation systems
+field.
+"
+Language Models Don't Always Say What They Think: Unfaithful  Explanations in Chain-of-Thought Prompting,Miles Turpin,http://arxiv.org/pdf/2305.04388v1.pdf,2023-05-07,"['cs.cl', 'cs.ai']",2305.04388v1.pdf,"  Large Language Models (LLMs) can achieve strong performance on many tasks by
+producing step-by-step reasoning before giving a final output, often referred
+to as chain-of-thought reasoning (CoT). It is tempting to interpret these CoT
+explanations as the LLM's process for solving a task. However, we find that CoT
+explanations can systematically misrepresent the true reason for a model's
+prediction. We demonstrate that CoT explanations can be heavily influenced by
+adding biasing features to model inputs -- e.g., by reordering the
+multiple-choice options in a few-shot prompt to make the answer always ""(A)"" --
+which models systematically fail to mention in their explanations. When we bias
+models toward incorrect answers, they frequently generate CoT explanations
+supporting those answers. This causes accuracy to drop by as much as 36% on a
+suite of 13 tasks from BIG-Bench Hard, when testing with GPT-3.5 from OpenAI
+and Claude 1.0 from Anthropic. On a social-bias task, model explanations
+justify giving answers in line with stereotypes without mentioning the
+influence of these social biases. Our findings indicate that CoT explanations
+can be plausible yet misleading, which risks increasing our trust in LLMs
+without guaranteeing their safety. CoT is promising for explainability, but our
+results highlight the need for targeted efforts to evaluate and improve
+explanation faithfulness.
+"
+Skill-Based Few-Shot Selection for In-Context Learning,Shengnan An,http://arxiv.org/pdf/2305.14210v2.pdf,2023-05-23,"['cs.cl', 'cs.ai']",2305.14210v2.pdf,"  In-context learning is the paradigm that adapts large language models to
+downstream tasks by providing a few examples. Few-shot selection -- selecting
+appropriate examples for each test instance separately -- is important for
+in-context learning. In this paper, we propose Skill-KNN, a skill-based
+few-shot selection method for in-context learning. The key advantages of
+Skill-KNN include: (1) it addresses the problem that existing methods based on
+pre-trained embeddings can be easily biased by surface natural language
+features that are not important for the target task; (2) it does not require
+training or fine-tuning of any models, making it suitable for frequently
+expanding or changing example banks. The key insight is to optimize the inputs
+fed into the embedding model, rather than tuning the model itself. Technically,
+Skill-KNN generates the skill-based descriptions for each test case and
+candidate example by utilizing a pre-processing few-shot prompting, thus
+eliminating unimportant surface features. Experimental results across five
+cross-domain semantic parsing datasets and six backbone models show that
+Skill-KNN significantly outperforms existing methods.
+"
+USB: A Unified Summarization Benchmark Across Tasks and Domains,Kundan Krishna,http://arxiv.org/pdf/2305.14296v1.pdf,2023-05-23,"['cs.cl', 'cs.lg']",2305.14296v1.pdf,"  An abundance of datasets exist for training and evaluating models on the task
+of summary generation.However, these datasets are often derived heuristically,
+and lack sufficient annotations to support research into all aspects of
+summarization, such as evidence extraction and controllable summarization. We
+introduce a benchmark comprising 8 tasks that require multi-dimensional
+understanding of summarization, e.g., surfacing evidence for a summary,
+assessing its correctness, and gauging its relevance to different topics. We
+compare various methods on this benchmark and discover that on multiple tasks,
+moderately-sized fine-tuned models consistently outperform much larger few-shot
+prompted language models. For factuality related tasks, we also evaluate
+existing heuristics to create training data and find that training on them
+performs worse than training on $20\times$ less human-labeled data. Our
+benchmark consists of data from 6 different domains, allowing us to study
+cross-domain performance of trained models. We find that for some tasks, the
+amount of training data matters more than the domain where it comes from, while
+for other tasks training specifically on data from the target domain, even if
+limited, is more beneficial. Our work fulfills the need for a well-annotated
+summarization benchmark with diverse tasks, and provides useful insights about
+the impact of the quality, size and domain of training data.
+"
+Self-Polish: Enhance Reasoning in Large Language Models via Problem  Refinement,Zhiheng Xi,http://arxiv.org/pdf/2305.14497v1.pdf,2023-05-23,"['cs.cl', 'cs.ai']",2305.14497v1.pdf,"  Prompting methods such as Chain-of-Thought (CoT) have shed new light on
+enhancing the reasoning capabilities of large language models, and researchers
+have extensively explored the generation process of rationales and answers.
+However, they have overlooked the potential challenges posed by the poor
+quality of reasoning problems, which may influence the reasoning performance
+significantly. In this work, we propose Self-Polish (SP), a novel method that
+facilitates the model's problem-solving process by prompting them to
+progressively refine the given problems to be more comprehensible and solvable.
+Specifically, the method teaches models to eliminate irrelevant information,
+rearrange the logic structure and organize local conditions into new ones
+parallelly. SP is orthogonal to all other prompting methods, making it
+convenient to integrate with state-of-the-art techniques for further
+improvement. We conduct thorough experiments on five benchmarks to illustrate
+the effectiveness of the proposed method. For example, with Text-davinci-003,
+our method boosts the performance of standard few-shot prompting by $8.0\%$ on
+GSM8K and $17.8\%$ on MultiArith; it also improves the performance of CoT by
+$6.0\%$ on GSM8K and $6.0\%$ on MathQA, respectively. Furthermore, our method
+also showcases impressive performance on robustness evaluation.
+"
+SciFix: Outperforming GPT3 on Scientific Factual Error Correction,Dhananjay Ashok,http://arxiv.org/pdf/2305.14707v2.pdf,2023-05-24,"['cs.cl', 'cs.ai', 'cs.lg']",2305.14707v2.pdf,"  Due to the prohibitively high cost of creating error correction datasets,
+most Factual Claim Correction methods rely on a powerful verification model to
+guide the correction process. This leads to a significant drop in performance
+in domains like scientific claims, where good verification models do not always
+exist. In this work, we introduce SciFix, a scientific claim correction system
+that does not require a verifier but can outperform existing methods by a
+considerable margin -- achieving correction accuracy of 84% on the SciFact
+dataset, 77% on SciFact-Open and 72% on the CovidFact dataset, compared to next
+best accuracies of 7%, 5%, and 15% on the same datasets respectively. Our
+method leverages the power of prompting with LLMs during training to create a
+richly annotated dataset that can be used for fully supervised training and
+regularization. We additionally use a claim-aware decoding procedure to improve
+the quality of corrected claims. Our method outperforms the very LLM that was
+used to generate the annotated dataset -- with Few-Shot Prompting on GPT3.5
+achieving 58%, 61%, and 64% on the respective datasets, a consistently lower
+correction accuracy, despite using nearly 800 times as many parameters as our
+model.
+"
+LaFTer: Label-Free Tuning of Zero-shot Classifier using Language and  Unlabeled Image Collections,M. Jehanzeb Mirza,http://arxiv.org/pdf/2305.18287v2.pdf,2023-05-29,"['cs.cv', 'cs.cl']",2305.18287v2.pdf,"  Recently, large-scale pre-trained Vision and Language (VL) models have set a
+new state-of-the-art (SOTA) in zero-shot visual classification enabling
+open-vocabulary recognition of potentially unlimited set of categories defined
+as simple language prompts. However, despite these great advances, the
+performance of these zeroshot classifiers still falls short of the results of
+dedicated (closed category set) classifiers trained with supervised fine
+tuning. In this paper we show, for the first time, how to reduce this gap
+without any labels and without any paired VL data, using an unlabeled image
+collection and a set of texts auto-generated using a Large Language Model (LLM)
+describing the categories of interest and effectively substituting labeled
+visual instances of those categories. Using our label-free approach, we are
+able to attain significant performance improvements over the zero-shot
+performance of the base VL model and other contemporary methods and baselines
+on a wide variety of datasets, demonstrating absolute improvement of up to
+11.7% (3.8% on average) in the label-free setting. Moreover, despite our
+approach being label-free, we observe 1.3% average gains over leading few-shot
+prompting baselines that do use 5-shot supervision.
+"
+"Better patching using LLM prompting, via Self-Consistency",Toufique Ahmed,http://arxiv.org/pdf/2306.00108v2.pdf,2023-05-31,"['cs.se', 'cs.lg']",2306.00108v2.pdf,"  Large Language models (LLMs) can be induced to solve non-trivial problems
+with ""few-shot"" prompts including illustrative problem-solution examples. Now
+if the few-shots also include ""chain of thought"" (CoT) explanations, which are
+of the form problem-explanation-solution, LLMs will generate a ""explained""
+solution, and perform even better. Recently an exciting, substantially better
+technique, self-consistency [1] (S-C) has emerged, based on the intuition that
+there are many plausible explanations for the right solution; when the LLM is
+sampled repeatedly to generate a pool of explanation-solution pairs, for a
+given problem, the most frequently occurring solutions in the pool (ignoring
+the explanations) tend to be even more likely to be correct! Unfortunately, the
+use of this highly-performant S-C (or even CoT) approach in software
+engineering settings is hampered by the lack of explanations; most software
+datasets lack explanations. In this paper, we describe an application of the
+S-C approach to program repair, using the commit log on the fix as the
+explanation, only in the illustrative few-shots. We achieve state-of-the art
+results, beating previous approaches to prompting-based program repair, on the
+MODIT dataset; we also find evidence suggesting that the correct commit
+messages are helping the LLM learn to produce better patches.
+"
+Large Language Models as Tax Attorneys: A Case Study in Legal  Capabilities Emergence,John J. Nay,http://arxiv.org/pdf/2306.07075v1.pdf,2023-06-12,"['cs.cl', 'cs.ai', 'cs.cy']",2306.07075v1.pdf,"  Better understanding of Large Language Models' (LLMs) legal analysis
+abilities can contribute to improving the efficiency of legal services,
+governing artificial intelligence, and leveraging LLMs to identify
+inconsistencies in law. This paper explores LLM capabilities in applying tax
+law. We choose this area of law because it has a structure that allows us to
+set up automated validation pipelines across thousands of examples, requires
+logical reasoning and maths skills, and enables us to test LLM capabilities in
+a manner relevant to real-world economic lives of citizens and companies. Our
+experiments demonstrate emerging legal understanding capabilities, with
+improved performance in each subsequent OpenAI model release. We experiment
+with retrieving and utilising the relevant legal authority to assess the impact
+of providing additional legal context to LLMs. Few-shot prompting, presenting
+examples of question-answer pairs, is also found to significantly enhance the
+performance of the most advanced model, GPT-4. The findings indicate that LLMs,
+particularly when combined with prompting enhancements and the correct legal
+texts, can perform at high levels of accuracy but not yet at expert tax lawyer
+levels. As LLMs continue to advance, their ability to reason about law
+autonomously could have significant implications for the legal profession and
+AI governance.
+"
+DIFFender: Diffusion-Based Adversarial Defense against Patch Attacks,Caixin Kang,http://arxiv.org/pdf/2306.09124v2.pdf,2023-06-15,"['cs.cv', 'cs.ai', 'cs.cr', 'cs.lg']",2306.09124v2.pdf,"  Adversarial attacks, particularly patch attacks, pose significant threats to
+the robustness and reliability of deep learning models. Developing reliable
+defenses against patch attacks is crucial for real-world applications, yet
+current research in this area is not satisfactory. In this paper, we propose
+DIFFender, a novel defense method that leverages a text-guided diffusion model
+to defend against adversarial patches. DIFFender includes two main stages:
+patch localization and patch restoration. In the localization stage, we find
+and exploit an intriguing property of the diffusion model to effectively
+identify the locations of adversarial patches. In the restoration stage, we
+employ the diffusion model to reconstruct the adversarial regions in the images
+while preserving the integrity of the visual content. Importantly, these two
+stages are carefully guided by a unified diffusion model, thus we can utilize
+the close interaction between them to improve the whole defense performance.
+Moreover, we propose a few-shot prompt-tuning algorithm to fine-tune the
+diffusion model, enabling the pre-trained diffusion model to easily adapt to
+the defense task. We conduct extensive experiments on the image classification
+and face recognition tasks, demonstrating that our proposed method exhibits
+superior robustness under strong adaptive attacks and generalizes well across
+various scenarios, diverse classifiers, and multiple patch attack methods.
+"
+Teaching Arithmetic to Small Transformers,Nayoung Lee,http://arxiv.org/pdf/2307.03381v1.pdf,2023-07-07,['cs.lg'],2307.03381v1.pdf,"  Large language models like GPT-4 exhibit emergent capabilities across
+general-purpose tasks, such as basic arithmetic, when trained on extensive text
+data, even though these tasks are not explicitly encoded by the unsupervised,
+next-token prediction objective. This study investigates how small
+transformers, trained from random initialization, can efficiently learn
+arithmetic operations such as addition, multiplication, and elementary
+functions like square root, using the next-token prediction objective. We first
+demonstrate that conventional training data is not the most effective for
+arithmetic learning, and simple formatting changes can significantly improve
+accuracy. This leads to sharp phase transitions as a function of training data
+scale, which, in some cases, can be explained through connections to low-rank
+matrix completion. Building on prior work, we then train on chain-of-thought
+style data that includes intermediate step results. Even in the complete
+absence of pretraining, this approach significantly and simultaneously improves
+accuracy, sample complexity, and convergence speed. We also study the interplay
+between arithmetic and text data during training and examine the effects of
+few-shot prompting, pretraining, and model scale. Additionally, we discuss
+length generalization challenges. Our work highlights the importance of
+high-quality, instructive data that considers the particular characteristics of
+the next-word prediction objective for rapidly eliciting arithmetic
+capabilities.
+"
+Controllable Generation of Dialogue Acts for Dialogue Systems via  Few-Shot Response Generation and Ranking,Angela Ramirez,http://arxiv.org/pdf/2307.14440v1.pdf,2023-07-26,['cs.cl'],2307.14440v1.pdf,"  Dialogue systems need to produce responses that realize multiple types of
+dialogue acts (DAs) with high semantic fidelity. In the past, natural language
+generators (NLGs) for dialogue were trained on large parallel corpora that map
+from a domain-specific DA and its semantic attributes to an output utterance.
+Recent work shows that pretrained language models (LLMs) offer new
+possibilities for controllable NLG using prompt-based learning. Here we develop
+a novel few-shot overgenerate-and-rank approach that achieves the controlled
+generation of DAs. We compare eight few-shot prompt styles that include a novel
+method of generating from textual pseudo-references using a textual style
+transfer approach. We develop six automatic ranking functions that identify
+outputs with both the correct DA and high semantic accuracy at generation time.
+We test our approach on three domains and four LLMs. To our knowledge, this is
+the first work on NLG for dialogue that automatically ranks outputs using both
+DA and attribute accuracy. For completeness, we compare our results to
+fine-tuned few-shot models trained with 5 to 100 instances per DA. Our results
+show that several prompt settings achieve perfect DA accuracy, and near perfect
+semantic accuracy (99.81%) and perform better than few-shot fine-tuning.
+"
+Contextual Biasing of Named-Entities with Large Language Models,Chuanneng Sun,http://arxiv.org/pdf/2309.00723v2.pdf,2023-09-01,"['cs.cl', 'cs.ai', 'cs.lg', 'cs.sd', 'eess.as', '68t10', 'i.2.7']",2309.00723v2.pdf,"  This paper studies contextual biasing with Large Language Models (LLMs),
+where during second-pass rescoring additional contextual information is
+provided to a LLM to boost Automatic Speech Recognition (ASR) performance. We
+propose to leverage prompts for a LLM without fine tuning during rescoring
+which incorporate a biasing list and few-shot examples to serve as additional
+information when calculating the score for the hypothesis. In addition to
+few-shot prompt learning, we propose multi-task training of the LLM to predict
+both the entity class and the next token. To improve the efficiency for
+contextual biasing and to avoid exceeding LLMs' maximum sequence lengths, we
+propose dynamic prompting, where we select the most likely class using the
+class tag prediction, and only use entities in this class as contexts for next
+token prediction. Word Error Rate (WER) evaluation is performed on i) an
+internal calling, messaging, and dictation dataset, and ii) the SLUE-Voxpopuli
+dataset. Results indicate that biasing lists and few-shot examples can achieve
+17.8% and 9.6% relative improvement compared to first pass ASR, and that
+multi-task training and dynamic prompting can achieve 20.0% and 11.3% relative
+WER improvement, respectively.
+"
+MindAgent: Emergent Gaming Interaction,Ran Gong,http://arxiv.org/pdf/2309.09971v2.pdf,2023-09-18,"['cs.ai', 'cs.hc', 'cs.ma']",2309.09971v2.pdf,"  Large Language Models (LLMs) have the capacity of performing complex
+scheduling in a multi-agent system and can coordinate these agents into
+completing sophisticated tasks that require extensive collaboration. However,
+despite the introduction of numerous gaming frameworks, the community has
+insufficient benchmarks towards building general multi-agents collaboration
+infrastructure that encompass both LLM and human-NPCs collaborations. In this
+work, we propose a novel infrastructure - MindAgent - to evaluate planning and
+coordination emergent capabilities for gaming interaction. In particular, our
+infrastructure leverages existing gaming framework, to i) require understanding
+of the coordinator for a multi-agent system, ii) collaborate with human players
+via un-finetuned proper instructions, and iii) establish an in-context learning
+on few-shot prompt with feedback. Furthermore, we introduce CUISINEWORLD, a new
+gaming scenario and related benchmark that dispatch a multi-agent collaboration
+efficiency and supervise multiple agents playing the game simultaneously. We
+conduct comprehensive evaluations with new auto-metric CoS for calculating the
+collaboration efficiency. Finally, our infrastructure can be deployed into
+real-world gaming scenarios in a customized VR version of CUISINEWORLD and
+adapted in existing broader Minecraft gaming domain. We hope our findings on
+LLMs and the new infrastructure for general-purpose scheduling and coordination
+can help shed light on how such skills can be obtained by learning from large
+language corpora.
+"
+DSPy: Compiling Declarative Language Model Calls into Self-Improving  Pipelines,Omar Khattab,http://arxiv.org/pdf/2310.03714v1.pdf,2023-10-05,"['cs.cl', 'cs.ai', 'cs.ir', 'cs.lg']",2310.03714v1.pdf,"  The ML community is rapidly exploring techniques for prompting language
+models (LMs) and for stacking them into pipelines that solve complex tasks.
+Unfortunately, existing LM pipelines are typically implemented using hard-coded
+""prompt templates"", i.e. lengthy strings discovered via trial and error. Toward
+a more systematic approach for developing and optimizing LM pipelines, we
+introduce DSPy, a programming model that abstracts LM pipelines as text
+transformation graphs, i.e. imperative computational graphs where LMs are
+invoked through declarative modules. DSPy modules are parameterized, meaning
+they can learn (by creating and collecting demonstrations) how to apply
+compositions of prompting, finetuning, augmentation, and reasoning techniques.
+We design a compiler that will optimize any DSPy pipeline to maximize a given
+metric. We conduct two case studies, showing that succinct DSPy programs can
+express and optimize sophisticated LM pipelines that reason about math word
+problems, tackle multi-hop retrieval, answer complex questions, and control
+agent loops. Within minutes of compiling, a few lines of DSPy allow GPT-3.5 and
+llama2-13b-chat to self-bootstrap pipelines that outperform standard few-shot
+prompting (generally by over 25% and 65%, respectively) and pipelines with
+expert-created demonstrations (by up to 5-46% and 16-40%, respectively). On top
+of that, DSPy programs compiled to open and relatively small LMs like
+770M-parameter T5 and llama2-13b-chat are competitive with approaches that rely
+on expert-written prompt chains for proprietary GPT-3.5. DSPy is available at
+https://github.com/stanfordnlp/dspy
+"
+InterroLang: Exploring NLP Models and Datasets through Dialogue-based  Explanations,Nils Feldhus,http://arxiv.org/pdf/2310.05592v2.pdf,2023-10-09,"['cs.cl', 'cs.ai', 'cs.hc']",2310.05592v2.pdf,"  While recently developed NLP explainability methods let us open the black box
+in various ways (Madsen et al., 2022), a missing ingredient in this endeavor is
+an interactive tool offering a conversational interface. Such a dialogue system
+can help users explore datasets and models with explanations in a
+contextualized manner, e.g. via clarification or follow-up questions, and
+through a natural language interface. We adapt the conversational explanation
+framework TalkToModel (Slack et al., 2022) to the NLP domain, add new
+NLP-specific operations such as free-text rationalization, and illustrate its
+generalizability on three NLP tasks (dialogue act classification, question
+answering, hate speech detection). To recognize user queries for explanations,
+we evaluate fine-tuned and few-shot prompting models and implement a novel
+Adapter-based approach. We then conduct two user studies on (1) the perceived
+correctness and helpfulness of the dialogues, and (2) the simulatability, i.e.
+how objectively helpful dialogical explanations are for humans in figuring out
+the model's predicted label when it's not shown. We found rationalization and
+feature attribution were helpful in explaining the model behavior. Moreover,
+users could more reliably predict the model outcome based on an explanation
+dialogue rather than one-off explanations.
+"
+FireAct: Toward Language Agent Fine-tuning,Baian Chen,http://arxiv.org/pdf/2310.05915v1.pdf,2023-10-09,"['cs.cl', 'cs.ai', 'cs.lg']",2310.05915v1.pdf,"  Recent efforts have augmented language models (LMs) with external tools or
+environments, leading to the development of language agents that can reason and
+act. However, most of these agents rely on few-shot prompting techniques with
+off-the-shelf LMs. In this paper, we investigate and argue for the overlooked
+direction of fine-tuning LMs to obtain language agents. Using a setup of
+question answering (QA) with a Google search API, we explore a variety of base
+LMs, prompting methods, fine-tuning data, and QA tasks, and find language
+agents are consistently improved after fine-tuning their backbone LMs. For
+example, fine-tuning Llama2-7B with 500 agent trajectories generated by GPT-4
+leads to a 77% HotpotQA performance increase. Furthermore, we propose FireAct,
+a novel approach to fine-tuning LMs with trajectories from multiple tasks and
+prompting methods, and show having more diverse fine-tuning data can further
+improve agents. Along with other findings regarding scaling effects,
+robustness, generalization, efficiency and cost, our work establishes
+comprehensive benefits of fine-tuning LMs for agents, and provides an initial
+set of experimental designs, insights, as well as open questions toward
+language agent fine-tuning.
+"
+Steering Large Language Models for Machine Translation with Finetuning  and In-Context Learning,Duarte M. Alves,http://arxiv.org/pdf/2310.13448v1.pdf,2023-10-20,['cs.cl'],2310.13448v1.pdf,"  Large language models (LLMs) are a promising avenue for machine translation
+(MT). However, current LLM-based MT systems are brittle: their effectiveness
+highly depends on the choice of few-shot examples and they often require extra
+post-processing due to overgeneration. Alternatives such as finetuning on
+translation instructions are computationally expensive and may weaken
+in-context learning capabilities, due to overspecialization. In this paper, we
+provide a closer look at this problem. We start by showing that adapter-based
+finetuning with LoRA matches the performance of traditional finetuning while
+reducing the number of training parameters by a factor of 50. This method also
+outperforms few-shot prompting and eliminates the need for post-processing or
+in-context examples. However, we show that finetuning generally degrades
+few-shot performance, hindering adaptation capabilities. Finally, to obtain the
+best of both worlds, we propose a simple approach that incorporates few-shot
+examples during finetuning. Experiments on 10 language pairs show that our
+proposed approach recovers the original few-shot capabilities while keeping the
+added benefits of finetuning.
+"
+On Bilingual Lexicon Induction with Large Language Models,Yaoyiran Li,http://arxiv.org/pdf/2310.13995v1.pdf,2023-10-21,"['cs.cl', 'cs.ai', 'cs.ir', 'cs.lg']",2310.13995v1.pdf,"  Bilingual Lexicon Induction (BLI) is a core task in multilingual NLP that
+still, to a large extent, relies on calculating cross-lingual word
+representations. Inspired by the global paradigm shift in NLP towards Large
+Language Models (LLMs), we examine the potential of the latest generation of
+LLMs for the development of bilingual lexicons. We ask the following research
+question: Is it possible to prompt and fine-tune multilingual LLMs (mLLMs) for
+BLI, and how does this approach compare against and complement current BLI
+approaches? To this end, we systematically study 1) zero-shot prompting for
+unsupervised BLI and 2) few-shot in-context prompting with a set of seed
+translation pairs, both without any LLM fine-tuning, as well as 3) standard
+BLI-oriented fine-tuning of smaller LLMs. We experiment with 18 open-source
+text-to-text mLLMs of different sizes (from 0.3B to 13B parameters) on two
+standard BLI benchmarks covering a range of typologically diverse languages.
+Our work is the first to demonstrate strong BLI capabilities of text-to-text
+mLLMs. The results reveal that few-shot prompting with in-context examples from
+nearest neighbours achieves the best performance, establishing new
+state-of-the-art BLI scores for many language pairs. We also conduct a series
+of in-depth analyses and ablation studies, providing more insights on BLI with
+(m)LLMs, also along with their limitations.
+"
+An Early Evaluation of GPT-4V(ision),Yang Wu,http://arxiv.org/pdf/2310.16534v1.pdf,2023-10-25,"['cs.cl', 'cs.cv']",2310.16534v1.pdf,"  In this paper, we evaluate different abilities of GPT-4V including visual
+understanding, language understanding, visual puzzle solving, and understanding
+of other modalities such as depth, thermal, video, and audio. To estimate
+GPT-4V's performance, we manually construct 656 test instances and carefully
+evaluate the results of GPT-4V. The highlights of our findings are as follows:
+(1) GPT-4V exhibits impressive performance on English visual-centric benchmarks
+but fails to recognize simple Chinese texts in the images; (2) GPT-4V shows
+inconsistent refusal behavior when answering questions related to sensitive
+traits such as gender, race, and age; (3) GPT-4V obtains worse results than
+GPT-4 (API) on language understanding tasks including general language
+understanding benchmarks and visual commonsense knowledge evaluation
+benchmarks; (4) Few-shot prompting can improve GPT-4V's performance on both
+visual understanding and language understanding; (5) GPT-4V struggles to find
+the nuances between two similar images and solve the easy math picture puzzles;
+(6) GPT-4V shows non-trivial performance on the tasks of similar modalities to
+image, such as video and thermal. Our experimental results reveal the ability
+and limitations of GPT-4V and we hope our paper can provide some insights into
+the application and research of GPT-4V.
+"
+"""You Are An Expert Linguistic Annotator"": Limits of LLMs as Analyzers of  Abstract Meaning Representation",Allyson Ettinger,http://arxiv.org/pdf/2310.17793v1.pdf,2023-10-26,"['cs.cl', 'cs.ai']",2310.17793v1.pdf,"  Large language models (LLMs) show amazing proficiency and fluency in the use
+of language. Does this mean that they have also acquired insightful linguistic
+knowledge about the language, to an extent that they can serve as an ""expert
+linguistic annotator""? In this paper, we examine the successes and limitations
+of the GPT-3, ChatGPT, and GPT-4 models in analysis of sentence meaning
+structure, focusing on the Abstract Meaning Representation (AMR; Banarescu et
+al. 2013) parsing formalism, which provides rich graphical representations of
+sentence meaning structure while abstracting away from surface forms. We
+compare models' analysis of this semantic structure across two settings: 1)
+direct production of AMR parses based on zero- and few-shot prompts, and 2)
+indirect partial reconstruction of AMR via metalinguistic natural language
+queries (e.g., ""Identify the primary event of this sentence, and the predicate
+corresponding to that event.""). Across these settings, we find that models can
+reliably reproduce the basic format of AMR, and can often capture core event,
+argument, and modifier structure -- however, model outputs are prone to
+frequent and major errors, and holistic analysis of parse acceptability shows
+that even with few-shot demonstrations, models have virtually 0% success in
+producing fully accurate parses. Eliciting natural language responses produces
+similar patterns of errors. Overall, our findings indicate that these models
+out-of-the-box can capture aspects of semantic structure, but there remain key
+limitations in their ability to support fully accurate semantic analyses or
+parses.
+"
+Style-Aware Radiology Report Generation with RadGraph and Few-Shot  Prompting,Benjamin Yan,http://arxiv.org/pdf/2310.17811v2.pdf,2023-10-26,"['cs.ai', 'cs.cl']",2310.17811v2.pdf,"  Automatically generated reports from medical images promise to improve the
+workflow of radiologists. Existing methods consider an image-to-report modeling
+task by directly generating a fully-fledged report from an image. However, this
+conflates the content of the report (e.g., findings and their attributes) with
+its style (e.g., format and choice of words), which can lead to clinically
+inaccurate reports. To address this, we propose a two-step approach for
+radiology report generation. First, we extract the content from an image; then,
+we verbalize the extracted content into a report that matches the style of a
+specific radiologist. For this, we leverage RadGraph -- a graph representation
+of reports -- together with large language models (LLMs). In our quantitative
+evaluations, we find that our approach leads to beneficial performance. Our
+human evaluation with clinical raters highlights that the AI-generated reports
+are indistinguishably tailored to the style of individual radiologist despite
+leveraging only a few examples as context.
+"
+Multi-lingual Evaluation of Code Generation Models,Ben Athiwaratkun,http://arxiv.org/pdf/2210.14868v3.pdf,2022-10-26,"['cs.lg', 'cs.cl']",2210.14868v3.pdf,"  We present new benchmarks on evaluation code generation models: MBXP and
+Multilingual HumanEval, and MathQA-X. These datasets cover over 10 programming
+languages and are generated using a scalable conversion framework that
+transpiles prompts and test cases from the original Python datasets into the
+corresponding data in the target language. Using these benchmarks, we are able
+to assess the performance of code generation models in a multi-lingual fashion,
+and discovered generalization ability of language models on out-of-domain
+languages, advantages of multi-lingual models over mono-lingual, the ability of
+few-shot prompting to teach the model new languages, and zero-shot translation
+abilities even on mono-lingual settings. Furthermore, we use our code
+generation model to perform large-scale bootstrapping to obtain synthetic
+canonical solutions in several languages, which can be used for other
+code-related evaluations such as code insertion, robustness, or summarization
+tasks. Overall, our benchmarks represents a significant step towards a deeper
+understanding of language models' code generation abilities. We publicly
+release our code and datasets at https://github.com/amazon-research/mxeval.
+"
+PAL: Program-aided Language Models,Luyu Gao,http://arxiv.org/pdf/2211.10435v2.pdf,2022-11-18,"['cs.cl', 'cs.ai']",2211.10435v2.pdf,"  Large language models (LLMs) have recently demonstrated an impressive ability
+to perform arithmetic and symbolic reasoning tasks, when provided with a few
+examples at test time (""few-shot prompting""). Much of this success can be
+attributed to prompting methods such as ""chain-of-thought'', which employ LLMs
+for both understanding the problem description by decomposing it into steps, as
+well as solving each step of the problem. While LLMs seem to be adept at this
+sort of step-by-step decomposition, LLMs often make logical and arithmetic
+mistakes in the solution part, even when the problem is decomposed correctly.
+In this paper, we present Program-Aided Language models (PAL): a novel approach
+that uses the LLM to read natural language problems and generate programs as
+the intermediate reasoning steps, but offloads the solution step to a runtime
+such as a Python interpreter. With PAL, decomposing the natural language
+problem into runnable steps remains the only learning task for the LLM, while
+solving is delegated to the interpreter. We demonstrate this synergy between a
+neural LLM and a symbolic interpreter across 13 mathematical, symbolic, and
+algorithmic reasoning tasks from BIG-Bench Hard and other benchmarks. In all
+these natural language reasoning tasks, generating code using an LLM and
+reasoning using a Python interpreter leads to more accurate results than much
+larger models. For example, PAL using Codex achieves state-of-the-art few-shot
+accuracy on the GSM8K benchmark of math word problems, surpassing PaLM-540B
+which uses chain-of-thought by absolute 15% top-1. Our code and data are
+publicly available at http://reasonwithpal.com/ .
+"
+Learning Performance-Improving Code Edits,Alexander Shypula,http://arxiv.org/pdf/2302.07867v4.pdf,2023-02-15,"['cs.se', 'cs.ai', 'cs.lg', 'cs.pf']",2302.07867v4.pdf,"  With the waning of Moore's law, optimizing program performance has become a
+major focus of software research. However, high-level optimizations such as API
+and algorithm changes remain elusive due to the difficulty of understanding the
+semantics of code. Simultaneously, pretrained large language models (LLMs) have
+demonstrated strong capabilities at solving a wide range of programming tasks.
+To that end, we introduce a framework for adapting LLMs to high-level program
+optimization. First, we curate a dataset of performance-improving edits made by
+human programmers of over 77K competitive C++ programming submission pairs,
+accompanied by extensive unit tests. A major challenge is the significant
+variability of measuring performance on commodity hardware, which can lead to
+spurious ""improvements"". To isolate and reliably evaluate the impact of program
+optimizations, we design an environment based on the gem5 full system
+simulator, the de facto simulator used in academia and industry. Next, we
+propose a broad range of adaptation strategies for code optimization; for
+prompting, these include retrieval-based few-shot prompting and
+chain-of-thought, and for finetuning, these include performance-conditioned
+generation and synthetic data augmentation based on self-play. A combination of
+these techniques achieves an average speedup of 5.65X on CodeLlama-13B and
+6.86X on GPT-3.5, surpassing the best human performance (4.06X). We find our
+proposed performance-conditioned generation is particularly effective at
+improving performance as well as increasing the fraction of optimized programs.
+"
+Large Language Models for User Interest Journeys,Konstantina Christakopoulou,http://arxiv.org/pdf/2305.15498v1.pdf,2023-05-24,"['cs.cl', 'cs.ai', 'cs.ir']",2305.15498v1.pdf,"  Large language models (LLMs) have shown impressive capabilities in natural
+language understanding and generation. Their potential for deeper user
+understanding and improved personalized user experience on recommendation
+platforms is, however, largely untapped. This paper aims to address this gap.
+Recommender systems today capture users' interests through encoding their
+historical activities on the platforms. The generated user representations are
+hard to examine or interpret. On the other hand, if we were to ask people about
+interests they pursue in their life, they might talk about their hobbies, like
+I just started learning the ukulele, or their relaxation routines, e.g., I like
+to watch Saturday Night Live, or I want to plant a vertical garden. We argue,
+and demonstrate through extensive experiments, that LLMs as foundation models
+can reason through user activities, and describe their interests in nuanced and
+interesting ways, similar to how a human would.
+  We define interest journeys as the persistent and overarching user interests,
+in other words, the non-transient ones. These are the interests that we believe
+will benefit most from the nuanced and personalized descriptions. We introduce
+a framework in which we first perform personalized extraction of interest
+journeys, and then summarize the extracted journeys via LLMs, using techniques
+like few-shot prompting, prompt-tuning and fine-tuning. Together, our results
+in prompting LLMs to name extracted user journeys in a large-scale industrial
+platform demonstrate great potential of these models in providing deeper, more
+interpretable, and controllable user understanding. We believe LLM powered user
+understanding can be a stepping stone to entirely new user experiences on
+recommendation platforms that are journey-aware, assistive, and enabling
+frictionless conversation down the line.
+"
+Passive learning of active causal strategies in agents and language  models,Andrew Kyle Lampinen,http://arxiv.org/pdf/2305.16183v2.pdf,2023-05-25,"['cs.lg', 'cs.ai', 'cs.cl']",2305.16183v2.pdf,"  What can be learned about causality and experimentation from passive data?
+This question is salient given recent successes of passively-trained language
+models in interactive domains such as tool use. Passive learning is inherently
+limited. However, we show that purely passive learning can in fact allow an
+agent to learn generalizable strategies for determining and using causal
+structures, as long as the agent can intervene at test time. We formally
+illustrate that learning a strategy of first experimenting, then seeking goals,
+can allow generalization from passive learning in principle. We then show
+empirically that agents trained via imitation on expert data can indeed
+generalize at test time to infer and use causal links which are never present
+in the training data; these agents can also generalize experimentation
+strategies to novel variable sets never observed in training. We then show that
+strategies for causal intervention and exploitation can be generalized from
+passive data even in a more complex environment with high-dimensional
+observations, with the support of natural language explanations. Explanations
+can even allow passive learners to generalize out-of-distribution from
+perfectly-confounded training data. Finally, we show that language models,
+trained only on passive next-word prediction, can generalize causal
+intervention strategies from a few-shot prompt containing examples of
+experimentation, together with explanations and reasoning. These results
+highlight the surprising power of passive learning of active causal strategies,
+and may help to understand the behaviors and capabilities of language models.
+"
+Tool Documentation Enables Zero-Shot Tool-Usage with Large Language  Models,Cheng-Yu Hsieh,http://arxiv.org/pdf/2308.00675v1.pdf,2023-08-01,"['cs.cl', 'cs.ai', 'cs.cv', 'cs.lg']",2308.00675v1.pdf,"  Today, large language models (LLMs) are taught to use new tools by providing
+a few demonstrations of the tool's usage. Unfortunately, demonstrations are
+hard to acquire, and can result in undesirable biased usage if the wrong
+demonstration is chosen. Even in the rare scenario that demonstrations are
+readily available, there is no principled selection protocol to determine how
+many and which ones to provide. As tasks grow more complex, the selection
+search grows combinatorially and invariably becomes intractable. Our work
+provides an alternative to demonstrations: tool documentation. We advocate the
+use of tool documentation, descriptions for the individual tool usage, over
+demonstrations. We substantiate our claim through three main empirical findings
+on 6 tasks across both vision and language modalities. First, on existing
+benchmarks, zero-shot prompts with only tool documentation are sufficient for
+eliciting proper tool usage, achieving performance on par with few-shot
+prompts. Second, on a newly collected realistic tool-use dataset with hundreds
+of available tool APIs, we show that tool documentation is significantly more
+valuable than demonstrations, with zero-shot documentation significantly
+outperforming few-shot without documentation. Third, we highlight the benefits
+of tool documentations by tackling image generation and video tracking using
+just-released unseen state-of-the-art models as tools. Finally, we highlight
+the possibility of using tool documentation to automatically enable new
+applications: by using nothing more than the documentation of GroundingDino,
+Stable Diffusion, XMem, and SAM, LLMs can re-invent the functionalities of the
+just-released Grounded-SAM and Track Anything models.
+"
+MathAttack: Attacking Large Language Models Towards Math Solving Ability,Zihao Zhou,http://arxiv.org/pdf/2309.01686v1.pdf,2023-09-04,['cs.cl'],2309.01686v1.pdf,"  With the boom of Large Language Models (LLMs), the research of solving Math
+Word Problem (MWP) has recently made great progress. However, there are few
+studies to examine the security of LLMs in math solving ability. Instead of
+attacking prompts in the use of LLMs, we propose a MathAttack model to attack
+MWP samples which are closer to the essence of security in solving math
+problems. Compared to traditional text adversarial attack, it is essential to
+preserve the mathematical logic of original MWPs during the attacking. To this
+end, we propose logical entity recognition to identify logical entries which
+are then frozen. Subsequently, the remaining text are attacked by adopting a
+word-level attacker. Furthermore, we propose a new dataset RobustMath to
+evaluate the robustness of LLMs in math solving ability. Extensive experiments
+on our RobustMath and two another math benchmark datasets GSM8K and MultiAirth
+show that MathAttack could effectively attack the math solving ability of LLMs.
+In the experiments, we observe that (1) Our adversarial samples from
+higher-accuracy LLMs are also effective for attacking LLMs with lower accuracy
+(e.g., transfer from larger to smaller-size LLMs, or from few-shot to zero-shot
+prompts); (2) Complex MWPs (such as more solving steps, longer text, more
+numbers) are more vulnerable to attack; (3) We can improve the robustness of
+LLMs by using our adversarial samples in few-shot prompts. Finally, we hope our
+practice and observation can serve as an important attempt towards enhancing
+the robustness of LLMs in math solving ability. We will release our code and
+dataset.
+"
+MentaLLaMA: Interpretable Mental Health Analysis on Social Media with  Large Language Models,Kailai Yang,http://arxiv.org/pdf/2309.13567v2.pdf,2023-09-24,['cs.cl'],2309.13567v2.pdf,"  With the development of web technology, social media texts are becoming a
+rich source for automatic mental health analysis. As traditional discriminative
+methods bear the problem of low interpretability, the recent large language
+models have been explored for interpretable mental health analysis on social
+media, which aims to provide detailed explanations along with predictions. The
+results show that ChatGPT can generate approaching-human explanations for its
+correct classifications. However, LLMs still achieve unsatisfactory
+classification performance in a zero-shot/few-shot manner. Domain-specific
+finetuning is an effective solution, but faces 2 challenges: 1) lack of
+high-quality training data. 2) no open-source LLMs for interpretable mental
+health analysis were released to lower the finetuning cost. To alleviate these
+problems, we build the first multi-task and multi-source interpretable mental
+health instruction (IMHI) dataset on social media, with 105K data samples. The
+raw social media data are collected from 10 existing sources covering 8 mental
+health analysis tasks. We use expert-written few-shot prompts and collected
+labels to prompt ChatGPT and obtain explanations from its responses. To ensure
+the reliability of the explanations, we perform strict automatic and human
+evaluations on the correctness, consistency, and quality of generated data.
+Based on the IMHI dataset and LLaMA2 foundation models, we train MentalLLaMA,
+the first open-source LLM series for interpretable mental health analysis with
+instruction-following capability. We also evaluate the performance of
+MentalLLaMA on the IMHI evaluation benchmark with 10 test sets, where their
+correctness for making predictions and the quality of explanations are
+examined. The results show that MentalLLaMA approaches state-of-the-art
+discriminative methods in correctness and generates high-quality explanations.
+"
+FreshLLMs: Refreshing Large Language Models with Search Engine  Augmentation,Tu Vu,http://arxiv.org/pdf/2310.03214v1.pdf,2023-10-05,['cs.cl'],2310.03214v1.pdf,"  Most large language models (LLMs) are trained once and never updated; thus,
+they lack the ability to dynamically adapt to our ever-changing world. In this
+work, we perform a detailed study of the factuality of LLM-generated text in
+the context of answering questions that test current world knowledge.
+Specifically, we introduce FreshQA, a novel dynamic QA benchmark encompassing a
+diverse range of question and answer types, including questions that require
+fast-changing world knowledge as well as questions with false premises that
+need to be debunked. We benchmark a diverse array of both closed and
+open-source LLMs under a two-mode evaluation procedure that allows us to
+measure both correctness and hallucination. Through human evaluations involving
+more than 50K judgments, we shed light on limitations of these models and
+demonstrate significant room for improvement: for instance, all models
+(regardless of model size) struggle on questions that involve fast-changing
+knowledge and false premises. Motivated by these results, we present
+FreshPrompt, a simple few-shot prompting method that substantially boosts the
+performance of an LLM on FreshQA by incorporating relevant and up-to-date
+information retrieved from a search engine into the prompt. Our experiments
+show that FreshPrompt outperforms both competing search engine-augmented
+prompting methods such as Self-Ask (Press et al., 2022) as well as commercial
+systems such as Perplexity.AI. Further analysis of FreshPrompt reveals that
+both the number of retrieved evidences and their order play a key role in
+influencing the correctness of LLM-generated answers. Additionally, instructing
+the LLM to generate concise and direct answers helps reduce hallucination
+compared to encouraging more verbose answers. To facilitate future work, we
+release FreshQA at github.com/freshllms/freshqa and commit to updating it at
+regular intervals.
+"
+A Comprehensive Survey on Pretrained Foundation Models: A History from  BERT to ChatGPT,Ce Zhou,http://arxiv.org/pdf/2302.09419v3.pdf,2023-02-18,"['cs.ai', 'cs.cl', 'cs.lg']",2302.09419v3.pdf,"  Pretrained Foundation Models (PFMs) are regarded as the foundation for
+various downstream tasks with different data modalities. A PFM (e.g., BERT,
+ChatGPT, and GPT-4) is trained on large-scale data which provides a reasonable
+parameter initialization for a wide range of downstream applications. BERT
+learns bidirectional encoder representations from Transformers, which are
+trained on large datasets as contextual language models. Similarly, the
+generative pretrained transformer (GPT) method employs Transformers as the
+feature extractor and is trained using an autoregressive paradigm on large
+datasets. Recently, ChatGPT shows promising success on large language models,
+which applies an autoregressive language model with zero shot or few shot
+prompting. The remarkable achievements of PFM have brought significant
+breakthroughs to various fields of AI. Numerous studies have proposed different
+methods, raising the demand for an updated survey. This study provides a
+comprehensive review of recent research advancements, challenges, and
+opportunities for PFMs in text, image, graph, as well as other data modalities.
+The review covers the basic components and existing pretraining methods used in
+natural language processing, computer vision, and graph learning. Additionally,
+it explores advanced PFMs used for different data modalities and unified PFMs
+that consider data quality and quantity. The review also discusses research
+related to the fundamentals of PFMs, such as model efficiency and compression,
+security, and privacy. Finally, the study provides key implications, future
+research directions, challenges, and open problems in the field of PFMs.
+Overall, this survey aims to shed light on the research of the PFMs on
+scalability, security, logical reasoning ability, cross-domain learning
+ability, and the user-friendly interactive ability for artificial general
+intelligence.
+"
+Short Answer Grading Using One-shot Prompting and Text Similarity  Scoring Model,Su-Youn Yoon,http://arxiv.org/pdf/2305.18638v1.pdf,2023-05-29,"['cs.cl', 'i.2.7']",2305.18638v1.pdf,"  In this study, we developed an automated short answer grading (ASAG) model
+that provided both analytic scores and final holistic scores. Short answer
+items typically consist of multiple sub-questions, and providing an analytic
+score and the text span relevant to each sub-question can increase the
+interpretability of the automated scores. Furthermore, they can be used to
+generate actionable feedback for students. Despite these advantages, most
+studies have focused on predicting only holistic scores due to the difficulty
+in constructing dataset with manual annotations. To address this difficulty, we
+used large language model (LLM)-based one-shot prompting and a text similarity
+scoring model with domain adaptation using small manually annotated dataset.
+The accuracy and quadratic weighted kappa of our model were 0.67 and 0.71 on a
+subset of the publicly available ASAG dataset. The model achieved a substantial
+improvement over the majority baseline.
+"
+DePlot: One-shot visual language reasoning by plot-to-table translation,Fangyu Liu,http://arxiv.org/pdf/2212.10505v2.pdf,2022-12-20,"['cs.cl', 'cs.ai', 'cs.cv']",2212.10505v2.pdf,"  Visual language such as charts and plots is ubiquitous in the human world.
+Comprehending plots and charts requires strong reasoning skills. Prior
+state-of-the-art (SOTA) models require at least tens of thousands of training
+examples and their reasoning capabilities are still much limited, especially on
+complex human-written queries. This paper presents the first one-shot solution
+to visual language reasoning. We decompose the challenge of visual language
+reasoning into two steps: (1) plot-to-text translation, and (2) reasoning over
+the translated text. The key in this method is a modality conversion module,
+named as DePlot, which translates the image of a plot or chart to a linearized
+table. The output of DePlot can then be directly used to prompt a pretrained
+large language model (LLM), exploiting the few-shot reasoning capabilities of
+LLMs. To obtain DePlot, we standardize the plot-to-table task by establishing
+unified task formats and metrics, and train DePlot end-to-end on this task.
+DePlot can then be used off-the-shelf together with LLMs in a plug-and-play
+fashion. Compared with a SOTA model finetuned on more than >28k data points,
+DePlot+LLM with just one-shot prompting achieves a 24.0% improvement over
+finetuned SOTA on human-written queries from the task of chart QA.
+"
+CHAI-DT: A Framework for Prompting Conversational Generative AI Agents  to Actively Participate in Co-Creation,Brandon Harwood,http://arxiv.org/pdf/2305.03852v1.pdf,2023-05-05,"['cs.hc', 'cs.ai']",2305.03852v1.pdf,"  This paper explores the potential for utilizing generative AI models in
+group-focused co-creative frameworks to enhance problem solving and ideation in
+business innovation and co-creation contexts, and proposes a novel prompting
+technique for conversational generative AI agents which employ methods inspired
+by traditional 'human-to-human' facilitation and instruction to enable active
+contribution to Design Thinking, a co-creative framework. Through experiments
+using this prompting technique, we gather evidence that conversational
+generative transformers (i.e. ChatGPT) have the capability to contribute
+context-specific, useful, and creative input into Design Thinking activities.
+We also discuss the potential benefits, limitations, and risks associated with
+using generative AI models in co-creative ideation and provide recommendations
+for future research.
+"
+AceCoder: Utilizing Existing Code to Enhance Code Generation,Jia Li,http://arxiv.org/pdf/2303.17780v3.pdf,2023-03-31,"['cs.se', 'cs.ai']",2303.17780v3.pdf,"  Large Language Models (LLMs) have shown great success in code generation.
+LLMs take as the input a prompt and output the code. A key question is how to
+make prompts (i.e., Prompting Techniques). Existing prompting techniques are
+designed for natural language generation and have low accuracy in code
+generation.
+  In this paper, we propose a new prompting technique named AceCoder. Our
+motivation is that code generation meets two unique challenges (i.e.,
+requirement understanding and code implementation). AceCoder contains two novel
+mechanisms (i.e., guided code generation and example retrieval) to solve these
+challenges. (1) Guided code generation asks LLMs first to analyze requirements
+and output an intermediate preliminary (e.g., test cases). The preliminary is
+used to clarify requirements and tell LLMs ""what to write"". (2) Example
+retrieval selects similar programs as examples in prompts, which provide lots
+of relevant content (e.g., algorithms, APIs) and teach LLMs ""how to write"". We
+apply AceCoder to three LLMs (e.g., Codex) and evaluate it on three public
+benchmarks using the Pass@k. Results show that AceCoder can significantly
+improve the performance of LLMs on code generation. (1) In terms of Pass@1,
+AceCoder outperforms the state-of-the-art baseline by up to 56.4% in MBPP,
+70.7% in MBJP, and 88.4% in MBJSP. (2) AceCoder is effective in LLMs with
+different sizes (i.e., 6B to 13B) and different languages (i.e., Python, Java,
+and JavaScript). (3) Human evaluation shows human developers prefer programs
+from AceCoder.
+"
+Compositional Semantic Parsing with Large Language Models,Andrew Drozdov,http://arxiv.org/pdf/2209.15003v2.pdf,2022-09-29,"['cs.cl', 'cs.ai']",2209.15003v2.pdf,"  Humans can reason compositionally when presented with new tasks. Previous
+research shows that appropriate prompting techniques enable large language
+models (LLMs) to solve artificial compositional generalization tasks such as
+SCAN. In this work, we identify additional challenges in more realistic
+semantic parsing tasks with larger vocabulary and refine these prompting
+techniques to address them. Our best method is based on least-to-most
+prompting: it decomposes the problem using prompting-based syntactic parsing,
+then uses this decomposition to select appropriate exemplars and to
+sequentially generate the semantic parse. This method allows us to set a new
+state of the art for CFQ while requiring only 1% of the training data used by
+traditional approaches. Due to the general nature of our approach, we expect
+similar efforts will lead to new results in other tasks and domains, especially
+for knowledge-intensive applications.
+"
+EvEntS ReaLM: Event Reasoning of Entity States via Language Models,Evangelia Spiliopoulou,http://arxiv.org/pdf/2211.05392v1.pdf,2022-11-10,['cs.cl'],2211.05392v1.pdf,"  This paper investigates models of event implications. Specifically, how well
+models predict entity state-changes, by targeting their understanding of
+physical attributes. Nominally, Large Language models (LLM) have been exposed
+to procedural knowledge about how objects interact, yet our benchmarking shows
+they fail to reason about the world. Conversely, we also demonstrate that
+existing approaches often misrepresent the surprising abilities of LLMs via
+improper task encodings and that proper model prompting can dramatically
+improve performance of reported baseline results across multiple tasks. In
+particular, our results indicate that our prompting technique is especially
+useful for unseen attributes (out-of-domain) or when only limited data is
+available.
+"
+GEMBA-MQM: Detecting Translation Quality Error Spans with GPT-4,Tom Kocmi,http://arxiv.org/pdf/2310.13988v1.pdf,2023-10-21,['cs.cl'],2310.13988v1.pdf,"  This paper introduces GEMBA-MQM, a GPT-based evaluation metric designed to
+detect translation quality errors, specifically for the quality estimation
+setting without the need for human reference translations. Based on the power
+of large language models (LLM), GEMBA-MQM employs a fixed three-shot prompting
+technique, querying the GPT-4 model to mark error quality spans. Compared to
+previous works, our method has language-agnostic prompts, thus avoiding the
+need for manual prompt preparation for new languages.
+  While preliminary results indicate that GEMBA-MQM achieves state-of-the-art
+accuracy for system ranking, we advise caution when using it in academic works
+to demonstrate improvements over other methods due to its dependence on the
+proprietary, black-box GPT model.
+"
+Utilizing Language Models for Energy Load Forecasting,Hao Xue,http://arxiv.org/pdf/2310.17788v1.pdf,2023-10-26,"['cs.ai', 'cs.cl']",2310.17788v1.pdf,"  Energy load forecasting plays a crucial role in optimizing resource
+allocation and managing energy consumption in buildings and cities. In this
+paper, we propose a novel approach that leverages language models for energy
+load forecasting. We employ prompting techniques to convert energy consumption
+data into descriptive sentences, enabling fine-tuning of language models. By
+adopting an autoregressive generating approach, our proposed method enables
+predictions of various horizons of future energy load consumption. Through
+extensive experiments on real-world datasets, we demonstrate the effectiveness
+and accuracy of our proposed method. Our results indicate that utilizing
+language models for energy load forecasting holds promise for enhancing energy
+efficiency and facilitating intelligent decision-making in energy systems.
+"
+Eliciting Topic Hierarchies from Large Language Models,Grace Li,http://arxiv.org/pdf/2310.19275v1.pdf,2023-10-30,['cs.hc'],2310.19275v1.pdf,"  Finding topics to write about can be a mentally demanding process. However,
+topic hierarchies can help writers explore topics of varying levels of
+specificity. In this paper, we use large language models (LLMs) to help
+construct topic hierarchies. Although LLMs have access to such knowledge, it
+can be difficult to elicit due to issues of specificity, scope, and repetition.
+We designed and tested three different prompting techniques to find one that
+maximized accuracy. We found that prepending the general topic area to a prompt
+yielded the most accurate results with 85% accuracy. We discuss applications of
+this research including STEM writing, education, and content creation.
+"
+Structured Chain-of-Thought Prompting for Code Generation,Jia Li,http://arxiv.org/pdf/2305.06599v3.pdf,2023-05-11,"['cs.se', 'cs.cl']",2305.06599v3.pdf,"  Large Language Models (LLMs) (e.g., ChatGPT) have shown impressive
+performance in code generation. LLMs take prompts as inputs, and
+Chain-of-Thought (CoT) prompting is the state-of-the-art prompting technique.
+CoT prompting asks LLMs first to generate CoTs (i.e., intermediate natural
+language reasoning steps) and then output the code. However, CoT prompting is
+designed for natural language generation and has low accuracy in code
+generation.
+  In this paper, we propose Structured CoTs (SCoTs) and present a novel
+prompting technique for code generation, named SCoT prompting. Our motivation
+is source code contains rich structural information and any code can be
+composed of three program structures (i.e., sequence, branch, and loop
+structures). Intuitively, structured intermediate reasoning steps make for
+structured source code. Thus, we ask LLMs to use program structures to build
+CoTs, obtaining SCoTs. Then, LLMs generate the final code based on SCoTs.
+Compared to CoT prompting, SCoT prompting explicitly constrains LLMs to think
+about how to solve requirements from the view of source code and further the
+performance of LLMs in code generation. We apply SCoT prompting to two LLMs
+(i.e., ChatGPT and Codex) and evaluate it on three benchmarks (i.e., HumanEval,
+MBPP, and MBCPP). (1) SCoT prompting outperforms the state-of-the-art baseline
+- CoT prompting by up to 13.79% in Pass@1. (2) Human evaluation shows human
+developers prefer programs from SCoT prompting. (3) SCoT prompting is robust to
+examples and achieves substantial improvements.
+"
+The Impact of AI in Physics Education: A Comprehensive Review from GCSE  to University Levels,Will Yeadon,http://arxiv.org/pdf/2309.05163v1.pdf,2023-09-10,['physics.ed-ph'],2309.05163v1.pdf,"  With the rapid evolution of Artificial Intelligence (AI), its potential
+implications for higher education have become a focal point of interest. This
+study delves into the capabilities of AI in Physics Education and offers
+actionable AI policy recommendations. Using a Large Language Model (LLM), we
+assessed its ability to answer 1337 Physics exam questions spanning GCSE,
+A-Level, and Introductory University curricula. We employed various AI
+prompting techniques: Zero Shot, In Context Learning, and Confirmatory
+Checking, which merges Chain of Thought reasoning with Reflection. The AI's
+proficiency varied across academic levels: it scored an average of 83.4% on
+GCSE, 63.8% on A-Level, and 37.4% on university-level questions, with an
+overall average of 59.9% using the most effective prompting technique. In a
+separate test, the LLM's accuracy on 5000 mathematical operations was found to
+decrease as the number of digits increased. Furthermore, when evaluated as a
+marking tool, the LLM's concordance with human markers averaged at 50.8%, with
+notable inaccuracies in marking straightforward questions, like
+multiple-choice. Given these results, our recommendations underscore caution:
+while current LLMs can consistently perform well on Physics questions at
+earlier educational stages, their efficacy diminishes with advanced content and
+complex calculations. LLM outputs often showcase novel methods not in the
+syllabus, excessive verbosity, and miscalculations in basic arithmetic. This
+suggests that at university, there's no substantial threat from LLMs for
+non-invigilated Physics questions. However, given the LLMs' considerable
+proficiency in writing Physics essays and coding abilities, non-invigilated
+examinations of these skills in Physics are highly vulnerable to automated
+completion by LLMs. This vulnerability also extends to Physics questions
+pitched at lower academic levels.
+"
+HELP ME THINK: A Simple Prompting Strategy for Non-experts to Create  Customized Content with Models,Swaroop Mishra,http://arxiv.org/pdf/2208.08232v2.pdf,2022-08-17,"['cs.cl', 'cs.ai', 'cs.cv', 'cs.hc', 'cs.lg']",2208.08232v2.pdf,"  Controlling the text generated by language models and customizing the content
+has been a long-standing challenge. Existing prompting techniques proposed in
+pursuit of providing control are task-specific and lack generality; this
+provides overwhelming choices for non-expert users to find a suitable method
+for their task. The effort associated with those techniques, such as in writing
+examples, explanations, instructions, etc. further limits their adoption among
+non-expert users. In this paper, we propose a simple prompting strategy HELP ME
+THINK where we encourage GPT3 to help non-expert users by asking a set of
+relevant questions and leveraging user answers to execute the task. We
+demonstrate the efficacy of our technique HELP ME THINK on a variety of tasks.
+Specifically, we focus on tasks that are hard for average humans and require
+significant thinking to perform. We hope our work will encourage the
+development of unconventional ways to harness the power of large language
+models.
+"
+Enabling Conversational Interaction with Mobile UI using Large Language  Models,Bryan Wang,http://arxiv.org/pdf/2209.08655v2.pdf,2022-09-18,"['cs.hc', 'cs.ai']",2209.08655v2.pdf,"  Conversational agents show the promise to allow users to interact with mobile
+devices using language. However, to perform diverse UI tasks with natural
+language, developers typically need to create separate datasets and models for
+each specific task, which is expensive and effort-consuming. Recently,
+pre-trained large language models (LLMs) have been shown capable of
+generalizing to various downstream tasks when prompted with a handful of
+examples from the target task. This paper investigates the feasibility of
+enabling versatile conversational interactions with mobile UIs using a single
+LLM. We designed prompting techniques to adapt an LLM to mobile UIs. We
+experimented with four important modeling tasks that address various scenarios
+in conversational interaction. Our method achieved competitive performance on
+these challenging tasks without requiring dedicated datasets and training,
+offering a lightweight and generalizable approach to enable language-based
+mobile interaction.
+"
+Teaching Algorithmic Reasoning via In-context Learning,Hattie Zhou,http://arxiv.org/pdf/2211.09066v1.pdf,2022-11-15,"['cs.lg', 'cs.ai', 'cs.cl']",2211.09066v1.pdf,"  Large language models (LLMs) have shown increasing in-context learning
+capabilities through scaling up model and data size. Despite this progress,
+LLMs are still unable to solve algorithmic reasoning problems. While providing
+a rationale with the final answer has led to further improvements in multi-step
+reasoning problems, Anil et al. 2022 showed that even simple algorithmic
+reasoning tasks such as parity are far from solved. In this work, we identify
+and study four key stages for successfully teaching algorithmic reasoning to
+LLMs: (1) formulating algorithms as skills, (2) teaching multiple skills
+simultaneously (skill accumulation), (3) teaching how to combine skills (skill
+composition) and (4) teaching how to use skills as tools. We show that it is
+possible to teach algorithmic reasoning to LLMs via in-context learning, which
+we refer to as algorithmic prompting. We evaluate our approach on a variety of
+arithmetic and quantitative reasoning tasks, and demonstrate significant boosts
+in performance over existing prompting techniques. In particular, for long
+parity, addition, multiplication and subtraction, we achieve an error reduction
+of approximately 10x, 9x, 5x and 2x respectively compared to the best available
+baselines.
+"
+Understanding Stereotypes in Language Models: Towards Robust Measurement  and Zero-Shot Debiasing,Justus Mattern,http://arxiv.org/pdf/2212.10678v1.pdf,2022-12-20,"['cs.cl', 'cs.lg']",2212.10678v1.pdf,"  Generated texts from large pretrained language models have been shown to
+exhibit a variety of harmful, human-like biases about various demographics.
+These findings prompted large efforts aiming to understand and measure such
+effects, with the goal of providing benchmarks that can guide the development
+of techniques mitigating these stereotypical associations. However, as recent
+research has pointed out, the current benchmarks lack a robust experimental
+setup, consequently hindering the inference of meaningful conclusions from
+their evaluation metrics. In this paper, we extend these arguments and
+demonstrate that existing techniques and benchmarks aiming to measure
+stereotypes tend to be inaccurate and consist of a high degree of experimental
+noise that severely limits the knowledge we can gain from benchmarking language
+models based on them. Accordingly, we propose a new framework for robustly
+measuring and quantifying biases exhibited by generative language models.
+Finally, we use this framework to investigate GPT-3's occupational gender bias
+and propose prompting techniques for mitigating these biases without the need
+for fine-tuning.
+"
+Image To Tree with Recursive Prompting,James Batten,http://arxiv.org/pdf/2301.00447v1.pdf,2023-01-01,"['cs.cv', 'cs.lg']",2301.00447v1.pdf,"  Extracting complex structures from grid-based data is a common key step in
+automated medical image analysis. The conventional solution to recovering
+tree-structured geometries typically involves computing the minimal cost path
+through intermediate representations derived from segmentation masks. However,
+this methodology has significant limitations in the context of projective
+imaging of tree-structured 3D anatomical data such as coronary arteries, since
+there are often overlapping branches in the 2D projection. In this work, we
+propose a novel approach to predicting tree connectivity structure which
+reformulates the task as an optimization problem over individual steps of a
+recursive process. We design and train a two-stage model which leverages the
+UNet and Transformer architectures and introduces an image-based prompting
+technique. Our proposed method achieves compelling results on a pair of
+synthetic datasets, and outperforms a shortest-path baseline.
+"
+Large Language Models Can Be Easily Distracted by Irrelevant Context,Freda Shi,http://arxiv.org/pdf/2302.00093v3.pdf,2023-01-31,"['cs.cl', 'cs.ai']",2302.00093v3.pdf,"  Large language models have achieved impressive performance on various natural
+language processing tasks. However, so far they have been evaluated primarily
+on benchmarks where all information in the input context is relevant for
+solving the task. In this work, we investigate the distractibility of large
+language models, i.e., how the model problem-solving accuracy can be influenced
+by irrelevant context. In particular, we introduce Grade-School Math with
+Irrelevant Context (GSM-IC), an arithmetic reasoning dataset with irrelevant
+information in the problem description. We use this benchmark to measure the
+distractibility of cutting-edge prompting techniques for large language models,
+and find that the model performance is dramatically decreased when irrelevant
+information is included. We also identify several approaches for mitigating
+this deficiency, such as decoding with self-consistency and adding to the
+prompt an instruction that tells the language model to ignore the irrelevant
+information.
+"
+Synthetic Prompting: Generating Chain-of-Thought Demonstrations for  Large Language Models,Zhihong Shao,http://arxiv.org/pdf/2302.00618v1.pdf,2023-02-01,['cs.cl'],2302.00618v1.pdf,"  Large language models can perform various reasoning tasks by using
+chain-of-thought prompting, which guides them to find answers through
+step-by-step demonstrations. However, the quality of the prompts depends on the
+demonstrations given to the models, and creating many of them by hand is
+costly. We introduce Synthetic prompting, a method that leverages a few
+handcrafted examples to prompt the model to generate more examples by itself,
+and selects effective demonstrations to elicit better reasoning. Our method
+alternates between a backward and forward process to generate new examples. The
+backward process generates a question that match a sampled reasoning chain, so
+that the question is solvable and clear. The forward process produces a more
+detailed reasoning chain for the question, improving the quality of the
+example. We evaluate our method on numerical, symbolic, and algorithmic
+reasoning tasks, and show that it outperforms existing prompting techniques.
+"
+Language-Specific Representation of Emotion-Concept Knowledge Causally  Supports Emotion Inference,Ming Li,http://arxiv.org/pdf/2302.09582v4.pdf,2023-02-19,"['cs.ai', 'cs.cl']",2302.09582v4.pdf,"  Understanding how language supports emotion inference remains a topic of
+debate in emotion science. The present study investigated whether
+language-derived emotion-concept knowledge would causally support emotion
+inference by manipulating the language-specific knowledge representations in
+large language models. Using the prompt technique, 14 attributes of emotion
+concepts were found to be represented by distinct artificial neuron
+populations. By manipulating these attribute-related neurons, the majority of
+the emotion inference tasks showed performance deterioration compared to random
+manipulations. The attribute-specific performance deterioration was related to
+the importance of different attributes in human mental space. Our findings
+provide causal evidence in support of a language-based mechanism for emotion
+inference and highlight the contributions of emotion-concept knowledge.
+"
+MathPrompter: Mathematical Reasoning using Large Language Models,Shima Imani,http://arxiv.org/pdf/2303.05398v1.pdf,2023-03-04,"['cs.cl', 'cs.ai']",2303.05398v1.pdf,"  Large Language Models (LLMs) have limited performance when solving arithmetic
+reasoning tasks and often provide incorrect answers. Unlike natural language
+understanding, math problems typically have a single correct answer, making the
+task of generating accurate solutions more challenging for LLMs. To the best of
+our knowledge, we are not aware of any LLMs that indicate their level of
+confidence in their responses which fuels a trust deficit in these models
+impeding their adoption. To address this deficiency, we propose `MathPrompter',
+a technique that improves performance of LLMs on arithmetic problems along with
+increased reliance in the predictions. MathPrompter uses the Zero-shot
+chain-of-thought prompting technique to generate multiple Algebraic expressions
+or Python functions to solve the same math problem in different ways and
+thereby raise the confidence level in the output results. This is in contrast
+to other prompt based CoT methods, where there is no check on the validity of
+the intermediate steps followed. Our technique improves over state-of-the-art
+on the MultiArith dataset ($78.7\%\rightarrow92.5\%$) evaluated using 175B
+parameter GPT-based LLM.
+"
+Zero-shot Temporal Relation Extraction with ChatGPT,Chenhan Yuan,http://arxiv.org/pdf/2304.05454v1.pdf,2023-04-11,"['cs.cl', 'cs.ai']",2304.05454v1.pdf,"  The goal of temporal relation extraction is to infer the temporal relation
+between two events in the document. Supervised models are dominant in this
+task. In this work, we investigate ChatGPT's ability on zero-shot temporal
+relation extraction. We designed three different prompt techniques to break
+down the task and evaluate ChatGPT. Our experiments show that ChatGPT's
+performance has a large gap with that of supervised methods and can heavily
+rely on the design of prompts. We further demonstrate that ChatGPT can infer
+more small relation classes correctly than supervised methods. The current
+shortcomings of ChatGPT on temporal relation extraction are also discussed in
+this paper. We found that ChatGPT cannot keep consistency during temporal
+inference and it fails in actively long-dependency temporal inference.
+"
+An Empirical Study on the Robustness of the Segment Anything Model (SAM),Yuqing Wang,http://arxiv.org/pdf/2305.06422v2.pdf,2023-05-10,['cs.cv'],2305.06422v2.pdf,"  The Segment Anything Model (SAM) is a foundation model for general image
+segmentation. Although it exhibits impressive performance predominantly on
+natural images, understanding its robustness against various image
+perturbations and domains is critical for real-world applications where such
+challenges frequently arise. In this study we conduct a comprehensive
+robustness investigation of SAM under diverse real-world conditions. Our
+experiments encompass a wide range of image perturbations. Our experimental
+results demonstrate that SAM's performance generally declines under perturbed
+images, with varying degrees of vulnerability across different perturbations.
+By customizing prompting techniques and leveraging domain knowledge based on
+the unique characteristics of each dataset, the model's resilience to these
+perturbations can be enhanced, addressing dataset-specific challenges. This
+work sheds light on the limitations and strengths of SAM in real-world
+applications, promoting the development of more robust and versatile image
+segmentation solutions.
+"
+SCITAB: A Challenging Benchmark for Compositional Reasoning and Claim  Verification on Scientific Tables,Xinyuan Lu,http://arxiv.org/pdf/2305.13186v3.pdf,2023-05-22,"['cs.cl', 'cs.ai']",2305.13186v3.pdf,"  Current scientific fact-checking benchmarks exhibit several shortcomings,
+such as biases arising from crowd-sourced claims and an over-reliance on
+text-based evidence. We present SCITAB, a challenging evaluation dataset
+consisting of 1.2K expert-verified scientific claims that 1) originate from
+authentic scientific publications and 2) require compositional reasoning for
+verification. The claims are paired with evidence-containing scientific tables
+annotated with labels. Through extensive evaluations, we demonstrate that
+SCITAB poses a significant challenge to state-of-the-art models, including
+table-based pretraining models and large language models. All models except
+GPT-4 achieved performance barely above random guessing. Popular prompting
+techniques, such as Chain-of-Thought, do not achieve much performance gains on
+SCITAB. Our analysis uncovers several unique challenges posed by SCITAB,
+including table grounding, claim ambiguity, and compositional reasoning. Our
+codes and data are publicly available at https://github.com/XinyuanLu00/SciTab.
+"
+Unraveling ChatGPT: A Critical Analysis of AI-Generated Goal-Oriented  Dialogues and Annotations,Tiziano Labruna,http://arxiv.org/pdf/2305.14556v1.pdf,2023-05-23,"['cs.cl', 'cs.ai']",2305.14556v1.pdf,"  Large pre-trained language models have exhibited unprecedented capabilities
+in producing high-quality text via prompting techniques. This fact introduces
+new possibilities for data collection and annotation, particularly in
+situations where such data is scarce, complex to gather, expensive, or even
+sensitive. In this paper, we explore the potential of these models to generate
+and annotate goal-oriented dialogues, and conduct an in-depth analysis to
+evaluate their quality. Our experiments employ ChatGPT, and encompass three
+categories of goal-oriented dialogues (task-oriented, collaborative, and
+explanatory), two generation modes (interactive and one-shot), and two
+languages (English and Italian). Based on extensive human-based evaluations, we
+demonstrate that the quality of generated dialogues and annotations is on par
+with those generated by humans.
+"
+StudentEval: A Benchmark of Student-Written Prompts for Large Language  Models of Code,Hannah McLean Babe,http://arxiv.org/pdf/2306.04556v1.pdf,2023-06-07,"['cs.lg', 'cs.hc', 'cs.se']",2306.04556v1.pdf,"  Code LLMs are being rapidly deployed and there is evidence that they can make
+professional programmers more productive. Current benchmarks for code
+generation measure whether models generate correct programs given an expert
+prompt. In this paper, we present a new benchmark containing multiple prompts
+per problem, written by a specific population of non-expert prompters:
+beginning programmers. StudentEval contains 1,749 prompts for 48 problems,
+written by 80 students who have only completed one semester of Python
+programming. Our students wrote these prompts while working interactively with
+a Code LLM, and we observed very mixed success rates. We use StudentEval to
+evaluate 5 Code LLMs and find that StudentEval is a better discriminator of
+model performance than existing benchmarks. We analyze the prompts and find
+significant variation in students' prompting techniques. We also find that
+nondeterministic LLM sampling could mislead students into thinking that their
+prompts are more (or less) effective than they actually are, which has
+implications for how to teach with Code LLMs.
+"
+Knowledge-Prompted Estimator: A Novel Approach to Explainable Machine  Translation Assessment,Hao Yang,http://arxiv.org/pdf/2306.07486v1.pdf,2023-06-13,['cs.cl'],2306.07486v1.pdf,"  Cross-lingual Machine Translation (MT) quality estimation plays a crucial
+role in evaluating translation performance. GEMBA, the first MT quality
+assessment metric based on Large Language Models (LLMs), employs one-step
+prompting to achieve state-of-the-art (SOTA) in system-level MT quality
+estimation; however, it lacks segment-level analysis. In contrast,
+Chain-of-Thought (CoT) prompting outperforms one-step prompting by offering
+improved reasoning and explainability. In this paper, we introduce
+Knowledge-Prompted Estimator (KPE), a CoT prompting method that combines three
+one-step prompting techniques, including perplexity, token-level similarity,
+and sentence-level similarity. This method attains enhanced performance for
+segment-level estimation compared with previous deep learning models and
+one-step prompting approaches. Furthermore, supplementary experiments on
+word-level visualized alignment demonstrate that our KPE method significantly
+improves token alignment compared with earlier models and provides better
+interpretability for MT quality estimation. Code will be released upon
+publication.
+"
+Questioning the Survey Responses of Large Language Models,Ricardo Dominguez-Olmedo,http://arxiv.org/pdf/2306.07951v2.pdf,2023-06-13,['cs.cl'],2306.07951v2.pdf,"  As large language models increase in capability, researchers have started to
+conduct surveys of all kinds on these models with varying scientific
+motivations. In this work, we examine what we can learn from language models'
+survey responses on the basis of the well-established American Community Survey
+(ACS) by the U.S. Census Bureau. Using a de-facto standard multiple-choice
+prompting technique and evaluating 40 different language models, hundreds of
+thousands of times each on questions from the ACS, we systematically establish
+two dominant patterns. First, models have significant position and labeling
+biases, for example, towards survey responses labeled with the letter ""A"".
+Second, when adjusting for labeling biases through randomized answer ordering,
+models across the board trend towards uniformly random survey responses. In
+fact, binary classifiers can almost perfectly differentiate between models'
+responses to the ACS and the responses of the US census. Taken together, our
+findings suggest caution in treating survey responses from language models as
+equivalent to those of human populations at present time.
+"
+Investigating Prompting Techniques for Zero- and Few-Shot Visual  Question Answering,Rabiul Awal,http://arxiv.org/pdf/2306.09996v1.pdf,2023-06-16,"['cs.cv', 'cs.cl']",2306.09996v1.pdf,"  Visual question answering (VQA) is a challenging task that requires the
+ability to comprehend and reason with visual information. While recent
+vision-language models have made strides, they continue to struggle with
+zero-shot VQA, particularly in handling complex compositional questions and
+adapting to new domains i.e. knowledge-based reasoning. This paper explores the
+use of various prompting strategies, focusing on the BLIP2 model, to enhance
+zero-shot VQA performance. We conduct a comprehensive investigation across
+several VQA datasets, examining the effectiveness of different question
+templates, the role of few-shot exemplars, the impact of chain-of-thought (CoT)
+reasoning, and the benefits of incorporating image captions as additional
+visual cues. Despite the varied outcomes, our findings demonstrate that
+carefully designed question templates and the integration of additional visual
+cues, like image captions, can contribute to improved VQA performance,
+especially when used in conjunction with few-shot examples. However, we also
+identify a limitation in the use of chain-of-thought rationalization, which
+negatively affects VQA accuracy. Our study thus provides critical insights into
+the potential of prompting for improving zero-shot VQA performance.
+"
+Extracting Multi-valued Relations from Language Models,Sneha Singhania,http://arxiv.org/pdf/2307.03122v2.pdf,2023-07-06,['cs.cl'],2307.03122v2.pdf,"  The widespread usage of latent language representations via pre-trained
+language models (LMs) suggests that they are a promising source of structured
+knowledge. However, existing methods focus only on a single object per
+subject-relation pair, even though often multiple objects are correct. To
+overcome this limitation, we analyze these representations for their potential
+to yield materialized multi-object relational knowledge. We formulate the
+problem as a rank-then-select task. For ranking candidate objects, we evaluate
+existing prompting techniques and propose new ones incorporating domain
+knowledge. Among the selection methods, we find that choosing objects with a
+likelihood above a learned relation-specific threshold gives a 49.5% F1 score.
+Our results highlight the difficulty of employing LMs for the multi-valued
+slot-filling task and pave the way for further research on extracting
+relational knowledge from latent language representations.
+"
+Prompts Should not be Seen as Secrets: Systematically Measuring Prompt  Extraction Attack Success,Yiming Zhang,http://arxiv.org/pdf/2307.06865v1.pdf,2023-07-13,"['cs.cl', 'cs.ai']",2307.06865v1.pdf,"  The generations of large language models are commonly controlled through
+prompting techniques, where a user's query to the model is prefixed with a
+prompt that aims to guide the model's behaviour on the query. The prompts used
+by companies to guide their models are often treated as secrets, to be hidden
+from the user making the query. They have even been treated as commodities to
+be bought and sold. However, there has been anecdotal evidence showing that the
+prompts can be extracted by a user even when they are kept secret. In this
+paper, we present a framework for systematically measuring the success of
+prompt extraction attacks. In experiments with multiple sources of prompts and
+multiple underlying language models, we find that simple text-based attacks can
+in fact reveal prompts with high probability.
+"
+Leveraging Large Language Models to Generate Answer Set Programs,Adam Ishay,http://arxiv.org/pdf/2307.07699v1.pdf,2023-07-15,"['cs.ai', 'cs.cl', 'cs.sc']",2307.07699v1.pdf,"  Large language models (LLMs), such as GPT-3 and GPT-4, have demonstrated
+exceptional performance in various natural language processing tasks and have
+shown the ability to solve certain reasoning problems. However, their reasoning
+capabilities are limited and relatively shallow, despite the application of
+various prompting techniques. In contrast, formal logic is adept at handling
+complex reasoning, but translating natural language descriptions into formal
+logic is a challenging task that non-experts struggle with. This paper proposes
+a neuro-symbolic method that combines the strengths of large language models
+and answer set programming. Specifically, we employ an LLM to transform natural
+language descriptions of logic puzzles into answer set programs. We carefully
+design prompts for an LLM to convert natural language descriptions into answer
+set programs in a step by step manner. Surprisingly, with just a few in-context
+learning examples, LLMs can generate reasonably complex answer set programs.
+The majority of errors made are relatively simple and can be easily corrected
+by humans, thus enabling LLMs to effectively assist in the creation of answer
+set programs.
+"
+Fixing Rust Compilation Errors using LLMs,Pantazis Deligiannis,http://arxiv.org/pdf/2308.05177v1.pdf,2023-08-09,"['cs.se', 'cs.pl']",2308.05177v1.pdf,"  The Rust programming language, with its safety guarantees, has established
+itself as a viable choice for low-level systems programming language over the
+traditional, unsafe alternatives like C/C++. These guarantees come from a
+strong ownership-based type system, as well as primitive support for features
+like closures, pattern matching, etc., that make the code more concise and
+amenable to reasoning. These unique Rust features also pose a steep learning
+curve for programmers.
+  This paper presents a tool called RustAssistant that leverages the emergent
+capabilities of Large Language Models (LLMs) to automatically suggest fixes for
+Rust compilation errors. RustAssistant uses a careful combination of prompting
+techniques as well as iteration with an LLM to deliver high accuracy of fixes.
+RustAssistant is able to achieve an impressive peak accuracy of roughly 74% on
+real-world compilation errors in popular open-source Rust repositories. We plan
+to release our dataset of Rust compilation errors to enable further research.
+"
+The Devil is in the Errors: Leveraging Large Language Models for  Fine-grained Machine Translation Evaluation,Patrick Fernandes,http://arxiv.org/pdf/2308.07286v1.pdf,2023-08-14,"['cs.cl', 'cs.lg']",2308.07286v1.pdf,"  Automatic evaluation of machine translation (MT) is a critical tool driving
+the rapid iterative development of MT systems. While considerable progress has
+been made on estimating a single scalar quality score, current metrics lack the
+informativeness of more detailed schemes that annotate individual errors, such
+as Multidimensional Quality Metrics (MQM). In this paper, we help fill this gap
+by proposing AutoMQM, a prompting technique which leverages the reasoning and
+in-context learning capabilities of large language models (LLMs) and asks them
+to identify and categorize errors in translations. We start by evaluating
+recent LLMs, such as PaLM and PaLM-2, through simple score prediction
+prompting, and we study the impact of labeled data through in-context learning
+and finetuning. We then evaluate AutoMQM with PaLM-2 models, and we find that
+it improves performance compared to just prompting for scores (with
+particularly large gains for larger models) while providing interpretability
+through error spans that align with human annotations.
+"
+Boosting Logical Reasoning in Large Language Models through a New  Framework: The Graph of Thought,Bin Lei,http://arxiv.org/pdf/2308.08614v1.pdf,2023-08-16,"['cs.lg', 'cs.ai', 'cs.cl']",2308.08614v1.pdf,"  Recent advancements in large-scale models, such as GPT-4, have showcased
+remarkable capabilities in addressing standard queries. However, when facing
+complex problems that require multi-step logical reasoning, their accuracy
+dramatically decreases. Current research has explored the realm of
+\textit{prompting engineering} to bolster the inferential capacities of these
+models. Our paper unveils a pioneering prompting technique, dubbed
+\textit{Graph of Thoughts (GoT)}. Through testing on a trio of escalating
+challenges: the 24-point game, resolution of high-degree polynomial equations,
+and derivation of formulas for recursive sequences, our method outperformed
+GPT-4, achieving accuracy improvements of $89.7\%$, $86\%$, and $56\%$ for each
+respective task. Moreover, when juxtaposed with the state-of-the-art (SOTA)
+prompting method, \textit{Tree of Thought (ToT)}, our approach registered an
+average accuracy boost of $23\%$, $24\%$, and $15\%$.
+"
+DevGPT: Studying Developer-ChatGPT Conversations,Tao Xiao,http://arxiv.org/pdf/2309.03914v1.pdf,2023-08-31,['cs.se'],2309.03914v1.pdf,"  The emergence of large language models (LLMs) such as ChatGPT has disrupted
+the landscape of software development. Many studies are investigating the
+quality of responses generated by ChatGPT, the efficacy of various prompting
+techniques, and its comparative performance in programming contests, to name a
+few examples. Yet, we know very little about how ChatGPT is actually used by
+software developers. What questions do developers present to ChatGPT? What are
+the dynamics of these interactions? What is the backdrop against which these
+conversations are held, and how do the conversations feedback into the
+artifacts of their work? To close this gap, we introduce DevGPT, a curated
+dataset which encompasses 17,913 prompts and ChatGPT's responses including
+11,751 code snippets, coupled with the corresponding software development
+artifacts -- ranging from source code, commits, issues, pull requests, to
+discussions and Hacker News threads -- to enable the analysis of the context
+and implications of these developer interactions with ChatGPT.
+"
+Generative Speech Recognition Error Correction with Large Language  Models and Task-Activating Prompting,Chao-Han Huck Yang,http://arxiv.org/pdf/2309.15649v2.pdf,2023-09-27,"['cs.cl', 'cs.ai', 'cs.lg', 'cs.sd', 'eess.as']",2309.15649v2.pdf,"  We explore the ability of large language models (LLMs) to act as speech
+recognition post-processors that perform rescoring and error correction. Our
+first focus is on instruction prompting to let LLMs perform these task without
+fine-tuning, for which we evaluate different prompting schemes, both zero- and
+few-shot in-context learning, and a novel task activation prompting method that
+combines causal instructions and demonstration to increase its context windows.
+Next, we show that rescoring only by in-context learning with frozen LLMs
+achieves results that are competitive with rescoring by domain-tuned LMs, using
+a pretrained first-pass recognition system and rescoring output on two
+out-of-domain tasks (ATIS and WSJ). By combining prompting techniques with
+fine-tuning we achieve error rates below the N-best oracle level, showcasing
+the generalization power of the LLMs.
+"
+UPAR: A Kantian-Inspired Prompting Framework for Enhancing Large  Language Model Capabilities,Hejia Geng,http://arxiv.org/pdf/2310.01441v1.pdf,2023-09-30,"['cs.cl', 'cs.ai']",2310.01441v1.pdf,"  Large Language Models (LLMs) have demonstrated impressive inferential
+capabilities, with numerous research endeavors devoted to enhancing this
+capacity through prompting. Despite these efforts, a unified epistemological
+foundation is still conspicuously absent. Drawing inspiration from Kant's a
+priori philosophy, we propose the UPAR prompting framework, designed to emulate
+the structure of human cognition within LLMs. The UPAR framework is delineated
+into four phases: ""Understand"", ""Plan"", ""Act"", and ""Reflect"", enabling the
+extraction of structured information from complex contexts, prior planning of
+solutions, execution according to plan, and self-reflection. This structure
+significantly augments the explainability and accuracy of LLM inference,
+producing a human-understandable and inspectable inferential trajectory.
+Furthermore, our work offers an epistemological foundation for existing
+prompting techniques, allowing for a possible systematic integration of these
+methods. With GPT-4, our approach elevates the accuracy from COT baseline of
+22.92% to 58.33% in a challenging subset of GSM8K, and from 67.91% to 75.40% in
+the causal judgment task.
+"
+Take a Step Back: Evoking Reasoning via Abstraction in Large Language  Models,Huaixiu Steven Zheng,http://arxiv.org/pdf/2310.06117v1.pdf,2023-10-09,"['cs.lg', 'cs.ai', 'cs.cl']",2310.06117v1.pdf,"  We present Step-Back Prompting, a simple prompting technique that enables
+LLMs to do abstractions to derive high-level concepts and first principles from
+instances containing specific details. Using the concepts and principles to
+guide the reasoning steps, LLMs significantly improve their abilities in
+following a correct reasoning path towards the solution. We conduct experiments
+of Step-Back Prompting with PaLM-2L models and observe substantial performance
+gains on a wide range of challenging reasoning-intensive tasks including STEM,
+Knowledge QA, and Multi-Hop Reasoning. For instance, Step-Back Prompting
+improves PaLM-2L performance on MMLU Physics and Chemistry by 7% and 11%,
+TimeQA by 27%, and MuSiQue by 7%.
+"
+POSQA: Probe the World Models of LLMs with Size Comparisons,Chang Shu,http://arxiv.org/pdf/2310.13394v1.pdf,2023-10-20,"['cs.cl', 'cs.ai', 'cs.cy']",2310.13394v1.pdf,"  Embodied language comprehension emphasizes that language understanding is not
+solely a matter of mental processing in the brain but also involves
+interactions with the physical and social environment. With the explosive
+growth of Large Language Models (LLMs) and their already ubiquitous presence in
+our daily lives, it is becoming increasingly necessary to verify their
+real-world understanding. Inspired by cognitive theories, we propose POSQA: a
+Physical Object Size Question Answering dataset with simple size comparison
+questions to examine the extremity and analyze the potential mechanisms of the
+embodied comprehension of the latest LLMs.
+  We show that even the largest LLMs today perform poorly under the zero-shot
+setting. We then push their limits with advanced prompting techniques and
+external knowledge augmentation. Furthermore, we investigate whether their
+real-world comprehension primarily derives from contextual information or
+internal weights and analyse the impact of prompt formats and report bias of
+different objects. Our results show that real-world understanding that LLMs
+shaped from textual data can be vulnerable to deception and confusion by the
+surface form of prompts, which makes it less aligned with human behaviours.
+"
+MuSR: Testing the Limits of Chain-of-thought with Multistep Soft  Reasoning,Zayne Sprague,http://arxiv.org/pdf/2310.16049v1.pdf,2023-10-24,['cs.cl'],2310.16049v1.pdf,"  While large language models (LLMs) equipped with techniques like
+chain-of-thought prompting have demonstrated impressive capabilities, they
+still fall short in their ability to reason robustly in complex settings.
+However, evaluating LLM reasoning is challenging because system capabilities
+continue to grow while benchmark datasets for tasks like logical deduction have
+remained static. We introduce MuSR, a dataset for evaluating language models on
+multistep soft reasoning tasks specified in a natural language narrative. This
+dataset has two crucial features. First, it is created through a novel
+neurosymbolic synthetic-to-natural generation algorithm, enabling the
+construction of complex reasoning instances that challenge GPT-4 (e.g., murder
+mysteries roughly 1000 words in length) and which can be scaled further as more
+capable LLMs are released. Second, our dataset instances are free text
+narratives corresponding to real-world domains of reasoning; this makes it
+simultaneously much more challenging than other synthetically-crafted
+benchmarks while remaining realistic and tractable for human annotators to
+solve with high accuracy. We evaluate a range of LLMs and prompting techniques
+on this dataset and characterize the gaps that remain for techniques like
+chain-of-thought to perform robust reasoning.
+"
+"Supercharging academic writing with generative AI: framework,  techniques, and caveats",Zhicheng Lin,http://arxiv.org/pdf/2310.17143v1.pdf,2023-10-26,"['cs.cy', 'cs.cl']",2310.17143v1.pdf,"  Academic writing is an indispensable yet laborious part of the research
+enterprise. This Perspective maps out principles and methods for using
+generative artificial intelligence (AI), specifically large language models
+(LLMs), to elevate the quality and efficiency of academic writing. We introduce
+a human-AI collaborative framework that delineates the rationale (why), process
+(how), and nature (what) of AI engagement in writing. The framework pinpoints
+both short-term and long-term reasons for engagement and their underlying
+mechanisms (e.g., cognitive offloading and imaginative stimulation). It reveals
+the role of AI throughout the writing process, conceptualized through a
+two-stage model for human-AI collaborative writing, and the nature of AI
+assistance in writing, represented through a model of writing-assistance types
+and levels. Building on this framework, we describe effective prompting
+techniques for incorporating AI into the writing routine (outlining, drafting,
+and editing) as well as strategies for maintaining rigorous scholarship,
+adhering to varied journal policies, and avoiding overreliance on AI.
+Ultimately, the prudent integration of AI into academic writing can ease the
+communication burden, empower authors, accelerate discovery, and promote
+diversity in science.
+"
+Little Giants: Exploring the Potential of Small LLMs as Evaluation  Metrics in Summarization in the Eval4NLP 2023 Shared Task,Neema Kotonya,http://arxiv.org/pdf/2311.00686v1.pdf,2023-11-01,['cs.cl'],2311.00686v1.pdf,"  This paper describes and analyzes our participation in the 2023 Eval4NLP
+shared task, which focuses on assessing the effectiveness of prompt-based
+techniques to empower Large Language Models to handle the task of quality
+estimation, particularly in the context of evaluating machine translations and
+summaries. We conducted systematic experiments with various prompting
+techniques, including standard prompting, prompts informed by annotator
+instructions, and innovative chain-of-thought prompting. In addition, we
+integrated these approaches with zero-shot and one-shot learning methods to
+maximize the efficacy of our evaluation procedures. Our work reveals that
+combining these approaches using a ""small"", open source model (orca_mini_v3_7B)
+yields competitive results.
+"
+Can Large Language Models Design Accurate Label Functions?,Naiqing Guan,http://arxiv.org/pdf/2311.00739v1.pdf,2023-11-01,"['cs.cl', 'cs.db', 'cs.lg', 'h.2.8; i.5.4']",2311.00739v1.pdf,"  Programmatic weak supervision methodologies facilitate the expedited labeling
+of extensive datasets through the use of label functions (LFs) that encapsulate
+heuristic data sources. Nonetheless, the creation of precise LFs necessitates
+domain expertise and substantial endeavors. Recent advances in pre-trained
+language models (PLMs) have exhibited substantial potential across diverse
+tasks. However, the capacity of PLMs to autonomously formulate accurate LFs
+remains an underexplored domain. In this research, we address this gap by
+introducing DataSculpt, an interactive framework that harnesses PLMs for the
+automated generation of LFs. Within DataSculpt, we incorporate an array of
+prompting techniques, instance selection strategies, and LF filtration methods
+to explore the expansive design landscape. Ultimately, we conduct a thorough
+assessment of DataSculpt's performance on 12 real-world datasets, encompassing
+a range of tasks. This evaluation unveils both the strengths and limitations of
+contemporary PLMs in LF design.
+"
+Prompting as Probing: Using Language Models for Knowledge Base  Construction,Dimitrios Alivanistos,http://arxiv.org/pdf/2208.11057v3.pdf,2022-08-23,"['cs.cl', 'cs.ai']",2208.11057v3.pdf,"  Language Models (LMs) have proven to be useful in various downstream
+applications, such as summarisation, translation, question answering and text
+classification. LMs are becoming increasingly important tools in Artificial
+Intelligence, because of the vast quantity of information they can store. In
+this work, we present ProP (Prompting as Probing), which utilizes GPT-3, a
+large Language Model originally proposed by OpenAI in 2020, to perform the task
+of Knowledge Base Construction (KBC). ProP implements a multi-step approach
+that combines a variety of prompting techniques to achieve this. Our results
+show that manual prompt curation is essential, that the LM must be encouraged
+to give answer sets of variable lengths, in particular including empty answer
+sets, that true/false questions are a useful device to increase precision on
+suggestions generated by the LM, that the size of the LM is a crucial factor,
+and that a dictionary of entity aliases improves the LM score. Our evaluation
+study indicates that these proposed techniques can substantially enhance the
+quality of the final predictions: ProP won track 2 of the LM-KBC competition,
+outperforming the baseline by 36.4 percentage points. Our implementation is
+available on https://github.com/HEmile/iswc-challenge.
+"
+Large Language Models are Pretty Good Zero-Shot Video Game Bug Detectors,Mohammad Reza Taesiri,http://arxiv.org/pdf/2210.02506v1.pdf,2022-10-05,"['cs.cl', 'cs.se']",2210.02506v1.pdf,"  Video game testing requires game-specific knowledge as well as common sense
+reasoning about the events in the game. While AI-driven agents can satisfy the
+first requirement, it is not yet possible to meet the second requirement
+automatically. Therefore, video game testing often still relies on manual
+testing, and human testers are required to play the game thoroughly to detect
+bugs. As a result, it is challenging to fully automate game testing. In this
+study, we explore the possibility of leveraging the zero-shot capabilities of
+large language models for video game bug detection. By formulating the bug
+detection problem as a question-answering task, we show that large language
+models can identify which event is buggy in a sequence of textual descriptions
+of events from a game. To this end, we introduce the GameBugDescriptions
+benchmark dataset, which consists of 167 buggy gameplay videos and a total of
+334 question-answer pairs across 8 games. We extensively evaluate the
+performance of six models across the OPT and InstructGPT large language model
+families on our benchmark dataset. Our results show promising results for
+employing language models to detect video game bugs. With the proper prompting
+technique, we could achieve an accuracy of 70.66%, and on some video games, up
+to 78.94%. Our code, evaluation data and the benchmark can be found on
+https://asgaardlab.github.io/LLMxBugs
+"
+Boosting Low-Data Instance Segmentation by Unsupervised Pre-training  with Saliency Prompt,Hao Li,http://arxiv.org/pdf/2302.01171v1.pdf,2023-02-02,"['cs.cv', 'cs.ai']",2302.01171v1.pdf,"  Recently, inspired by DETR variants, query-based end-to-end instance
+segmentation (QEIS) methods have outperformed CNN-based models on large-scale
+datasets. Yet they would lose efficacy when only a small amount of training
+data is available since it's hard for the crucial queries/kernels to learn
+localization and shape priors. To this end, this work offers a novel
+unsupervised pre-training solution for low-data regimes. Inspired by the recent
+success of the Prompting technique, we introduce a new pre-training method that
+boosts QEIS models by giving Saliency Prompt for queries/kernels. Our method
+contains three parts: 1) Saliency Masks Proposal is responsible for generating
+pseudo masks from unlabeled images based on the saliency mechanism. 2)
+Prompt-Kernel Matching transfers pseudo masks into prompts and injects the
+corresponding localization and shape priors to the best-matched kernels. 3)
+Kernel Supervision is applied to supply supervision at the kernel level for
+robust learning. From a practical perspective, our pre-training method helps
+QEIS models achieve a similar convergence speed and comparable performance with
+CNN-based models in low-data regimes. Experimental results show that our method
+significantly boosts several QEIS models on three datasets. Code will be made
+available.
+"
+One-Shot Labeling for Automatic Relevance Estimation,Sean MacAvaney,http://arxiv.org/pdf/2302.11266v2.pdf,2023-02-22,['cs.ir'],2302.11266v2.pdf,"  Dealing with unjudged documents (""holes"") in relevance assessments is a
+perennial problem when evaluating search systems with offline experiments.
+Holes can reduce the apparent effectiveness of retrieval systems during
+evaluation and introduce biases in models trained with incomplete data. In this
+work, we explore whether large language models can help us fill such holes to
+improve offline evaluations. We examine an extreme, albeit common, evaluation
+setting wherein only a single known relevant document per query is available
+for evaluation. We then explore various approaches for predicting the relevance
+of unjudged documents with respect to a query and the known relevant document,
+including nearest neighbor, supervised, and prompting techniques. We find that
+although the predictions of these One-Shot Labelers (1SL) frequently disagree
+with human assessments, the labels they produce yield a far more reliable
+ranking of systems than the single labels do alone. Specifically, the strongest
+approaches can consistently reach system ranking correlations of over 0.86 with
+the full rankings over a variety of measures. Meanwhile, the approach
+substantially increases the reliability of t-tests due to filling holes in
+relevance assessments, giving researchers more confidence in results they find
+to be significant. Alongside this work, we release an easy-to-use software
+package to enable the use of 1SL for evaluation of other ad-hoc collections or
+systems.
+"
+Are Large Language Models Ready for Healthcare? A Comparative Study on  Clinical Language Understanding,Yuqing Wang,http://arxiv.org/pdf/2304.05368v3.pdf,2023-04-09,"['cs.cl', 'cs.ai']",2304.05368v3.pdf,"  Large language models (LLMs) have made significant progress in various
+domains, including healthcare. However, the specialized nature of clinical
+language understanding tasks presents unique challenges and limitations that
+warrant further investigation. In this study, we conduct a comprehensive
+evaluation of state-of-the-art LLMs, namely GPT-3.5, GPT-4, and Bard, within
+the realm of clinical language understanding tasks. These tasks span a diverse
+range, including named entity recognition, relation extraction, natural
+language inference, semantic textual similarity, document classification, and
+question-answering. We also introduce a novel prompting strategy,
+self-questioning prompting (SQP), tailored to enhance LLMs' performance by
+eliciting informative questions and answers pertinent to the clinical scenarios
+at hand. Our evaluation underscores the significance of task-specific learning
+strategies and prompting techniques for improving LLMs' effectiveness in
+healthcare-related tasks. Additionally, our in-depth error analysis on the
+challenging relation extraction task offers valuable insights into error
+distribution and potential avenues for improvement using SQP. Our study sheds
+light on the practical implications of employing LLMs in the specialized domain
+of healthcare, serving as a foundation for future research and the development
+of potential applications in healthcare settings.
+"
+Multi-Prompt with Depth Partitioned Cross-Modal Learning,Yingjie Tian,http://arxiv.org/pdf/2305.06221v3.pdf,2023-05-10,"['cs.cv', 'cs.ai']",2305.06221v3.pdf,"  In recent years, soft prompt learning methods have been proposed to fine-tune
+large-scale vision-language pre-trained models for various downstream tasks.
+These methods typically combine learnable textual tokens with class tokens as
+input for models with frozen parameters. However, they often employ a single
+prompt to describe class contexts, failing to capture categories' diverse
+attributes adequately. This study introduces the Partitioned Multi-modal Prompt
+(PMPO), a multi-modal prompting technique that extends the soft prompt from a
+single learnable prompt to multiple prompts. Our method divides the visual
+encoder depths and connects learnable prompts to the separated visual depths,
+enabling different prompts to capture the hierarchical contextual depths of
+visual representations. Furthermore, to maximize the advantages of multi-prompt
+learning, we incorporate prior information from manually designed templates and
+learnable multi-prompts, thus improving the generalization capabilities of our
+approach. We evaluate the effectiveness of our approach on three challenging
+tasks: new class generalization, cross-dataset evaluation, and domain
+generalization. For instance, our method achieves a $79.28$ harmonic mean,
+averaged over 11 diverse image recognition datasets ($+7.62$ compared to CoOp),
+demonstrating significant competitiveness compared to state-of-the-art
+prompting methods.
+"
+ONCE: Boosting Content-based Recommendation with Both Open- and  Closed-source Large Language Models,Qijiong Liu,http://arxiv.org/pdf/2305.06566v4.pdf,2023-05-11,"['cs.ir', 'cs.cl']",2305.06566v4.pdf,"  Personalized content-based recommender systems have become indispensable
+tools for users to navigate through the vast amount of content available on
+platforms like daily news websites and book recommendation services. However,
+existing recommenders face significant challenges in understanding the content
+of items. Large language models (LLMs), which possess deep semantic
+comprehension and extensive knowledge from pretraining, have proven to be
+effective in various natural language processing tasks. In this study, we
+explore the potential of leveraging both open- and closed-source LLMs to
+enhance content-based recommendation. With open-source LLMs, we utilize their
+deep layers as content encoders, enriching the representation of content at the
+embedding level. For closed-source LLMs, we employ prompting techniques to
+enrich the training data at the token level. Through comprehensive experiments,
+we demonstrate the high effectiveness of both types of LLMs and show the
+synergistic relationship between them. Notably, we observed a significant
+relative improvement of up to 19.32% compared to existing state-of-the-art
+recommendation models. These findings highlight the immense potential of both
+open- and closed-source of LLMs in enhancing content-based recommendation
+systems. We will make our code and LLM-generated data available for other
+researchers to reproduce our results.
+"
+OPT-R: Exploring the Role of Explanations in Finetuning and Prompting  for Reasoning Skills of Large Language Models,Badr AlKhamissi,http://arxiv.org/pdf/2305.12001v2.pdf,2023-05-19,['cs.cl'],2305.12001v2.pdf,"  In this paper, we conduct a thorough investigation into the reasoning
+capabilities of Large Language Models (LLMs), focusing specifically on the Open
+Pretrained Transformers (OPT) models as a representative of such models. Our
+study entails finetuning three different sizes of OPT on a carefully curated
+reasoning corpus, resulting in two sets of finetuned models: OPT-R, finetuned
+without explanations, and OPT-RE, finetuned with explanations. We then evaluate
+all models on 57 out-of-domain tasks drawn from the SUPER-NATURALINSTRUCTIONS
+benchmark, covering 26 distinct reasoning skills, utilizing three prompting
+techniques. Through a comprehensive grid of 27 configurations and 6,156 test
+evaluations, we investigate the dimensions of finetuning, prompting, and scale
+to understand the role of explanations on different reasoning skills. Our
+findings reveal that having explanations in the fewshot exemplar has no
+significant impact on the model's performance when the model is finetuned,
+while positively affecting the non-finetuned counterpart. Moreover, we observe
+a slight yet consistent increase in classification accuracy as we incorporate
+explanations during prompting and finetuning, respectively. Finally, we offer
+insights on which skills benefit the most from incorporating explanations
+during finetuning and prompting, such as Numerical (+20.4%) and Analogical
+(+13.9%) reasoning, as well as skills that exhibit negligible or negative
+effects.
+"
+The Utility of Large Language Models and Generative AI for Education  Research,Andrew Katz,http://arxiv.org/pdf/2305.18125v1.pdf,2023-05-29,['cs.hc'],2305.18125v1.pdf,"  The use of natural language processing (NLP) techniques in engineering
+education can provide valuable insights into the underlying processes involved
+in generating text. While accessing these insights can be labor-intensive if
+done manually, recent advances in NLP and large language models have made it a
+realistic option for individuals. This study explores and evaluates a
+combination of clustering, summarization, and prompting techniques to analyze
+over 1,000 student essays in which students discussed their career interests.
+The specific assignment prompted students to define and explain their career
+goals as engineers. Using text embedding representations of student responses,
+we clustered the responses together to identify thematically similar statements
+from students. The clustered responses were then summarized to quickly identify
+career interest themes. We also used a set of a priori codes about career
+satisfaction and sectors to demonstrate an alternative approach to using these
+generative text models to analyze student writing. The results of this study
+demonstrate the feasibility and usefulness of NLP techniques in engineering
+education research. By automating the initial analysis of student essays,
+researchers and educators can more efficiently and accurately identify key
+themes and patterns in student writing. The methods presented in this paper
+have broader applications for engineering education and research purposes
+beyond analyzing student essays. By explaining these methods to the engineering
+education community, readers can utilize them in their own contexts.
+"
+Fine-Grained Visual Prompting,Lingfeng Yang,http://arxiv.org/pdf/2306.04356v1.pdf,2023-06-07,['cs.cv'],2306.04356v1.pdf,"  Vision-Language Models (VLMs), such as CLIP, have demonstrated impressive
+zero-shot transfer capabilities in image-level visual perception. However,
+these models have shown limited performance in instance-level tasks that demand
+precise localization and recognition. Previous works have suggested that
+incorporating visual prompts, such as colorful boxes or circles, can improve
+the ability of models to recognize objects of interest. Nonetheless, compared
+to language prompting, visual prompting designs are rarely explored. Existing
+approaches, which employ coarse visual cues such as colorful boxes or circles,
+often result in sub-optimal performance due to the inclusion of irrelevant and
+noisy pixels. In this paper, we carefully study the visual prompting designs by
+exploring more fine-grained markings, such as segmentation masks and their
+variations. In addition, we introduce a new zero-shot framework that leverages
+pixel-level annotations acquired from a generalist segmentation model for
+fine-grained visual prompting. Consequently, our investigation reveals that a
+straightforward application of blur outside the target mask, referred to as the
+Blur Reverse Mask, exhibits exceptional effectiveness. This proposed prompting
+strategy leverages the precise mask annotations to reduce focus on weakly
+related regions while retaining spatial coherence between the target and the
+surrounding background. Our Fine-Grained Visual Prompting (FGVP) demonstrates
+superior performance in zero-shot comprehension of referring expressions on the
+RefCOCO, RefCOCO+, and RefCOCOg benchmarks. It outperforms prior methods by an
+average margin of 3.0% to 4.6%, with a maximum improvement of 12.5% on the
+RefCOCO+ testA subset. The part detection experiments conducted on the PACO
+dataset further validate the preponderance of FGVP over existing visual
+prompting techniques. Code and models will be made available.
+"
+The FormAI Dataset: Generative AI in Software Security Through the Lens  of Formal Verification,Norbert Tihanyi,http://arxiv.org/pdf/2307.02192v2.pdf,2023-07-05,"['cs.db', 'cs.ai']",2307.02192v2.pdf,"  This paper presents the FormAI dataset, a large collection of 112, 000
+AI-generated compilable and independent C programs with vulnerability
+classification. We introduce a dynamic zero-shot prompting technique
+constructed to spawn diverse programs utilizing Large Language Models (LLMs).
+The dataset is generated by GPT-3.5-turbo and comprises programs with varying
+levels of complexity. Some programs handle complicated tasks like network
+management, table games, or encryption, while others deal with simpler tasks
+like string manipulation. Every program is labeled with the vulnerabilities
+found within the source code, indicating the type, line number, and vulnerable
+function name. This is accomplished by employing a formal verification method
+using the Efficient SMT-based Bounded Model Checker (ESBMC), which uses model
+checking, abstract interpretation, constraint programming, and satisfiability
+modulo theories to reason over safety/security properties in programs. This
+approach definitively detects vulnerabilities and offers a formal model known
+as a counterexample, thus eliminating the possibility of generating false
+positive reports. We have associated the identified vulnerabilities with Common
+Weakness Enumeration (CWE) numbers. We make the source code available for the
+112, 000 programs, accompanied by a separate file containing the
+vulnerabilities detected in each program, making the dataset ideal for training
+LLMs and machine learning algorithms. Our study unveiled that according to
+ESBMC, 51.24% of the programs generated by GPT-3.5 contained vulnerabilities,
+thereby presenting considerable risks to software safety and security.
+"
+SciGraphQA: A Large-Scale Synthetic Multi-Turn Question-Answering  Dataset for Scientific Graphs,Shengzhi Li,http://arxiv.org/pdf/2308.03349v1.pdf,2023-08-07,"['cs.cl', 'cs.ai', 'cs.cv']",2308.03349v1.pdf,"  In this work, we present SciGraphQA, a synthetic multi-turn question-answer
+dataset related to academic graphs. SciGraphQA is 13 times larger than
+ChartVQA, the previously largest chart-visual question-answering dataset. It is
+also the largest open-sourced chart VQA dataset with non-synthetic charts. To
+build our dataset, we selected 290,000 Computer Science or Machine Learning
+ArXiv papers published between 2010 and 2020, and then used Palm-2 to generate
+295K samples of open-vocabulary multi-turn question-answering dialogues about
+the graphs. As context, we provided the text-only Palm-2 with paper title,
+abstract, paragraph mentioning the graph, and rich text contextual data from
+the graph itself, obtaining dialogues with an average 2.23 question-answer
+turns for each graph. We asked GPT-4 to assess the matching quality of our
+question-answer turns given the paper's context, obtaining an average rating of
+8.7/10 on our 3K test set. We evaluated the 0-shot capability of the most
+popular MLLM models such as LLaVa, mPLUGowl, BLIP-2, and openFlamingo's on our
+dataset, finding LLaVA-13B being the most performant with a CIDEr score of
+0.08. We further enriched the question prompts for LLAVA by including the
+serialized data tables extracted from the graphs using the DePlot model,
+boosting LLaVA's 0-shot CIDEr to 0.15. To verify the validity of our dataset,
+we also fine-tuned LLaVa using our dataset, reaching a substantially higher
+CIDEr score of 0.26. We anticipate further accuracy improvement by including
+segmentation mask tokens and leveraging larger LLM backbones coupled with
+emergent prompting techniques. Our code and data are open-sourced.
+"
+GOPro: Generate and Optimize Prompts in CLIP using Self-Supervised  Learning,Mainak Singha,http://arxiv.org/pdf/2308.11605v1.pdf,2023-08-22,['cs.cv'],2308.11605v1.pdf,"  Large-scale foundation models, such as CLIP, have demonstrated remarkable
+success in visual recognition tasks by embedding images in a semantically rich
+space. Self-supervised learning (SSL) has also shown promise in improving
+visual recognition by learning invariant features. However, the combination of
+CLIP with SSL is found to face challenges due to the multi-task framework that
+blends CLIP's contrastive loss and SSL's loss, including difficulties with loss
+weighting and inconsistency among different views of images in CLIP's output
+space. To overcome these challenges, we propose a prompt learning-based model
+called GOPro, which is a unified framework that ensures similarity between
+various augmented views of input images in a shared image-text embedding space,
+using a pair of learnable image and text projectors atop CLIP, to promote
+invariance and generalizability. To automatically learn such prompts, we
+leverage the visual content and style primitives extracted from pre-trained
+CLIP and adapt them to the target task. In addition to CLIP's cross-domain
+contrastive loss, we introduce a visual contrastive loss and a novel prompt
+consistency loss, considering the different views of the images. GOPro is
+trained end-to-end on all three loss objectives, combining the strengths of
+CLIP and SSL in a principled manner. Empirical evaluations demonstrate that
+GOPro outperforms the state-of-the-art prompting techniques on three
+challenging domain generalization tasks across multiple benchmarks by a
+significant margin. Our code is available at
+https://github.com/mainaksingha01/GOPro.
+"
+Spoken Language Intelligence of Large Language Models for Language  Learning,Linkai Peng,http://arxiv.org/pdf/2308.14536v1.pdf,2023-08-28,"['cs.cl', 'cs.ai', 'cs.lg', 'cs.sd', 'eess.as']",2308.14536v1.pdf,"  People have long hoped for a conversational system that can assist in
+real-life situations, and recent progress on large language models (LLMs) is
+bringing this idea closer to reality. While LLMs are often impressive in
+performance, their efficacy in real-world scenarios that demand expert
+knowledge remains unclear. LLMs are believed to hold the most potential and
+value in education, especially in the development of Artificial intelligence
+(AI) based virtual teachers capable of facilitating language learning. Our
+focus is centered on evaluating the efficacy of LLMs in the realm of education,
+specifically in the areas of spoken language learning which encompass
+phonetics, phonology, and second language acquisition. We introduce a new
+multiple-choice question dataset to evaluate the effectiveness of LLMs in the
+aforementioned scenarios, including understanding and application of spoken
+language knowledge. In addition, we investigate the influence of various
+prompting techniques such as zero- and few-shot method (prepending the question
+with question-answer exemplars), chain-of-thought (CoT, think step-by-step),
+in-domain exampler and external tools (Google, Wikipedia). We conducted
+large-scale evaluation on popular LLMs (20 distinct models) using these
+methods. We achieved significant performance improvements compared to the
+zero-shot baseline in the practical questions reasoning (GPT-3.5, 49.1% ->
+63.1%; LLaMA2-70B-Chat, 42.2% -> 48.6%). We found that models of different
+sizes have good understanding of concepts in phonetics, phonology, and second
+language acquisition, but show limitations in reasoning for real-world
+problems. Additionally, we also explore preliminary findings on conversational
+communication.
+"
+Are Emergent Abilities in Large Language Models just In-Context  Learning?,Sheng Lu,http://arxiv.org/pdf/2309.01809v1.pdf,2023-09-04,['cs.cl'],2309.01809v1.pdf,"  Large language models have exhibited emergent abilities, demonstrating
+exceptional performance across diverse tasks for which they were not explicitly
+trained, including those that require complex reasoning abilities. The
+emergence of such abilities carries profound implications for the future
+direction of research in NLP, especially as the deployment of such models
+becomes more prevalent. However, one key challenge is that the evaluation of
+these abilities is often confounded by competencies that arise in models
+through alternative prompting techniques, such as in-context learning and
+instruction following, which also emerge as the models are scaled up. In this
+study, we provide the first comprehensive examination of these emergent
+abilities while accounting for various potentially biasing factors that can
+influence the evaluation of models. We conduct rigorous tests on a set of 18
+models, encompassing a parameter range from 60 million to 175 billion
+parameters, across a comprehensive set of 22 tasks. Through an extensive series
+of over 1,000 experiments, we provide compelling evidence that emergent
+abilities can primarily be ascribed to in-context learning. We find no evidence
+for the emergence of reasoning abilities, thus providing valuable insights into
+the underlying mechanisms driving the observed abilities and thus alleviating
+safety concerns regarding their use.
+"
+Unsupervised Contrast-Consistent Ranking with Language Models,Niklas Stoehr,http://arxiv.org/pdf/2309.06991v1.pdf,2023-09-13,"['cs.lg', 'cs.cl', 'stat.ml']",2309.06991v1.pdf,"  Language models contain ranking-based knowledge and are powerful solvers of
+in-context ranking tasks. For instance, they may have parametric knowledge
+about the ordering of countries by size or may be able to rank reviews by
+sentiment. Recent work focuses on pairwise, pointwise, and listwise prompting
+techniques to elicit a language model's ranking knowledge. However, we find
+that even with careful calibration and constrained decoding, prompting-based
+techniques may not always be self-consistent in the rankings they produce. This
+motivates us to explore an alternative approach that is inspired by an
+unsupervised probing method called Contrast-Consistent Search (CCS). The idea
+is to train a probing model guided by a logical constraint: a model's
+representation of a statement and its negation must be mapped to contrastive
+true-false poles consistently across multiple statements. We hypothesize that
+similar constraints apply to ranking tasks where all items are related via
+consistent pairwise or listwise comparisons. To this end, we extend the binary
+CCS method to Contrast-Consistent Ranking (CCR) by adapting existing ranking
+methods such as the Max-Margin Loss, Triplet Loss, and Ordinal Regression
+objective. Our results confirm that, for the same language model, CCR probing
+outperforms prompting and even performs on a par with prompting much larger
+language models.
+"
+S3-DST: Structured Open-Domain Dialogue Segmentation and State Tracking  in the Era of LLMs,Sarkar Snigdha Sarathi Das,http://arxiv.org/pdf/2309.08827v1.pdf,2023-09-16,"['cs.cl', 'cs.ai']",2309.08827v1.pdf,"  The traditional Dialogue State Tracking (DST) problem aims to track user
+preferences and intents in user-agent conversations. While sufficient for
+task-oriented dialogue systems supporting narrow domain applications, the
+advent of Large Language Model (LLM)-based chat systems has introduced many
+real-world intricacies in open-domain dialogues. These intricacies manifest in
+the form of increased complexity in contextual interactions, extended dialogue
+sessions encompassing a diverse array of topics, and more frequent contextual
+shifts. To handle these intricacies arising from evolving LLM-based chat
+systems, we propose joint dialogue segmentation and state tracking per segment
+in open-domain dialogue systems. Assuming a zero-shot setting appropriate to a
+true open-domain dialogue system, we propose S3-DST, a structured prompting
+technique that harnesses Pre-Analytical Recollection, a novel grounding
+mechanism we designed for improving long context tracking. To demonstrate the
+efficacy of our proposed approach in joint segmentation and state tracking, we
+evaluate S3-DST on a proprietary anonymized open-domain dialogue dataset, as
+well as publicly available DST and segmentation datasets. Across all datasets
+and settings, S3-DST consistently outperforms the state-of-the-art,
+demonstrating its potency and robustness the next generation of LLM-based chat
+systems.
+"
+Scalable Multi-Robot Collaboration with Large Language Models:  Centralized or Decentralized Systems?,Yongchao Chen,http://arxiv.org/pdf/2309.15943v1.pdf,2023-09-27,['cs.ro'],2309.15943v1.pdf,"  A flurry of recent work has demonstrated that pre-trained large language
+models (LLMs) can be effective task planners for a variety of single-robot
+tasks. The planning performance of LLMs is significantly improved via prompting
+techniques, such as in-context learning or re-prompting with state feedback,
+placing new importance on the token budget for the context window. An
+under-explored but natural next direction is to investigate LLMs as multi-robot
+task planners. However, long-horizon, heterogeneous multi-robot planning
+introduces new challenges of coordination while also pushing up against the
+limits of context window length. It is therefore critical to find
+token-efficient LLM planning frameworks that are also able to reason about the
+complexities of multi-robot coordination. In this work, we compare the task
+success rate and token efficiency of four multi-agent communication frameworks
+(centralized, decentralized, and two hybrid) as applied to four
+coordination-dependent multi-agent 2D task scenarios for increasing numbers of
+agents. We find that a hybrid framework achieves better task success rates
+across all four tasks and scales better to more agents. We further demonstrate
+the hybrid frameworks in 3D simulations where the vision-to-text problem and
+dynamical errors are considered. See our project website
+https://yongchao98.github.io/MIT-REALM-Multi-Robot/ for prompts, videos, and
+code.
+"
+Adaptive-Solver Framework for Dynamic Strategy Selection in Large  Language Model Reasoning,Jianpeng Zhou,http://arxiv.org/pdf/2310.01446v1.pdf,2023-10-01,"['cs.cl', 'cs.ai']",2310.01446v1.pdf,"  Large Language Models (LLMs) are showcasing impressive ability in handling
+complex reasoning tasks. In real-world situations, problems often span a
+spectrum of complexities. Humans inherently adjust their problem-solving
+approaches based on task complexity. However, most methodologies that leverage
+LLMs tend to adopt a uniform approach: utilizing consistent models, prompting
+methods, and degrees of problem decomposition, regardless of the problem
+complexity. Inflexibility of them can bring unnecessary computational overhead
+or sub-optimal performance. To address this problem, we introduce an
+Adaptive-Solver framework. It strategically modulates solving strategies based
+on the difficulties of the problems. Given an initial solution, the framework
+functions with two primary modules. The initial evaluation module assesses the
+adequacy of the current solution. If improvements are needed, the subsequent
+adaptation module comes into play. Within this module, three key adaptation
+strategies are employed: (1) Model Adaptation: Switching to a stronger LLM when
+a weaker variant is inadequate. (2) Prompting Method Adaptation: Alternating
+between different prompting techniques to suit the problem's nuances. (3)
+Decomposition Granularity Adaptation: Breaking down a complex problem into more
+fine-grained sub-questions to enhance solvability. Through such dynamic
+adaptations, our framework not only enhances computational efficiency but also
+elevates the overall performance. This dual-benefit ensures both the efficiency
+of the system for simpler tasks and the precision required for more complex
+questions. Experimental results from complex reasoning tasks reveal that the
+prompting method adaptation and decomposition granularity adaptation enhance
+performance across all tasks. Furthermore, the model adaptation approach
+significantly reduces API costs (up to 50%) while maintaining superior
+performance.
+"
+Revisiting Large Language Models as Zero-shot Relation Extractors,Guozheng Li,http://arxiv.org/pdf/2310.05028v3.pdf,2023-10-08,"['cs.ai', 'cs.cl']",2310.05028v3.pdf,"  Relation extraction (RE) consistently involves a certain degree of labeled or
+unlabeled data even if under zero-shot setting. Recent studies have shown that
+large language models (LLMs) transfer well to new tasks out-of-the-box simply
+given a natural language prompt, which provides the possibility of extracting
+relations from text without any data and parameter tuning. This work focuses on
+the study of exploring LLMs, such as ChatGPT, as zero-shot relation extractors.
+On the one hand, we analyze the drawbacks of existing RE prompts and attempt to
+incorporate recent prompt techniques such as chain-of-thought (CoT) to improve
+zero-shot RE. We propose the summarize-and-ask (\textsc{SumAsk}) prompting, a
+simple prompt recursively using LLMs to transform RE inputs to the effective
+question answering (QA) format. On the other hand, we conduct comprehensive
+experiments on various benchmarks and settings to investigate the capabilities
+of LLMs on zero-shot RE. Specifically, we have the following findings: (i)
+\textsc{SumAsk} consistently and significantly improves LLMs performance on
+different model sizes, benchmarks and settings; (ii) Zero-shot prompting with
+ChatGPT achieves competitive or superior results compared with zero-shot and
+fully supervised methods; (iii) LLMs deliver promising performance in
+extracting overlapping relations; (iv) The performance varies greatly regarding
+different relations. Different from small language models, LLMs are effective
+in handling challenge none-of-the-above (NoTA) relation.
+"
+Towards Training-free Open-world Segmentation via Image Prompting  Foundation Models,Lv Tang,http://arxiv.org/pdf/2310.10912v1.pdf,2023-10-17,['cs.cv'],2310.10912v1.pdf,"  The realm of computer vision has witnessed a paradigm shift with the advent
+of foundational models, mirroring the transformative influence of large
+language models in the domain of natural language processing. This paper delves
+into the exploration of open-world segmentation, presenting a novel approach
+called Image Prompt Segmentation (IPSeg) that harnesses the power of vision
+foundational models. At the heart of IPSeg lies the principle of a
+training-free paradigm, which capitalizes on image prompting techniques. IPSeg
+utilizes a single image containing a subjective visual concept as a flexible
+prompt to query vision foundation models like DINOv2 and Stable Diffusion. Our
+approach extracts robust features for the prompt image and input image, then
+matches the input representations to the prompt representations via a novel
+feature interaction module to generate point prompts highlighting target
+objects in the input image. The generated point prompts are further utilized to
+guide the Segment Anything Model to segment the target object in the input
+image. The proposed method stands out by eliminating the need for exhaustive
+training sessions, thereby offering a more efficient and scalable solution.
+Experiments on COCO, PASCAL VOC, and other datasets demonstrate IPSeg's
+efficacy for flexible open-world segmentation using intuitive image prompts.
+This work pioneers tapping foundation models for open-world understanding
+through visual concepts conveyed in images.
+"
+Cross-lingual Prompting: Improving Zero-shot Chain-of-Thought Reasoning  across Languages,Libo Qin,http://arxiv.org/pdf/2310.14799v1.pdf,2023-10-23,"['cs.cl', 'cs.ai']",2310.14799v1.pdf,"  Chain-of-thought (CoT) is capable of eliciting models to explicitly generate
+reasoning paths, thus promoting reasoning accuracy and attracting increasing
+attention. Specifically, zero-shot CoT achieves remarkable improvements in a
+wide range of reasoning tasks by simply instructing the LLM with the prompt
+""Let's think step by step!"". Despite the success of zero-shot CoT, the existing
+zero-shot prompting techniques remain limited to a single language, making it
+challenging to generalize to other languages and hindering global development.
+In this work, we introduce cross-lingual prompting (CLP), aiming to improve
+zero-shot CoT reasoning across languages. Specifically, CLP consists of two
+main components: (1) cross-lingual alignment prompting and (2) task-specific
+solver prompting. The cross-lingual alignment prompting is responsible for
+aligning representations across different languages, whereas the task-specific
+solver prompting is used to generate the final chain of thoughts and results
+for the reasoning task. In addition, we further introduce cross-lingual
+self-consistent prompting (CLSP) to ensemble different reasoning paths across
+languages. Our experimental evaluations on several benchmarks demonstrate that
+CLP and CLSP significantly outperform the existing prompting methods and
+achieve state-of-the-art performance. We hope this work will inspire further
+breakthroughs in cross-lingual CoT.
+"
+HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained  Heterogeneous Graph Neural Networks,Yihong Ma,http://arxiv.org/pdf/2310.15318v1.pdf,2023-10-23,"['cs.lg', 'cs.ai']",2310.15318v1.pdf,"  Graphs have emerged as a natural choice to represent and analyze the
+intricate patterns and rich information of the Web, enabling applications such
+as online page classification and social recommendation. The prevailing
+""pre-train, fine-tune"" paradigm has been widely adopted in graph machine
+learning tasks, particularly in scenarios with limited labeled nodes. However,
+this approach often exhibits a misalignment between the training objectives of
+pretext tasks and those of downstream tasks. This gap can result in the
+""negative transfer"" problem, wherein the knowledge gained from pre-training
+adversely affects performance in the downstream tasks. The surge in
+prompt-based learning within Natural Language Processing (NLP) suggests the
+potential of adapting a ""pre-train, prompt"" paradigm to graphs as an
+alternative. However, existing graph prompting techniques are tailored to
+homogeneous graphs, neglecting the inherent heterogeneity of Web graphs. To
+bridge this gap, we propose HetGPT, a general post-training prompting framework
+to improve the predictive performance of pre-trained heterogeneous graph neural
+networks (HGNNs). The key is the design of a novel prompting function that
+integrates a virtual class prompt and a heterogeneous feature prompt, with the
+aim to reformulate downstream tasks to mirror pretext tasks. Moreover, HetGPT
+introduces a multi-view neighborhood aggregation mechanism, capturing the
+complex neighborhood structure in heterogeneous graphs. Extensive experiments
+on three benchmark datasets demonstrate HetGPT's capability to enhance the
+performance of state-of-the-art HGNNs on semi-supervised node classification.
+"
+Videoprompter: an ensemble of foundational models for zero-shot video  understanding,Adeel Yousaf,http://arxiv.org/pdf/2310.15324v1.pdf,2023-10-23,['cs.cv'],2310.15324v1.pdf,"  Vision-language models (VLMs) classify the query video by calculating a
+similarity score between the visual features and text-based class label
+representations. Recently, large language models (LLMs) have been used to
+enrich the text-based class labels by enhancing the descriptiveness of the
+class names. However, these improvements are restricted to the text-based
+classifier only, and the query visual features are not considered. In this
+paper, we propose a framework which combines pre-trained discriminative VLMs
+with pre-trained generative video-to-text and text-to-text models. We introduce
+two key modifications to the standard zero-shot setting. First, we propose
+language-guided visual feature enhancement and employ a video-to-text model to
+convert the query video to its descriptive form. The resulting descriptions
+contain vital visual cues of the query video, such as what objects are present
+and their spatio-temporal interactions. These descriptive cues provide
+additional semantic knowledge to VLMs to enhance their zeroshot performance.
+Second, we propose video-specific prompts to LLMs to generate more meaningful
+descriptions to enrich class label representations. Specifically, we introduce
+prompt techniques to create a Tree Hierarchy of Categories for class names,
+offering a higher-level action context for additional visual cues, We
+demonstrate the effectiveness of our approach in video understanding across
+three different zero-shot settings: 1) video action recognition, 2)
+video-to-text and textto-video retrieval, and 3) time-sensitive video tasks.
+Consistent improvements across multiple benchmarks and with various VLMs
+demonstrate the effectiveness of our proposed framework. Our code will be made
+publicly available.
+"
+Improving Diversity of Demographic Representation in Large Language  Models via Collective-Critiques and Self-Voting,Preethi Lahoti,http://arxiv.org/pdf/2310.16523v1.pdf,2023-10-25,"['cs.cl', 'cs.ai']",2310.16523v1.pdf,"  A crucial challenge for generative large language models (LLMs) is diversity:
+when a user's prompt is under-specified, models may follow implicit assumptions
+while generating a response, which may result in homogenization of the
+responses, as well as certain demographic groups being under-represented or
+even erased from the generated responses. In this paper, we formalize diversity
+of representation in generative LLMs. We present evaluation datasets and
+propose metrics to measure diversity in generated responses along people and
+culture axes. We find that LLMs understand the notion of diversity, and that
+they can reason and critique their own responses for that goal. This finding
+motivated a new prompting technique called collective-critique and self-voting
+(CCSV) to self-improve people diversity of LLMs by tapping into its diversity
+reasoning capabilities, without relying on handcrafted examples or prompt
+tuning. Extensive empirical experiments with both human and automated
+evaluations show that our proposed approach is effective at improving people
+and culture diversity, and outperforms all baseline methods by a large margin.
+"
+LLM4DyG: Can Large Language Models Solve Problems on Dynamic Graphs?,Zeyang Zhang,http://arxiv.org/pdf/2310.17110v1.pdf,2023-10-26,['cs.lg'],2310.17110v1.pdf,"  In an era marked by the increasing adoption of Large Language Models (LLMs)
+for various tasks, there is a growing focus on exploring LLMs' capabilities in
+handling web data, particularly graph data. Dynamic graphs, which capture
+temporal network evolution patterns, are ubiquitous in real-world web data.
+Evaluating LLMs' competence in understanding spatial-temporal information on
+dynamic graphs is essential for their adoption in web applications, which
+remains unexplored in the literature. In this paper, we bridge the gap via
+proposing to evaluate LLMs' spatial-temporal understanding abilities on dynamic
+graphs, to the best of our knowledge, for the first time. Specifically, we
+propose the LLM4DyG benchmark, which includes nine specially designed tasks
+considering the capability evaluation of LLMs from both temporal and spatial
+dimensions. Then, we conduct extensive experiments to analyze the impacts of
+different data generators, data statistics, prompting techniques, and LLMs on
+the model performance. Finally, we propose Disentangled Spatial-Temporal
+Thoughts (DST2) for LLMs on dynamic graphs to enhance LLMs' spatial-temporal
+understanding abilities. Our main observations are: 1) LLMs have preliminary
+spatial-temporal understanding abilities on dynamic graphs, 2) Dynamic graph
+tasks show increasing difficulties for LLMs as the graph size and density
+increase, while not sensitive to the time span and data generation mechanism,
+3) the proposed DST2 prompting method can help to improve LLMs'
+spatial-temporal understanding abilities on dynamic graphs for most tasks. The
+data and codes will be open-sourced at publication time.
+"
+Which is better? Exploring Prompting Strategy For LLM-based Metrics,Joonghoon Kim,http://arxiv.org/pdf/2311.03754v1.pdf,2023-11-07,['cs.cl'],2311.03754v1.pdf,"  This paper describes the DSBA submissions to the Prompting Large Language
+Models as Explainable Metrics shared task, where systems were submitted to two
+tracks: small and large summarization tracks. With advanced Large Language
+Models (LLMs) such as GPT-4, evaluating the quality of Natural Language
+Generation (NLG) has become increasingly paramount. Traditional
+similarity-based metrics such as BLEU and ROUGE have shown to misalign with
+human evaluation and are ill-suited for open-ended generation tasks. To address
+this issue, we explore the potential capability of LLM-based metrics,
+especially leveraging open-source LLMs. In this study, wide range of prompts
+and prompting techniques are systematically analyzed with three approaches:
+prompting strategy, score aggregation, and explainability. Our research focuses
+on formulating effective prompt templates, determining the granularity of NLG
+quality scores and assessing the impact of in-context examples on LLM-based
+evaluation. Furthermore, three aggregation strategies are compared to identify
+the most reliable method for aggregating NLG quality scores. To examine
+explainability, we devise a strategy that generates rationales for the scores
+and analyzes the characteristics of the explanation produced by the open-source
+LLMs. Extensive experiments provide insights regarding evaluation capabilities
+of open-source LLMs and suggest effective prompting strategies.
+"
+Understanding and Improving Visual Prompting: A Label-Mapping  Perspective,Aochuan Chen,http://arxiv.org/pdf/2211.11635v5.pdf,2022-11-21,['cs.cv'],2211.11635v5.pdf,"  We revisit and advance visual prompting (VP), an input prompting technique
+for vision tasks. VP can reprogram a fixed, pre-trained source model to
+accomplish downstream tasks in the target domain by simply incorporating
+universal prompts (in terms of input perturbation patterns) into downstream
+data points. Yet, it remains elusive why VP stays effective even given a
+ruleless label mapping (LM) between the source classes and the target classes.
+Inspired by the above, we ask: How is LM interrelated with VP? And how to
+exploit such a relationship to improve its accuracy on target tasks? We peer
+into the influence of LM on VP and provide an affirmative answer that a better
+'quality' of LM (assessed by mapping precision and explanation) can
+consistently improve the effectiveness of VP. This is in contrast to the prior
+art where the factor of LM was missing. To optimize LM, we propose a new VP
+framework, termed ILM-VP (iterative label mapping-based visual prompting),
+which automatically re-maps the source labels to the target labels and
+progressively improves the target task accuracy of VP. Further, when using a
+contrastive language-image pretrained (CLIP) model, we propose to integrate an
+LM process to assist the text prompt selection of CLIP and to improve the
+target task accuracy. Extensive experiments demonstrate that our proposal
+significantly outperforms state-of-the-art VP methods. As highlighted below, we
+show that when reprogramming an ImageNet-pretrained ResNet-18 to 13 target
+tasks, our method outperforms baselines by a substantial margin, e.g., 7.9% and
+6.7% accuracy improvements in transfer learning to the target Flowers102 and
+CIFAR100 datasets. Besides, our proposal on CLIP-based VP provides 13.7% and
+7.1% accuracy improvements on Flowers102 and DTD respectively. Our code is
+available at https://github.com/OPTML-Group/ILM-VP.
+"
+The Power of Large Language Models for Wireless Communication System  Development: A Case Study on FPGA Platforms,Yuyang Du,http://arxiv.org/pdf/2307.07319v4.pdf,2023-07-14,['eess.sp'],2307.07319v4.pdf,"  Large language models (LLMs) have garnered significant attention across
+various research disciplines, including the wireless communication community.
+There have been several heated discussions on the intersection of LLMs and
+wireless technologies. While recent studies have demonstrated the ability of
+LLMs to generate hardware description language (HDL) code for simple
+computation tasks, developing wireless prototypes and products via HDL poses
+far greater challenges because of the more complex computation tasks involved.
+In this paper, we aim to address this challenge by investigating the role of
+LLMs in FPGA-based hardware development for advanced wireless signal
+processing. We begin by exploring LLM-assisted code refactoring, reuse, and
+validation, using an open-source software-defined radio (SDR) project as a case
+study. Through the case study, we find that an LLM assistant can potentially
+yield substantial productivity gains for researchers and developers. We then
+examine the feasibility of using LLMs to generate HDL code for advanced
+wireless signal processing, using the Fast Fourier Transform (FFT) algorithm as
+an example. This task presents two unique challenges: the scheduling of
+subtasks within the overall task and the multi-step thinking required to solve
+certain arithmetic problem within the task. To address these challenges, we
+employ in-context learning (ICL) and Chain-of-Thought (CoT) prompting
+techniques, culminating in the successful generation of a 64-point Verilog FFT
+module. Our results demonstrate the potential of LLMs for generalization and
+imitation, affirming their usefulness in writing HDL code for wireless
+communication systems. Overall, this work contributes to understanding the role
+of LLMs in wireless communication and motivates further exploration of their
+capabilities.
+"
+Foundation Metrics: Quantifying Effectiveness of Healthcare  Conversations powered by Generative AI,Mahyar Abbasian,http://arxiv.org/pdf/2309.12444v2.pdf,2023-09-21,['cs.cl'],2309.12444v2.pdf,"  Generative Artificial Intelligence is set to revolutionize healthcare
+delivery by transforming traditional patient care into a more personalized,
+efficient, and proactive process. Chatbots, serving as interactive
+conversational models, will probably drive this patient-centered transformation
+in healthcare. Through the provision of various services, including diagnosis,
+personalized lifestyle recommendations, and mental health support, the
+objective is to substantially augment patient health outcomes, all the while
+mitigating the workload burden on healthcare providers. The life-critical
+nature of healthcare applications necessitates establishing a unified and
+comprehensive set of evaluation metrics for conversational models. Existing
+evaluation metrics proposed for various generic large language models (LLMs)
+demonstrate a lack of comprehension regarding medical and health concepts and
+their significance in promoting patients' well-being. Moreover, these metrics
+neglect pivotal user-centered aspects, including trust-building, ethics,
+personalization, empathy, user comprehension, and emotional support. The
+purpose of this paper is to explore state-of-the-art LLM-based evaluation
+metrics that are specifically applicable to the assessment of interactive
+conversational models in healthcare. Subsequently, we present an comprehensive
+set of evaluation metrics designed to thoroughly assess the performance of
+healthcare chatbots from an end-user perspective. These metrics encompass an
+evaluation of language processing abilities, impact on real-world clinical
+tasks, and effectiveness in user-interactive conversations. Finally, we engage
+in a discussion concerning the challenges associated with defining and
+implementing these metrics, with particular emphasis on confounding factors
+such as the target audience, evaluation methods, and prompt techniques involved
+in the evaluation process.
+"
+Fill in the Blank: Exploring and Enhancing LLM Capabilities for Backward  Reasoning in Math Word Problems,Aniruddha Deb,http://arxiv.org/pdf/2310.01991v1.pdf,2023-10-03,"['cs.cl', 'cs.ai', 'cs.lg', 'i.2.3']",2310.01991v1.pdf,"  While forward reasoning (i.e. find the answer given the question) has been
+explored extensively in the recent literature, backward reasoning is relatively
+unexplored. We examine the backward reasoning capabilities of LLMs on Math Word
+Problems (MWPs): given a mathematical question and its answer, with some
+details omitted from the question, can LLMs effectively retrieve the missing
+information?
+  In this paper, we formally define the backward reasoning task on math word
+problems and modify three datasets to evaluate this task: GSM8k, SVAMP and
+MultiArith. Our findings show a significant drop in the accuracy of models on
+backward reasoning compared to forward reasoning across four SOTA LLMs (GPT4,
+GPT3.5, PaLM-2, and LLaMa-2). Utilizing the specific format of this task, we
+propose three novel techniques that improve performance: Rephrase reformulates
+the given problem into a forward reasoning problem, PAL-Tools combines the idea
+of Program-Aided LLMs to produce a set of equations that can be solved by an
+external solver, and Check your Work exploits the availability of natural
+verifier of high accuracy in the forward direction, interleaving solving and
+verification steps. Finally, realizing that each of our base methods correctly
+solves a different set of problems, we propose a novel Bayesian formulation for
+creating an ensemble over these base methods aided by a verifier to further
+boost the accuracy by a significant margin. Extensive experimentation
+demonstrates that our techniques successively improve the performance of LLMs
+on the backward reasoning task, with the final ensemble-based method resulting
+in a substantial performance gain compared to the raw LLMs with standard
+prompting techniques such as chain-of-thought.
+"
+Autonomous Tree-search Ability of Large Language Models,Zheyu Zhang,http://arxiv.org/pdf/2310.10686v1.pdf,2023-10-14,"['cs.cl', 'cs.ai']",2310.10686v1.pdf,"  Large Language Models have excelled in remarkable reasoning capabilities with
+advanced prompting techniques, but they fall short on tasks that require
+exploration, strategic foresight, and sequential decision-making. Recent works
+propose to utilize external programs to define search logic, such that LLMs can
+perform passive tree search to solve more challenging reasoning tasks. Though
+impressive results have been achieved, there are several fundamental
+limitations of these approaches. First, passive tree searches are not efficient
+as they usually require multiple rounds of LLM API calls to solve one single
+problem. Moreover, passive search methods are not flexible since they need
+task-specific program designs. Then a natural question arises: can we maintain
+the tree-search capability of LLMs without the aid of external programs, and
+can still generate responses that clearly demonstrate the process of a
+tree-structure search? To this end, we propose a new concept called autonomous
+tree-search ability of LLM, which can automatically generate a response
+containing search trajectories for the correct answer. Concretely, we perform
+search trajectories using capable LLM API via a fixed system prompt, allowing
+them to perform autonomous tree-search (ATS) right out of the box. Experiments
+on 4 puzzle games demonstrate our method can achieve huge improvements. The
+ATS-BFS method outperforms the Chain of Thought approach by achieving an
+average accuracy improvement of 33%. Compared to Tree of Thoughts, it requires
+65.6% or 47.7% less GPT-api cost to attain a comparable level of accuracy.
+Moreover, we have collected data using the ATS prompt method and fine-tuned
+LLaMA. This approach yield a greater improvement compared to the ones
+fine-tuned on CoT data. Specifically, it outperforms CoT-tuned LLaMAs by an
+average of 40.6% and 38.5% for LLaMA2-7B and LLaMA2-13B, respectively.
+"
+In-Context Impersonation Reveals Large Language Models' Strengths and  Biases,Leonard Salewski,http://arxiv.org/pdf/2305.14930v1.pdf,2023-05-24,"['cs.ai', 'cs.cl', 'cs.lg']",2305.14930v1.pdf,"  In everyday conversations, humans can take on different roles and adapt their
+vocabulary to their chosen roles. We explore whether LLMs can take on, that is
+impersonate, different roles when they generate text in-context. We ask LLMs to
+assume different personas before solving vision and language tasks. We do this
+by prefixing the prompt with a persona that is associated either with a social
+identity or domain expertise. In a multi-armed bandit task, we find that LLMs
+pretending to be children of different ages recover human-like developmental
+stages of exploration. In a language-based reasoning task, we find that LLMs
+impersonating domain experts perform better than LLMs impersonating non-domain
+experts. Finally, we test whether LLMs' impersonations are complementary to
+visual information when describing different categories. We find that
+impersonation can improve performance: an LLM prompted to be a bird expert
+describes birds better than one prompted to be a car expert. However,
+impersonation can also uncover LLMs' biases: an LLM prompted to be a man
+describes cars better than one prompted to be a woman. These findings
+demonstrate that LLMs are capable of taking on diverse roles and that this
+in-context impersonation can be used to uncover their hidden strengths and
+biases.
+"
+ROSGPT_Vision: Commanding Robots Using Only Language Models' Prompts,Bilel Benjdira,http://arxiv.org/pdf/2308.11236v2.pdf,2023-08-22,"['cs.ro', 'cs.ai']",2308.11236v2.pdf,"  In this paper, we argue that the next generation of robots can be commanded
+using only Language Models' prompts. Every prompt interrogates separately a
+specific Robotic Modality via its Modality Language Model (MLM). A central Task
+Modality mediates the whole communication to execute the robotic mission via a
+Large Language Model (LLM). This paper gives this new robotic design pattern
+the name of: Prompting Robotic Modalities (PRM). Moreover, this paper applies
+this PRM design pattern in building a new robotic framework named
+ROSGPT_Vision. ROSGPT_Vision allows the execution of a robotic task using only
+two prompts: a Visual and an LLM prompt. The Visual Prompt extracts, in natural
+language, the visual semantic features related to the task under consideration
+(Visual Robotic Modality). Meanwhile, the LLM Prompt regulates the robotic
+reaction to the visual description (Task Modality). The framework automates all
+the mechanisms behind these two prompts. The framework enables the robot to
+address complex real-world scenarios by processing visual data, making informed
+decisions, and carrying out actions automatically. The framework comprises one
+generic vision module and two independent ROS nodes. As a test application, we
+used ROSGPT_Vision to develop CarMate, which monitors the driver's distraction
+on the roads and makes real-time vocal notifications to the driver. We showed
+how ROSGPT_Vision significantly reduced the development cost compared to
+traditional methods. We demonstrated how to improve the quality of the
+application by optimizing the prompting strategies, without delving into
+technical details. ROSGPT_Vision is shared with the community (link:
+https://github.com/bilel-bj/ROSGPT_Vision) to advance robotic research in this
+direction and to build more robotic frameworks that implement the PRM design
+pattern and enables controlling robots using only prompts.
+"
+ProgPrompt: Generating Situated Robot Task Plans using Large Language  Models,Ishika Singh,http://arxiv.org/pdf/2209.11302v1.pdf,2022-09-22,"['cs.ro', 'cs.ai', 'cs.cl', 'cs.lg']",2209.11302v1.pdf,"  Task planning can require defining myriad domain knowledge about the world in
+which a robot needs to act. To ameliorate that effort, large language models
+(LLMs) can be used to score potential next actions during task planning, and
+even generate action sequences directly, given an instruction in natural
+language with no additional domain information. However, such methods either
+require enumerating all possible next steps for scoring, or generate free-form
+text that may contain actions not possible on a given robot in its current
+context. We present a programmatic LLM prompt structure that enables plan
+generation functional across situated environments, robot capabilities, and
+tasks. Our key insight is to prompt the LLM with program-like specifications of
+the available actions and objects in an environment, as well as with example
+programs that can be executed. We make concrete recommendations about prompt
+structure and generation constraints through ablation experiments, demonstrate
+state of the art success rates in VirtualHome household tasks, and deploy our
+method on a physical robot arm for tabletop tasks. Website at
+progprompt.github.io
+"
+Characterizing Attribution and Fluency Tradeoffs for Retrieval-Augmented  Large Language Models,Renat Aksitov,http://arxiv.org/pdf/2302.05578v2.pdf,2023-02-11,"['cs.cl', 'cs.ai']",2302.05578v2.pdf,"  Despite recent progress, it has been difficult to prevent semantic
+hallucinations in generative Large Language Models. One common solution to this
+is augmenting LLMs with a retrieval system and making sure that the generated
+output is attributable to the retrieved information. Given this new added
+constraint, it is plausible to expect that the overall quality of the output
+will be affected, for example, in terms of fluency. Can scaling language models
+help?
+  Here we examine the relationship between fluency and attribution in LLMs
+prompted with retrieved evidence in knowledge-heavy dialog settings. Our
+experiments were implemented with a set of auto-metrics that are aligned with
+human preferences. They were used to evaluate a large set of generations,
+produced under varying parameters of LLMs and supplied context.
+  We show that larger models tend to do much better in both fluency and
+attribution, and that (naively) using top-k retrieval versus top-1 retrieval
+improves attribution but hurts fluency. We next propose a recipe that could
+allow smaller models to both close the gap with larger models and preserve the
+benefits of top-k retrieval while avoiding its drawbacks.
+"
+Dictionary-based Phrase-level Prompting of Large Language Models for  Machine Translation,Marjan Ghazvininejad,http://arxiv.org/pdf/2302.07856v1.pdf,2023-02-15,"['cs.cl', 'cs.lg']",2302.07856v1.pdf,"  Large language models (LLMs) demonstrate remarkable machine translation (MT)
+abilities via prompting, even though they were not explicitly trained for this
+task. However, even given the incredible quantities of data they are trained
+on, LLMs can struggle to translate inputs with rare words, which are common in
+low resource or domain transfer scenarios. We show that LLM prompting can
+provide an effective solution for rare words as well, by using prior knowledge
+from bilingual dictionaries to provide control hints in the prompts. We propose
+a novel method, DiPMT, that provides a set of possible translations for a
+subset of the input words, thereby enabling fine-grained phrase-level prompted
+control of the LLM. Extensive experiments show that DiPMT outperforms the
+baseline both in low-resource MT, as well as for out-of-domain MT. We further
+provide a qualitative analysis of the benefits and limitations of this
+approach, including the overall level of controllability that is achieved.
+"
+UDAPDR: Unsupervised Domain Adaptation via LLM Prompting and  Distillation of Rerankers,Jon Saad-Falcon,http://arxiv.org/pdf/2303.00807v3.pdf,2023-03-01,"['cs.ir', 'cs.cl']",2303.00807v3.pdf,"  Many information retrieval tasks require large labeled datasets for
+fine-tuning. However, such datasets are often unavailable, and their utility
+for real-world applications can diminish quickly due to domain shifts. To
+address this challenge, we develop and motivate a method for using large
+language models (LLMs) to generate large numbers of synthetic queries cheaply.
+The method begins by generating a small number of synthetic queries using an
+expensive LLM. After that, a much less expensive one is used to create large
+numbers of synthetic queries, which are used to fine-tune a family of reranker
+models. These rerankers are then distilled into a single efficient retriever
+for use in the target domain. We show that this technique boosts zero-shot
+accuracy in long-tail domains and achieves substantially lower latency than
+standard reranking methods.
+"
+LMCanvas: Object-Oriented Interaction to Personalize Large Language  Model-Powered Writing Environments,Tae Soo Kim,http://arxiv.org/pdf/2303.15125v1.pdf,2023-03-27,"['cs.hc', 'cs.cl']",2303.15125v1.pdf,"  Large language models (LLMs) can enhance writing by automating or supporting
+specific tasks in writers' workflows (e.g., paraphrasing, creating analogies).
+Leveraging this capability, a collection of interfaces have been developed that
+provide LLM-powered tools for specific writing tasks. However, these interfaces
+provide limited support for writers to create personal tools for their own
+unique tasks, and may not comprehensively fulfill a writer's needs -- requiring
+them to continuously switch between interfaces during writing. In this work, we
+envision LMCanvas, an interface that enables writers to create their own
+LLM-powered writing tools and arrange their personal writing environment by
+interacting with ""blocks"" in a canvas. In this interface, users can create text
+blocks to encapsulate writing and LLM prompts, model blocks for model parameter
+configurations, and connect these to create pipeline blocks that output
+generations. In this workshop paper, we discuss the design for LMCanvas and our
+plans to develop this concept.
+"
+SGP-TOD: Building Task Bots Effortlessly via Schema-Guided LLM Prompting,Xiaoying Zhang,http://arxiv.org/pdf/2305.09067v1.pdf,2023-05-15,['cs.cl'],2305.09067v1.pdf,"  Building end-to-end task bots and maintaining their integration with new
+functionalities using minimal human efforts is a long-standing challenge in
+dialog research. Recently large language models (LLMs) have demonstrated
+exceptional proficiency in conversational engagement and adherence to
+instructions across various downstream tasks. In this work, we introduce
+SGP-TOD, Schema-Guided Prompting for building Task-Oriented Dialog systems
+effortlessly based on LLMs. Utilizing the symbolic knowledge -- task schema, we
+instruct fixed LLMs to generate appropriate responses on novel tasks,
+circumventing the need for training data. Specifically, SGP-TOD comprises three
+components: a LLM for engaging with users, a DST Prompter to aid the LLM with
+dialog state tracking, which is then used to retrieve database items, and a
+Policy Prompter to elicit proper responses adhering to the provided dialog
+policy. Experimental results on Multiwoz, RADDLE and STAR datasets show that
+our training-free strategy SGP-TOD, without any task-specific data, yields
+state-of-the-art (SOTA) zero-shot performance, greatly surpasses the few-shot
+approaches. In a domain-extension setting, SGP-TOD aptly adapts to new
+functionalities by merely adding supplementary schema rules. We make our code
+and data publicly available.
+"
+TELeR: A General Taxonomy of LLM Prompts for Benchmarking Complex Tasks,Shubhra Kanti Karmaker Santu,http://arxiv.org/pdf/2305.11430v2.pdf,2023-05-19,"['cs.ai', 'cs.cl', 'cs.ir', 'cs.lg', 'i.2.7']",2305.11430v2.pdf,"  While LLMs have shown great success in understanding and generating text in
+traditional conversational settings, their potential for performing ill-defined
+complex tasks is largely under-studied. Indeed, we are yet to conduct
+comprehensive benchmarking studies with multiple LLMs that are exclusively
+focused on a complex task. However, conducting such benchmarking studies is
+challenging because of the large variations in LLMs' performance when different
+prompt types/styles are used and different degrees of detail are provided in
+the prompts. To address this issue, the paper proposes a general taxonomy that
+can be used to design prompts with specific properties in order to perform a
+wide range of complex tasks. This taxonomy will allow future benchmarking
+studies to report the specific categories of prompts used as part of the study,
+enabling meaningful comparisons across different studies. Also, by establishing
+a common standard through this taxonomy, researchers will be able to draw more
+accurate conclusions about LLMs' performance on a specific complex task.
+"
+S$^3$HQA: A Three-Stage Approach for Multi-hop Text-Table Hybrid  Question Answering,Fangyu Lei,http://arxiv.org/pdf/2305.11725v1.pdf,2023-05-19,['cs.cl'],2305.11725v1.pdf,"  Answering multi-hop questions over hybrid factual knowledge from the given
+text and table (TextTableQA) is a challenging task. Existing models mainly
+adopt a retriever-reader framework, which have several deficiencies, such as
+noisy labeling in training retriever, insufficient utilization of heterogeneous
+information over text and table, and deficient ability for different reasoning
+operations. In this paper, we propose a three-stage TextTableQA framework
+S3HQA, which comprises of retriever, selector, and reasoner. We use a retriever
+with refinement training to solve the noisy labeling problem. Then, a hybrid
+selector considers the linked relationships between heterogeneous data to
+select the most relevant factual knowledge. For the final stage, instead of
+adapting a reading comprehension module like in previous methods, we employ a
+generation-based reasoner to obtain answers. This includes two approaches: a
+row-wise generator and an LLM prompting generator~(first time used in this
+task). The experimental results demonstrate that our method achieves
+competitive results in the few-shot setting. When trained on the full dataset,
+our approach outperforms all baseline methods, ranking first on the HybridQA
+leaderboard.
+"
+LLM-Eval: Unified Multi-Dimensional Automatic Evaluation for Open-Domain  Conversations with Large Language Models,Yen-Ting Lin,http://arxiv.org/pdf/2305.13711v1.pdf,2023-05-23,"['cs.cl', 'cs.ai']",2305.13711v1.pdf,"  We propose LLM-Eval, a unified multi-dimensional automatic evaluation method
+for open-domain conversations with large language models (LLMs). Existing
+evaluation methods often rely on human annotations, ground-truth responses, or
+multiple LLM prompts, which can be expensive and time-consuming. To address
+these issues, we design a single prompt-based evaluation method that leverages
+a unified evaluation schema to cover multiple dimensions of conversation
+quality in a single model call. We extensively evaluate the performance of
+LLM-Eval on various benchmark datasets, demonstrating its effectiveness,
+efficiency, and adaptability compared to state-of-the-art evaluation methods.
+Our analysis also highlights the importance of choosing suitable LLMs and
+decoding strategies for accurate evaluation results. LLM-Eval offers a
+versatile and robust solution for evaluating open-domain conversation systems,
+streamlining the evaluation process and providing consistent performance across
+diverse scenarios.
+"
+AutoPlan: Automatic Planning of Interactive Decision-Making Tasks With  Large Language Models,Siqi Ouyang,http://arxiv.org/pdf/2305.15064v3.pdf,2023-05-24,['cs.cl'],2305.15064v3.pdf,"  Recent large language models (LLMs) are promising for making decisions in
+grounded environments. However, LLMs frequently fail in complex decision-making
+tasks due to the misalignment between the pre-trained knowledge in LLMs and the
+actual rules in the environment. Existing methods require either costly
+gradient computation or lengthy in-context demonstrations. In this paper, we
+propose AutoPlan, an approach to guide LLM-based agents to accomplish
+interactive decision-making tasks. AutoPlan augments the LLM prompt with a
+task-solving plan and optimizes it through iterative experience collection and
+reflection. Our experiments show that AutoPlan, though using no in-context
+demonstrations, achieves success rates on par with the baselines using
+human-written demonstrations on ALFWorld and even outperforms them by 8% on
+HotpotQA. The code is available at https://github.com/owaski/AutoPlan.
+"
+ChatGPT for PLC/DCS Control Logic Generation,Heiko Koziolek,http://arxiv.org/pdf/2305.15809v1.pdf,2023-05-25,"['cs.se', 'cs.ai', 'd.2.2']",2305.15809v1.pdf,"  Large language models (LLMs) providing generative AI have become popular to
+support software engineers in creating, summarizing, optimizing, and
+documenting source code. It is still unknown how LLMs can support control
+engineers using typical control programming languages in programming tasks.
+Researchers have explored GitHub CoPilot or DeepMind AlphaCode for source code
+generation but did not yet tackle control logic programming. The contribution
+of this paper is an exploratory study, for which we created 100 LLM prompts in
+10 representative categories to analyze control logic generation for of PLCs
+and DCS from natural language. We tested the prompts by generating answers with
+ChatGPT using the GPT-4 LLM. It generated syntactically correct IEC 61131-3
+Structured Text code in many cases and demonstrated useful reasoning skills
+that could boost control engineer productivity. Our prompt collection is the
+basis for a more formal LLM benchmark to test and compare such models for
+control logic generation.
+"
+AdaPlanner: Adaptive Planning from Feedback with Language Models,Haotian Sun,http://arxiv.org/pdf/2305.16653v1.pdf,2023-05-26,"['cs.cl', 'cs.ai', 'cs.lg']",2305.16653v1.pdf,"  Large language models (LLMs) have recently demonstrated the potential in
+acting as autonomous agents for sequential decision-making tasks. However, most
+existing methods either take actions greedily without planning or rely on
+static plans that are not adaptable to environmental feedback. Consequently,
+the sequential decision-making performance of LLM agents degenerates with
+problem complexity and plan horizons increase. We propose a closed-loop
+approach, AdaPlanner, which allows the LLM agent to refine its self-generated
+plan adaptively in response to environmental feedback. In AdaPlanner, the LLM
+agent adaptively refines its plan from feedback with both in-plan and
+out-of-plan refinement strategies. To mitigate hallucination, we develop a
+code-style LLM prompt structure that facilitates plan generation across a
+variety of tasks, environments, and agent capabilities. Furthermore, we propose
+a skill discovery mechanism that leverages successful plans as few-shot
+exemplars, enabling the agent to plan and refine with fewer task
+demonstrations. Our experiments in the ALFWorld and MiniWoB++ environments
+demonstrate that AdaPlanner outperforms state-of-the-art baselines by 3.73% and
+4.11% while utilizing 2x and 600x fewer samples, respectively.
+"
+Robot Task Planning Based on Large Language Model Representing Knowledge  with Directed Graph Structures,Yue Zhen,http://arxiv.org/pdf/2306.05171v1.pdf,2023-06-08,"['cs.ro', 'cs.ai']",2306.05171v1.pdf,"  Traditional robot task planning methods face challenges when dealing with
+highly unstructured environments and complex tasks. We propose a task planning
+method that combines human expertise with an LLM and have designed an LLM
+prompt template, Think_Net_Prompt, with stronger expressive power to represent
+structured professional knowledge. We further propose a method to progressively
+decompose tasks and generate a task tree to reduce the planning volume for each
+task, and we have designed a strategy to decouple robot task planning. By
+dividing different planning entities and separating the task from the actual
+machine binding process, the task planning process becomes more flexible.
+Research results show that our method performs well in handling specified code
+formats, understanding the relationship between tasks and subtasks, and
+extracting parameters from text descriptions. However, there are also problems
+such as limited complexity of task logic handling, ambiguity in the quantity of
+parts and the precise location of assembly. Improving the precision of task
+description and cognitive structure can bring certain improvements.
+https://github.com/NOMIzy/Think_Net_Prompt
+"
+SayTap: Language to Quadrupedal Locomotion,Yujin Tang,http://arxiv.org/pdf/2306.07580v3.pdf,2023-06-13,['cs.ro'],2306.07580v3.pdf,"  Large language models (LLMs) have demonstrated the potential to perform
+high-level planning. Yet, it remains a challenge for LLMs to comprehend
+low-level commands, such as joint angle targets or motor torques. This paper
+proposes an approach to use foot contact patterns as an interface that bridges
+human commands in natural language and a locomotion controller that outputs
+these low-level commands. This results in an interactive system for quadrupedal
+robots that allows the users to craft diverse locomotion behaviors flexibly. We
+contribute an LLM prompt design, a reward function, and a method to expose the
+controller to the feasible distribution of contact patterns. The results are a
+controller capable of achieving diverse locomotion patterns that can be
+transferred to real robot hardware. Compared with other design choices, the
+proposed approach enjoys more than 50% success rate in predicting the correct
+contact patterns and can solve 10 more tasks out of a total of 30 tasks. Our
+project site is: https://saytap.github.io.
+"
+Large Language Models Enable Few-Shot Clustering,Vijay Viswanathan,http://arxiv.org/pdf/2307.00524v1.pdf,2023-07-02,['cs.cl'],2307.00524v1.pdf,"  Unlike traditional unsupervised clustering, semi-supervised clustering allows
+users to provide meaningful structure to the data, which helps the clustering
+algorithm to match the user's intent. Existing approaches to semi-supervised
+clustering require a significant amount of feedback from an expert to improve
+the clusters. In this paper, we ask whether a large language model can amplify
+an expert's guidance to enable query-efficient, few-shot semi-supervised text
+clustering. We show that LLMs are surprisingly effective at improving
+clustering. We explore three stages where LLMs can be incorporated into
+clustering: before clustering (improving input features), during clustering (by
+providing constraints to the clusterer), and after clustering (using LLMs
+post-correction). We find incorporating LLMs in the first two stages can
+routinely provide significant improvements in cluster quality, and that LLMs
+enable a user to make trade-offs between cost and accuracy to produce desired
+clusters. We release our code and LLM prompts for the public to use.
+"
+GEAR: Augmenting Language Models with Generalizable and Efficient Tool  Resolution,Yining Lu,http://arxiv.org/pdf/2307.08775v1.pdf,2023-07-17,['cs.ai'],2307.08775v1.pdf,"  Augmenting large language models (LLM) to use external tools enhances their
+performance across a variety of tasks. However, prior works over-rely on
+task-specific demonstration of tool use that limits their generalizability and
+computational cost due to making many calls to large-scale LLMs. We introduce
+GEAR, a computationally efficient query-tool grounding algorithm that is
+generalizable to various tasks that require tool use while not relying on
+task-specific demonstrations. GEAR achieves better efficiency by delegating
+tool grounding and execution to small language models (SLM) and LLM,
+respectively; while leveraging semantic and pattern-based evaluation at both
+question and answer levels for generalizable tool grounding. We evaluate GEAR
+on 14 datasets across 6 downstream tasks, demonstrating its strong
+generalizability to novel tasks, tools and different SLMs. Despite offering
+more efficiency, GEAR achieves higher precision in tool grounding compared to
+prior strategies using LLM prompting, thus improving downstream accuracy at a
+reduced computational cost. For example, we demonstrate that GEAR-augmented
+GPT-J and GPT-3 outperform counterpart tool-augmented baselines because of
+better tool use.
+"
+Simple LLM Prompting is State-of-the-Art for Robust and Multilingual  Dialogue Evaluation,John Mendonça,http://arxiv.org/pdf/2308.16797v2.pdf,2023-08-31,['cs.cl'],2308.16797v2.pdf,"  Despite significant research effort in the development of automatic dialogue
+evaluation metrics, little thought is given to evaluating dialogues other than
+in English. At the same time, ensuring metrics are invariant to semantically
+similar responses is also an overlooked topic. In order to achieve the desired
+properties of robustness and multilinguality for dialogue evaluation metrics,
+we propose a novel framework that takes advantage of the strengths of current
+evaluation models with the newly-established paradigm of prompting Large
+Language Models (LLMs). Empirical results show our framework achieves state of
+the art results in terms of mean Spearman correlation scores across several
+benchmarks and ranks first place on both the Robust and Multilingual tasks of
+the DSTC11 Track 4 ""Automatic Evaluation Metrics for Open-Domain Dialogue
+Systems"", proving the evaluation capabilities of prompted LLMs.
+"
+"MMHQA-ICL: Multimodal In-context Learning for Hybrid Question Answering  over Text, Tables and Images",Weihao Liu,http://arxiv.org/pdf/2309.04790v1.pdf,2023-09-09,['cs.cl'],2309.04790v1.pdf,"  In the real world, knowledge often exists in a multimodal and heterogeneous
+form. Addressing the task of question answering with hybrid data types,
+including text, tables, and images, is a challenging task (MMHQA). Recently,
+with the rise of large language models (LLM), in-context learning (ICL) has
+become the most popular way to solve QA problems. We propose MMHQA-ICL
+framework for addressing this problems, which includes stronger heterogeneous
+data retriever and an image caption module. Most importantly, we propose a
+Type-specific In-context Learning Strategy for MMHQA, enabling LLMs to leverage
+their powerful performance in this task. We are the first to use end-to-end LLM
+prompting method for this task. Experimental results demonstrate that our
+framework outperforms all baselines and methods trained on the full dataset,
+achieving state-of-the-art results under the few-shot setting on the
+MultimodalQA dataset.
+"
+Empowering Private Tutoring by Chaining Large Language Models,Yulin Chen,http://arxiv.org/pdf/2309.08112v1.pdf,2023-09-15,['cs.hc'],2309.08112v1.pdf,"  Artificial intelligence has been applied in various aspects of online
+education to facilitate teaching and learning. However, few approaches has been
+made toward a complete AI-powered tutoring system. In this work, we explore the
+development of a full-fledged intelligent tutoring system powered by
+state-of-the-art large language models (LLMs), covering automatic course
+planning and adjusting, tailored instruction, and flexible quiz evaluation. To
+make the system robust to prolonged interaction and cater to individualized
+education, the system is decomposed into three inter-connected core
+processes-interaction, reflection, and reaction. Each process is implemented by
+chaining LLM-powered tools along with dynamically updated memory modules. Tools
+are LLMs prompted to execute one specific task at a time, while memories are
+data storage that gets updated during education process. Statistical results
+from learning logs demonstrate the effectiveness and mechanism of each tool
+usage. Subjective feedback from human users reveal the usability of each
+function, and comparison with ablation systems further testify the benefits of
+the designed processes in long-term interaction.
+"
+Retrieve-Rewrite-Answer: A KG-to-Text Enhanced LLMs Framework for  Knowledge Graph Question Answering,Yike Wu,http://arxiv.org/pdf/2309.11206v2.pdf,2023-09-20,"['cs.cl', 'cs.ai']",2309.11206v2.pdf,"  Despite their competitive performance on knowledge-intensive tasks, large
+language models (LLMs) still have limitations in memorizing all world knowledge
+especially long tail knowledge. In this paper, we study the KG-augmented
+language model approach for solving the knowledge graph question answering
+(KGQA) task that requires rich world knowledge. Existing work has shown that
+retrieving KG knowledge to enhance LLMs prompting can significantly improve
+LLMs performance in KGQA. However, their approaches lack a well-formed
+verbalization of KG knowledge, i.e., they ignore the gap between KG
+representations and textual representations. To this end, we propose an
+answer-sensitive KG-to-Text approach that can transform KG knowledge into
+well-textualized statements most informative for KGQA. Based on this approach,
+we propose a KG-to-Text enhanced LLMs framework for solving the KGQA task.
+Experiments on several KGQA benchmarks show that the proposed KG-to-Text
+augmented LLMs approach outperforms previous KG-augmented LLMs approaches
+regarding answer accuracy and usefulness of knowledge statements.
+"
+LPML: LLM-Prompting Markup Language for Mathematical Reasoning,Ryutaro Yamauchi,http://arxiv.org/pdf/2309.13078v2.pdf,2023-09-21,"['cs.ai', 'cs.lg', 'cs.pl']",2309.13078v2.pdf,"  In utilizing large language models (LLMs) for mathematical reasoning,
+addressing the errors in the reasoning and calculation present in the generated
+text by LLMs is a crucial challenge. In this paper, we propose a novel
+framework that integrates the Chain-of-Thought (CoT) method with an external
+tool (Python REPL). We discovered that by prompting LLMs to generate structured
+text in XML-like markup language, we could seamlessly integrate CoT and the
+external tool and control the undesired behaviors of LLMs. With our approach,
+LLMs can utilize Python computation to rectify errors within CoT. We applied
+our method to ChatGPT (GPT-3.5) to solve challenging mathematical problems and
+demonstrated that combining CoT and Python REPL through the markup language
+enhances the reasoning capability of LLMs. Our approach enables LLMs to write
+the markup language and perform advanced mathematical reasoning using only
+zero-shot prompting.
+"
+HeaP: Hierarchical Policies for Web Actions using LLMs,Paloma Sodhi,http://arxiv.org/pdf/2310.03720v1.pdf,2023-10-05,['cs.lg'],2310.03720v1.pdf,"  Large language models (LLMs) have demonstrated remarkable capabilities in
+performing a range of instruction following tasks in few and zero-shot
+settings. However, teaching LLMs to perform tasks on the web presents
+fundamental challenges -- combinatorially large open-world tasks and variations
+across web interfaces. We tackle these challenges by leveraging LLMs to
+decompose web tasks into a collection of sub-tasks, each of which can be solved
+by a low-level, closed-loop policy. These policies constitute a shared grammar
+across tasks, i.e., new web tasks can be expressed as a composition of these
+policies. We propose a novel framework, Hierarchical Policies for Web Actions
+using LLMs (HeaP), that learns a set of hierarchical LLM prompts from
+demonstrations for planning high-level tasks and executing them via a sequence
+of low-level policies. We evaluate HeaP against a range of baselines on a suite
+of web tasks, including MiniWoB++, WebArena, a mock airline CRM, as well as
+live website interactions, and show that it is able to outperform prior works
+using orders of magnitude less data.
+"
+OptiMUS: Optimization Modeling Using MIP Solvers and large language  models,Ali AhmadiTeshnizi,http://arxiv.org/pdf/2310.06116v2.pdf,2023-10-09,['cs.ai'],2310.06116v2.pdf,"  Optimization problems are pervasive across various sectors, from
+manufacturing and distribution to healthcare. However, most such problems are
+still solved heuristically by hand rather than optimally by state-of-the-art
+solvers, as the expertise required to formulate and solve these problems limits
+the widespread adoption of optimization tools and techniques. We introduce
+OptiMUS, a Large Language Model (LLM)-based agent designed to formulate and
+solve MILP problems from their natural language descriptions. OptiMUS is
+capable of developing mathematical models, writing and debugging solver code,
+developing tests, and checking the validity of generated solutions. To
+benchmark our agent, we present NLP4LP, a novel dataset of linear programming
+(LP) and mixed integer linear programming (MILP) problems. Our experiments
+demonstrate that OptiMUS solves nearly twice as many problems as a basic LLM
+prompting strategy. OptiMUS code and NLP4LP dataset are available at
+\href{https://github.com/teshnizi/OptiMUS}{https://github.com/teshnizi/OptiMUS}
+"
+A ML-LLM pairing for better code comment classification,Hanna Abi Akl,http://arxiv.org/pdf/2310.10275v1.pdf,2023-10-13,"['cs.se', 'cs.ai']",2310.10275v1.pdf,"  The ""Information Retrieval in Software Engineering (IRSE)"" at FIRE 2023
+shared task introduces code comment classification, a challenging task that
+pairs a code snippet with a comment that should be evaluated as either useful
+or not useful to the understanding of the relevant code. We answer the code
+comment classification shared task challenge by providing a two-fold
+evaluation: from an algorithmic perspective, we compare the performance of
+classical machine learning systems and complement our evaluations from a
+data-driven perspective by generating additional data with the help of large
+language model (LLM) prompting to measure the potential increase in
+performance. Our best model, which took second place in the shared task, is a
+Neural Network with a Macro-F1 score of 88.401% on the provided seed data and a
+1.5% overall increase in performance on the data generated by the LLM.
+"
+Multi-stage Large Language Model Correction for Speech Recognition,Jie Pu,http://arxiv.org/pdf/2310.11532v1.pdf,2023-10-17,"['cs.cl', 'eess.as']",2310.11532v1.pdf,"  In this paper, we investigate the usage of large language models (LLMs) to
+improve the performance of competitive speech recognition systems. Different
+from traditional language models that focus on one single data domain, the rise
+of LLMs brings us the opportunity to push the limit of state-of-the-art ASR
+performance, and at the same time to achieve higher robustness and generalize
+effectively across multiple domains. Motivated by this, we propose a novel
+multi-stage approach to combine traditional language model re-scoring and LLM
+prompting. Specifically, the proposed method has two stages: the first stage
+uses a language model to re-score an N-best list of ASR hypotheses and run a
+confidence check; The second stage uses prompts to a LLM to perform ASR error
+correction on less confident results from the first stage. Our experimental
+results demonstrate the effectiveness of the proposed method by showing a 10% ~
+20% relative improvement in WER over a competitive ASR system -- across
+multiple test domains.
+"
+PromptInfuser: How Tightly Coupling AI and UI Design Impacts Designers'  Workflows,Savvas Petridis,http://arxiv.org/pdf/2310.15435v1.pdf,2023-10-24,"['cs.hc', 'cs.ai']",2310.15435v1.pdf,"  Prototyping AI applications is notoriously difficult. While large language
+model (LLM) prompting has dramatically lowered the barriers to AI prototyping,
+designers are still prototyping AI functionality and UI separately. We
+investigate how coupling prompt and UI design affects designers' workflows.
+Grounding this research, we developed PromptInfuser, a Figma plugin that
+enables users to create semi-functional mockups, by connecting UI elements to
+the inputs and outputs of prompts. In a study with 14 designers, we compare
+PromptInfuser to designers' current AI-prototyping workflow. PromptInfuser was
+perceived to be significantly more useful for communicating product ideas, more
+capable of producing prototypes that realistically represent the envisioned
+artifact, more efficient for prototyping, and more helpful for anticipating UI
+issues and technical constraints. PromptInfuser encouraged iteration over
+prompt and UI together, which helped designers identify UI and prompt
+incompatibilities and reflect upon their total solution. Together, these
+findings inform future systems for prototyping AI applications.
+"
+OmniFill: Domain-Agnostic Form Filling Suggestions Using Multi-Faceted  Context,Timothy J. Aveni,http://arxiv.org/pdf/2310.17826v1.pdf,2023-10-27,['cs.hc'],2310.17826v1.pdf,"  Predictive suggestion systems offer contextually-relevant text entry
+completions. Existing approaches, like autofill, often excel in
+narrowly-defined domains but fail to generalize to arbitrary workflows. We
+introduce a conceptual framework to analyze the compound demands of a
+particular suggestion context, yielding unique opportunities for large language
+models (LLMs) to infer suggestions for a wide range of domain-agnostic
+form-filling tasks that were out of reach with prior approaches. We explore
+these opportunities in OmniFill, a prototype that collects multi-faceted
+context including browsing and text entry activity to construct an LLM prompt
+that offers suggestions in situ for arbitrary structured text entry interfaces.
+Through a user study with 18 participants, we found that OmniFill offered
+valuable suggestions and we identified four themes that characterize users'
+behavior and attitudes: an ""opportunistic scrapbooking"" approach; a trust
+placed in the system; value in partial success; and a need for visibility into
+prompt context.
+"
+Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data  Generation with Large Language Models,Ran Xu,http://arxiv.org/pdf/2311.00287v1.pdf,2023-11-01,"['cs.cl', 'cs.ai', 'cs.lg', 'q-bio.qm']",2311.00287v1.pdf,"  Clinical natural language processing requires methods that can address
+domain-specific challenges, such as complex medical terminology and clinical
+contexts. Recently, large language models (LLMs) have shown promise in this
+domain. Yet, their direct deployment can lead to privacy issues and are
+constrained by resources. To address this challenge, we delve into synthetic
+clinical text generation using LLMs for clinical NLP tasks. We propose an
+innovative, resource-efficient approach, ClinGen, which infuses knowledge into
+the process. Our model involves clinical knowledge extraction and
+context-informed LLM prompting. Both clinical topics and writing styles are
+drawn from external domain-specific knowledge graphs and LLMs to guide data
+generation. Our extensive empirical study across 7 clinical NLP tasks and 16
+datasets reveals that ClinGen consistently enhances performance across various
+tasks, effectively aligning the distribution of real datasets and significantly
+enriching the diversity of generated training instances. We will publish our
+code and all the generated data in \url{https://github.com/ritaranx/ClinGen}.
+"
+Promptagator: Few-shot Dense Retrieval From 8 Examples,Zhuyun Dai,http://arxiv.org/pdf/2209.11755v1.pdf,2022-09-23,"['cs.cl', 'cs.ir']",2209.11755v1.pdf,"  Much recent research on information retrieval has focused on how to transfer
+from one task (typically with abundant supervised data) to various other tasks
+where supervision is limited, with the implicit assumption that it is possible
+to generalize from one task to all the rest. However, this overlooks the fact
+that there are many diverse and unique retrieval tasks, each targeting
+different search intents, queries, and search domains. In this paper, we
+suggest to work on Few-shot Dense Retrieval, a setting where each task comes
+with a short description and a few examples. To amplify the power of a few
+examples, we propose Prompt-base Query Generation for Retriever (Promptagator),
+which leverages large language models (LLM) as a few-shot query generator, and
+creates task-specific retrievers based on the generated data. Powered by LLM's
+generalization ability, Promptagator makes it possible to create task-specific
+end-to-end retrievers solely based on a few examples {without} using Natural
+Questions or MS MARCO to train %question generators or dual encoders.
+Surprisingly, LLM prompting with no more than 8 examples allows dual encoders
+to outperform heavily engineered models trained on MS MARCO like ColBERT v2 by
+more than 1.2 nDCG on average on 11 retrieval sets. Further training
+standard-size re-rankers using the same generated data yields another 5.0 point
+nDCG improvement. Our studies determine that query generation can be far more
+effective than previously observed, especially when a small amount of
+task-specific knowledge is given.
+"
+Check Your Facts and Try Again: Improving Large Language Models with  External Knowledge and Automated Feedback,Baolin Peng,http://arxiv.org/pdf/2302.12813v3.pdf,2023-02-24,"['cs.cl', 'cs.ai']",2302.12813v3.pdf,"  Large language models (LLMs), such as ChatGPT, are able to generate
+human-like, fluent responses for many downstream tasks, e.g., task-oriented
+dialog and question answering. However, applying LLMs to real-world,
+mission-critical applications remains challenging mainly due to their tendency
+to generate hallucinations and their inability to use external knowledge. This
+paper proposes a LLM-Augmenter system, which augments a black-box LLM with a
+set of plug-and-play modules. Our system makes the LLM generate responses
+grounded in external knowledge, e.g., stored in task-specific databases. It
+also iteratively revises LLM prompts to improve model responses using feedback
+generated by utility functions, e.g., the factuality score of a LLM-generated
+response. The effectiveness of LLM-Augmenter is empirically validated on two
+types of scenarios, task-oriented dialog and open-domain question answering.
+LLM-Augmenter significantly reduces ChatGPT's hallucinations without
+sacrificing the fluency and informativeness of its responses. We make the
+source code and models publicly available.
+"
+AlpacaFarm: A Simulation Framework for Methods that Learn from Human  Feedback,Yann Dubois,http://arxiv.org/pdf/2305.14387v2.pdf,2023-05-22,"['cs.lg', 'cs.ai', 'cs.cl']",2305.14387v2.pdf,"  Large language models (LLMs) such as ChatGPT have seen widespread adoption
+due to their ability to follow user instructions well. Developing these LLMs
+involves a complex yet poorly understood workflow requiring training with human
+feedback. Replicating and understanding this instruction-following process
+faces three major challenges: the high cost of data collection, the lack of
+trustworthy evaluation, and the absence of reference method implementations. We
+address these challenges with AlpacaFarm, a simulator that enables research and
+development for learning from feedback at a low cost. First, we design LLM
+prompts to simulate human feedback that are 45x cheaper than crowdworkers and
+display high agreement with humans. Second, we propose an automatic evaluation
+and validate it against human instructions obtained on real-world interactions.
+Third, we contribute reference implementations for several methods (PPO,
+best-of-n, expert iteration, and more) that learn from pairwise feedback.
+Finally, as an end-to-end validation of AlpacaFarm, we train and evaluate
+eleven models on 10k pairs of real human feedback and show that rankings of
+models trained in AlpacaFarm match rankings of models trained on human data. As
+a demonstration of the research possible in AlpacaFarm, we find that methods
+that use a reward model can substantially improve over supervised fine-tuning
+and that our reference PPO implementation leads to a +10% improvement in
+win-rate against Davinci003. We release all components of AlpacaFarm at
+https://github.com/tatsu-lab/alpaca_farm.
+"
+MathDial: A Dialogue Tutoring Dataset with Rich Pedagogical Properties  Grounded in Math Reasoning Problems,Jakub Macina,http://arxiv.org/pdf/2305.14536v2.pdf,2023-05-23,['cs.cl'],2305.14536v2.pdf,"  While automatic dialogue tutors hold great potential in making education
+personalized and more accessible, research on such systems has been hampered by
+a lack of sufficiently large and high-quality datasets. Collecting such
+datasets remains challenging, as recording tutoring sessions raises privacy
+concerns and crowdsourcing leads to insufficient data quality. To address this,
+we propose a framework to generate such dialogues by pairing human teachers
+with a Large Language Model (LLM) prompted to represent common student errors.
+We describe how we use this framework to collect MathDial, a dataset of 3k
+one-to-one teacher-student tutoring dialogues grounded in multi-step math
+reasoning problems. While models like GPT-3 are good problem solvers, they fail
+at tutoring because they generate factually incorrect feedback or are prone to
+revealing solutions to students too early. To overcome this, we let teachers
+provide learning opportunities to students by guiding them using various
+scaffolding questions according to a taxonomy of teacher moves. We demonstrate
+MathDial and its extensive annotations can be used to finetune models to be
+more effective tutors (and not just solvers). We confirm this by automatic and
+human evaluation, notably in an interactive setting that measures the trade-off
+between student solving success and telling solutions. The dataset is released
+publicly.
+"
+SPRING: GPT-4 Out-performs RL Algorithms by Studying Papers and  Reasoning,Yue Wu,http://arxiv.org/pdf/2305.15486v2.pdf,2023-05-24,"['cs.ai', 'cs.lg']",2305.15486v2.pdf,"  Open-world survival games pose significant challenges for AI algorithms due
+to their multi-tasking, deep exploration, and goal prioritization requirements.
+Despite reinforcement learning (RL) being popular for solving games, its high
+sample complexity limits its effectiveness in complex open-world games like
+Crafter or Minecraft. We propose a novel approach, SPRING, to read the game's
+original academic paper and use the knowledge learned to reason and play the
+game through a large language model (LLM). Prompted with the LaTeX source as
+game context and a description of the agent's current observation, our SPRING
+framework employs a directed acyclic graph (DAG) with game-related questions as
+nodes and dependencies as edges. We identify the optimal action to take in the
+environment by traversing the DAG and calculating LLM responses for each node
+in topological order, with the LLM's answer to final node directly translating
+to environment actions. In our experiments, we study the quality of in-context
+""reasoning"" induced by different forms of prompts under the setting of the
+Crafter open-world environment. Our experiments suggest that LLMs, when
+prompted with consistent chain-of-thought, have great potential in completing
+sophisticated high-level trajectories. Quantitatively, SPRING with GPT-4
+outperforms all state-of-the-art RL baselines, trained for 1M steps, without
+any training. Finally, we show the potential of games as a test bed for LLMs.
+"
+Flocks of Stochastic Parrots: Differentially Private Prompt Learning for  Large Language Models,Haonan Duan,http://arxiv.org/pdf/2305.15594v1.pdf,2023-05-24,"['cs.lg', 'cs.cl', 'cs.cr']",2305.15594v1.pdf,"  Large language models (LLMs) are excellent in-context learners. However, the
+sensitivity of data contained in prompts raises privacy concerns. Our work
+first shows that these concerns are valid: we instantiate a simple but highly
+effective membership inference attack against the data used to prompt LLMs. To
+address this vulnerability, one could forego prompting and resort to
+fine-tuning LLMs with known algorithms for private gradient descent. However,
+this comes at the expense of the practicality and efficiency offered by
+prompting. Therefore, we propose to privately learn to prompt. We first show
+that soft prompts can be obtained privately through gradient descent on
+downstream data. However, this is not the case for discrete prompts. Thus, we
+orchestrate a noisy vote among an ensemble of LLMs presented with different
+prompts, i.e., a flock of stochastic parrots. The vote privately transfers the
+flock's knowledge into a single public prompt. We show that LLMs prompted with
+our private algorithms closely match the non-private baselines. For example,
+using GPT3 as the base model, we achieve a downstream accuracy of 92.7% on the
+sst2 dataset with ($\epsilon=0.147, \delta=10^{-6}$)-differential privacy vs.
+95.2% for the non-private baseline. Through our experiments, we also show that
+our prompt-based approach is easily deployed with existing commercial APIs.
+"
+Iterative Zero-Shot LLM Prompting for Knowledge Graph Construction,Salvatore Carta,http://arxiv.org/pdf/2307.01128v1.pdf,2023-07-03,"['cs.cl', 'cs.ai']",2307.01128v1.pdf,"  In the current digitalization era, capturing and effectively representing
+knowledge is crucial in most real-world scenarios. In this context, knowledge
+graphs represent a potent tool for retrieving and organizing a vast amount of
+information in a properly interconnected and interpretable structure. However,
+their generation is still challenging and often requires considerable human
+effort and domain expertise, hampering the scalability and flexibility across
+different application fields. This paper proposes an innovative knowledge graph
+generation approach that leverages the potential of the latest generative large
+language models, such as GPT-3.5, that can address all the main critical issues
+in knowledge graph building. The approach is conveyed in a pipeline that
+comprises novel iterative zero-shot and external knowledge-agnostic strategies
+in the main stages of the generation process. Our unique manifold approach may
+encompass significant benefits to the scientific community. In particular, the
+main contribution can be summarized by: (i) an innovative strategy for
+iteratively prompting large language models to extract relevant components of
+the final graph; (ii) a zero-shot strategy for each prompt, meaning that there
+is no need for providing examples for ""guiding"" the prompt result; (iii) a
+scalable solution, as the adoption of LLMs avoids the need for any external
+resources or human expertise. To assess the effectiveness of our proposed
+model, we performed experiments on a dataset that covered a specific domain. We
+claim that our proposal is a suitable solution for scalable and versatile
+knowledge graph construction and may be applied to different and novel
+contexts.
+"
+PREFER: Prompt Ensemble Learning via Feedback-Reflect-Refine,Chenrui Zhang,http://arxiv.org/pdf/2308.12033v1.pdf,2023-08-23,"['cs.cl', 'cs.ai']",2308.12033v1.pdf,"  As an effective tool for eliciting the power of Large Language Models (LLMs),
+prompting has recently demonstrated unprecedented abilities across a variety of
+complex tasks. To further improve the performance, prompt ensemble has
+attracted substantial interest for tackling the hallucination and instability
+of LLMs. However, existing methods usually adopt a two-stage paradigm, which
+requires a pre-prepared set of prompts with substantial manual effort, and is
+unable to perform directed optimization for different weak learners. In this
+paper, we propose a simple, universal, and automatic method named PREFER (Pompt
+Ensemble learning via Feedback-Reflect-Refine) to address the stated
+limitations. Specifically, given the fact that weak learners are supposed to
+focus on hard examples during boosting, PREFER builds a feedback mechanism for
+reflecting on the inadequacies of existing weak learners. Based on this, the
+LLM is required to automatically synthesize new prompts for iterative
+refinement. Moreover, to enhance stability of the prompt effect evaluation, we
+propose a novel prompt bagging method involving forward and backward thinking,
+which is superior to majority voting and is beneficial for both feedback and
+weight calculation in boosting. Extensive experiments demonstrate that our
+PREFER achieves state-of-the-art performance in multiple types of tasks by a
+significant margin. We have made our code publicly available.
+"
+ABScribe: Rapid Exploration of Multiple Writing Variations in Human-AI  Co-Writing Tasks using Large Language Models,Mohi Reza,http://arxiv.org/pdf/2310.00117v2.pdf,2023-09-29,"['cs.hc', 'cs.ai', 'cs.lg']",2310.00117v2.pdf,"  Exploring alternative ideas by rewriting text is integral to the writing
+process. State-of-the-art large language models (LLMs) can simplify writing
+variation generation. However, current interfaces pose challenges for
+simultaneous consideration of multiple variations: creating new versions
+without overwriting text can be difficult, and pasting them sequentially can
+clutter documents, increasing workload and disrupting writers' flow. To tackle
+this, we present ABScribe, an interface that supports rapid, yet visually
+structured, exploration of writing variations in human-AI co-writing tasks.
+With ABScribe, users can swiftly produce multiple variations using LLM prompts,
+which are auto-converted into reusable buttons. Variations are stored
+adjacently within text segments for rapid in-place comparisons using mouse-over
+interactions on a context toolbar. Our user study with 12 writers shows that
+ABScribe significantly reduces task workload (d = 1.20, p < 0.001), enhances
+user perceptions of the revision process (d = 2.41, p < 0.001) compared to a
+popular baseline workflow, and provides insights into how writers explore
+variations using LLMs.
+"
+Knowledge Crosswords: Geometric Reasoning over Structured Knowledge with  Large Language Models,Wenxuan Ding,http://arxiv.org/pdf/2310.01290v1.pdf,2023-10-02,"['cs.cl', 'cs.ai']",2310.01290v1.pdf,"  Large language models (LLMs) are widely adopted in knowledge-intensive tasks
+and have achieved impressive performance thanks to their knowledge abilities.
+While LLMs have demonstrated outstanding performance on atomic or linear
+(multi-hop) QA tasks, whether they can reason in knowledge-rich scenarios with
+interweaving constraints remains an underexplored problem. In this work, we
+propose geometric reasoning over structured knowledge, where pieces of
+knowledge are connected in a graph structure and models need to fill in the
+missing information. Such geometric knowledge reasoning would require the
+ability to handle structured knowledge, reason with uncertainty, verify facts,
+and backtrack when an error occurs. We propose Knowledge Crosswords, a
+multi-blank QA dataset where each problem consists of a natural language
+question representing the geometric constraints of an incomplete entity
+network, where LLMs are tasked with working out the missing entities while
+meeting all factual constraints. Knowledge Crosswords contains 2,101 individual
+problems, covering various knowledge domains and further divided into three
+difficulty levels. We conduct extensive experiments to evaluate existing LLM
+prompting approaches on the Knowledge Crosswords benchmark. We additionally
+propose two new approaches, Staged Prompting and Verify-All, to augment LLMs'
+ability to backtrack and verify structured constraints. Our results demonstrate
+that while baseline approaches perform well on easier problems but struggle
+with hard ones, our proposed Verify-All outperforms other methods by a large
+margin and is more robust with hard problems. Further analysis reveals that
+LLMs' ability of geometric reasoning over structured knowledge is still far
+from robust or perfect, susceptible to confounders such as the order of
+options, certain structural patterns, assumption of existence of correct
+answer, and more.
+"
+Retrieval-augmented Generation to Improve Math Question-Answering:  Trade-offs Between Groundedness and Human Preference,Zachary Levonian,http://arxiv.org/pdf/2310.03184v1.pdf,2023-10-04,"['cs.cl', 'cs.hc']",2310.03184v1.pdf,"  For middle-school math students, interactive question-answering (QA) with
+tutors is an effective way to learn. The flexibility and emergent capabilities
+of generative large language models (LLMs) has led to a surge of interest in
+automating portions of the tutoring process - including interactive QA to
+support conceptual discussion of mathematical concepts. However, LLM responses
+to math questions can be incorrect or mismatched to the educational context -
+such as being misaligned with a school's curriculum. One potential solution is
+retrieval-augmented generation (RAG), which involves incorporating a vetted
+external knowledge source in the LLM prompt to increase response quality. In
+this paper, we designed prompts that retrieve and use content from a
+high-quality open-source math textbook to generate responses to real student
+questions. We evaluate the efficacy of this RAG system for middle-school
+algebra and geometry QA by administering a multi-condition survey, finding that
+humans prefer responses generated using RAG, but not when responses are too
+grounded in the textbook content. We argue that while RAG is able to improve
+response quality, designers of math QA systems must consider trade-offs between
+generating responses preferred by students and responses closely matched to
+specific educational resources.
+"
+Small Language Models Fine-tuned to Coordinate Larger Language Models  improve Complex Reasoning,Gurusha Juneja,http://arxiv.org/pdf/2310.18338v1.pdf,2023-10-21,"['cs.cl', 'cs.ai']",2310.18338v1.pdf,"  Large Language Models (LLMs) prompted to generate chain-of-thought (CoT)
+exhibit impressive reasoning capabilities. Recent attempts at prompt
+decomposition toward solving complex, multi-step reasoning problems depend on
+the ability of the LLM to simultaneously decompose and solve the problem. A
+significant disadvantage is that foundational LLMs are typically not available
+for fine-tuning, making adaptation computationally prohibitive. We believe (and
+demonstrate) that problem decomposition and solution generation are distinct
+capabilites, better addressed in separate modules, than by one monolithic LLM.
+We introduce DaSLaM, which uses a decomposition generator to decompose complex
+problems into subproblems that require fewer reasoning steps. These subproblems
+are answered by a solver. We use a relatively small (13B parameters) LM as the
+decomposition generator, which we train using policy gradient optimization to
+interact with a solver LM (regarded as black-box) and guide it through
+subproblems, thereby rendering our method solver-agnostic. Evaluation on
+multiple different reasoning datasets reveal that with our method, a 175
+billion parameter LM (text-davinci-003) can produce competitive or even better
+performance, compared to its orders-of-magnitude larger successor, GPT-4.
+Additionally, we show that DaSLaM is not limited by the solver's capabilities
+as a function of scale; e.g., solver LMs with diverse sizes give significant
+performance improvement with our solver-agnostic decomposition technique.
+Exhaustive ablation studies evince the superiority of our modular finetuning
+technique over exorbitantly large decomposer LLMs, based on prompting alone.
+"
+Universal Fuzzing via Large Language Models,Chunqiu Steven Xia,http://arxiv.org/pdf/2308.04748v1.pdf,2023-08-09,"['cs.se', 'cs.lg']",2308.04748v1.pdf,"  Fuzzing has achieved tremendous success in discovering bugs and
+vulnerabilities in various software systems. Systems under test (SUTs) that
+take in programming or formal language as inputs, e.g., compilers, runtime
+engines, constraint solvers, and software libraries with accessible APIs, are
+especially important as they are fundamental building blocks of software
+development. However, existing fuzzers for such systems often target a specific
+language, and thus cannot be easily applied to other languages or even other
+versions of the same language. Moreover, the inputs generated by existing
+fuzzers are often limited to specific features of the input language, and thus
+can hardly reveal bugs related to other or new features. This paper presents
+Fuzz4All, the first fuzzer that is universal in the sense that it can target
+many different input languages and many different features of these languages.
+The key idea behind Fuzz4All is to leverage large language models (LLMs) as an
+input generation and mutation engine, which enables the approach to produce
+diverse and realistic inputs for any practically relevant language. To realize
+this potential, we present a novel autoprompting technique, which creates LLM
+prompts that are wellsuited for fuzzing, and a novel LLM-powered fuzzing loop,
+which iteratively updates the prompt to create new fuzzing inputs. We evaluate
+Fuzz4All on nine systems under test that take in six different languages (C,
+C++, Go, SMT2, Java and Python) as inputs. The evaluation shows, across all six
+languages, that universal fuzzing achieves higher coverage than existing,
+language-specific fuzzers. Furthermore, Fuzz4All has identified 76 bugs in
+widely used systems, such as GCC, Clang, Z3, CVC5, OpenJDK, and the Qiskit
+quantum computing platform, with 47 bugs already confirmed by developers as
+previously unknown.
+"
+AI Chains: Transparent and Controllable Human-AI Interaction by Chaining  Large Language Model Prompts,Tongshuang Wu,http://arxiv.org/pdf/2110.01691v3.pdf,2021-10-04,"['cs.hc', 'cs.cl']",2110.01691v3.pdf,"  Although large language models (LLMs) have demonstrated impressive potential
+on simple tasks, their breadth of scope, lack of transparency, and insufficient
+controllability can make them less effective when assisting humans on more
+complex tasks. In response, we introduce the concept of Chaining LLM steps
+together, where the output of one step becomes the input for the next, thus
+aggregating the gains per step. We first define a set of LLM primitive
+operations useful for Chain construction, then present an interactive system
+where users can modify these Chains, along with their intermediate results, in
+a modular way. In a 20-person user study, we found that Chaining not only
+improved the quality of task outcomes, but also significantly enhanced system
+transparency, controllability, and sense of collaboration. Additionally, we saw
+that users developed new ways of interacting with LLMs through Chains: they
+leveraged sub-tasks to calibrate model expectations, compared and contrasted
+alternative strategies by observing parallel downstream effects, and debugged
+unexpected model outputs by ""unit-testing"" sub-components of a Chain. In two
+case studies, we further explore how LLM Chains may be used in future
+applications
+"
+PromptChainer: Chaining Large Language Model Prompts through Visual  Programming,Tongshuang Wu,http://arxiv.org/pdf/2203.06566v1.pdf,2022-03-13,['cs.hc'],2203.06566v1.pdf,"  While LLMs can effectively help prototype single ML functionalities, many
+real-world applications involve complex tasks that cannot be easily handled via
+a single run of an LLM. Recent work has found that chaining multiple LLM runs
+together (with the output of one step being the input to the next) can help
+users accomplish these more complex tasks, and in a way that is perceived to be
+more transparent and controllable. However, it remains unknown what users need
+when authoring their own LLM chains -- a key step for lowering the barriers for
+non-AI-experts to prototype AI-infused applications. In this work, we explore
+the LLM chain authoring process. We conclude from pilot studies find that
+chaining requires careful scaffolding for transforming intermediate node
+outputs, as well as debugging the chain at multiple granularities; to help with
+these needs, we designed PromptChainer, an interactive interface for visually
+programming chains. Through case studies with four people, we show that
+PromptChainer supports building prototypes for a range of applications, and
+conclude with open questions on scaling chains to complex tasks, and supporting
+low-fi chain prototyping.
+"
+Few-shot Reranking for Multi-hop QA via Language Model Prompting,Muhammad Khalifa,http://arxiv.org/pdf/2205.12650v3.pdf,2022-05-25,"['cs.cl', 'cs.ir']",2205.12650v3.pdf,"  We study few-shot reranking for multi-hop QA with open-domain questions. To
+alleviate the need for a large number of labeled question-document pairs for
+retriever training, we propose PromptRank, which relies on large language
+models prompting for multi-hop path reranking. PromptRank first constructs an
+instruction-based prompt that includes a candidate document path and then
+computes the relevance score between a given question and the path based on the
+conditional likelihood of the question given the path prompt according to a
+language model. PromptRank yields strong retrieval performance on HotpotQA with
+only 128 training examples compared to state-of-the-art methods trained on
+thousands of examples -- 73.6 recall@10 by PromptRank vs. 77.8 by PathRetriever
+and 77.5 by multi-hop dense retrieval. Code available at
+https://github.com/mukhal/PromptRank
+"
+Flatness-Aware Prompt Selection Improves Accuracy and Sample Efficiency,Lingfeng Shen,http://arxiv.org/pdf/2305.10713v2.pdf,2023-05-18,"['cs.cl', 'cs.lg']",2305.10713v2.pdf,"  With growing capabilities of large language models, prompting them has become
+the dominant way to access them. This has motivated the development of
+strategies for automatically selecting effective language prompts. In this
+paper, we introduce prompt flatness, a new metric to quantify the expected
+utility of a language prompt. This metric is inspired by flatness
+regularization in statistical learning that quantifies the robustness of the
+model towards its parameter perturbations. We provide theoretical foundations
+for this metric and its relationship with other prompt selection metrics,
+providing a comprehensive understanding of existing methods. Empirically, we
+show that combining prompt flatness with existing metrics improves both
+performance and sample efficiency. Our metric outperforms the previous prompt
+selection metrics with an average increase of 5% in accuracy and 10% in Pearson
+correlation across 6 classification benchmarks.
+"
+A Monte Carlo Language Model Pipeline for Zero-Shot Sociopolitical Event  Extraction,Erica Cai,http://arxiv.org/pdf/2305.15051v1.pdf,2023-05-24,['cs.cl'],2305.15051v1.pdf,"  We consider dyadic zero-shot event extraction (EE) to identify actions
+between pairs of actors. The \emph{zero-shot} setting allows social scientists
+or other non-computational researchers to extract any customized,
+user-specified set of events without training, resulting in a \emph{dyadic}
+event database, allowing insight into sociopolitical relational dynamics among
+actors and the higher level organizations or countries they represent.
+Unfortunately, we find that current zero-shot EE methods perform poorly for the
+task, with issues including word sense ambiguity, modality mismatch, and
+efficiency. Straightforward application of large language model prompting
+typically performs even worse. We address these challenges with a new
+fine-grained, multi-stage generative question-answer method, using a Monte
+Carlo approach to exploit and overcome the randomness of generative outputs. It
+performs 90\% fewer queries than a previous approach, with strong performance
+on the widely-used Automatic Content Extraction dataset. Finally, we extend our
+method to extract affiliations of actor arguments and demonstrate our method
+and findings on a dyadic international relations case study.
+"
+EvalLM: Interactive Evaluation of Large Language Model Prompts on  User-Defined Criteria,Tae Soo Kim,http://arxiv.org/pdf/2309.13633v1.pdf,2023-09-24,"['cs.hc', 'cs.ai', 'cs.cl']",2309.13633v1.pdf,"  By simply composing prompts, developers can prototype novel generative
+applications with Large Language Models (LLMs). To refine prototypes into
+products, however, developers must iteratively revise prompts by evaluating
+outputs to diagnose weaknesses. Formative interviews (N=8) revealed that
+developers invest significant effort in manually evaluating outputs as they
+assess context-specific and subjective criteria. We present EvalLM, an
+interactive system for iteratively refining prompts by evaluating multiple
+outputs on user-defined criteria. By describing criteria in natural language,
+users can employ the system's LLM-based evaluator to get an overview of where
+prompts excel or fail, and improve these based on the evaluator's feedback. A
+comparative study (N=12) showed that EvalLM, when compared to manual
+evaluation, helped participants compose more diverse criteria, examine twice as
+many outputs, and reach satisfactory prompts with 59% fewer revisions. Beyond
+prompts, our work can be extended to augment model evaluation and alignment in
+specific application contexts.
+"
+Terminology-Aware Translation with Constrained Decoding and Large  Language Model Prompting,Nikolay Bogoychev,http://arxiv.org/pdf/2310.05824v1.pdf,2023-10-09,['cs.cl'],2310.05824v1.pdf,"  Terminology correctness is important in the downstream application of machine
+translation, and a prevalent way to ensure this is to inject terminology
+constraints into a translation system. In our submission to the WMT 2023
+terminology translation task, we adopt a translate-then-refine approach which
+can be domain-independent and requires minimal manual efforts. We annotate
+random source words with pseudo-terminology translations obtained from word
+alignment to first train a terminology-aware model. Further, we explore two
+post-processing methods. First, we use an alignment process to discover whether
+a terminology constraint has been violated, and if so, we re-decode with the
+violating word negatively constrained. Alternatively, we leverage a large
+language model to refine a hypothesis by providing it with terminology
+constraints. Results show that our terminology-aware model learns to
+incorporate terminologies effectively, and the large language model refinement
+process can further improve terminology recall.
+"
+Prompter: Utilizing Large Language Model Prompting for a Data Efficient  Embodied Instruction Following,Yuki Inoue,http://arxiv.org/pdf/2211.03267v1.pdf,2022-11-07,"['cs.ro', 'cs.cv']",2211.03267v1.pdf,"  Embodied Instruction Following (EIF) studies how mobile manipulator robots
+should be controlled to accomplish long-horizon tasks specified by natural
+language instructions. While most research on EIF are conducted in simulators,
+the ultimate goal of the field is to deploy the agents in real life. As such,
+it is important to minimize the data cost required for training an agent, to
+help the transition from sim to real. However, many studies only focus on the
+performance and overlook the data cost -- modules that require separate
+training on extra data are often introduced without a consideration on
+deployability. In this work, we propose FILM++ which extends the existing work
+FILM with modifications that do not require extra data. While all data-driven
+modules are kept constant, FILM++ more than doubles FILM's performance.
+Furthermore, we propose Prompter, which replaces FILM++'s semantic search
+module with language model prompting. Unlike FILM++'s implementation that
+requires training on extra sets of data, no training is needed for our
+prompting based implementation while achieving better or at least comparable
+performance. Prompter achieves 42.64% and 45.72% on the ALFRED benchmark with
+high-level instructions only and with step-by-step instructions, respectively,
+outperforming the previous state of the art by 6.57% and 10.31%.
+"
+FIRE: Food Image to REcipe generation,Prateek Chhikara,http://arxiv.org/pdf/2308.14391v1.pdf,2023-08-28,"['cs.cv', 'cs.cl']",2308.14391v1.pdf,"  Food computing has emerged as a prominent multidisciplinary field of research
+in recent years. An ambitious goal of food computing is to develop end-to-end
+intelligent systems capable of autonomously producing recipe information for a
+food image. Current image-to-recipe methods are retrieval-based and their
+success depends heavily on the dataset size and diversity, as well as the
+quality of learned embeddings. Meanwhile, the emergence of powerful
+attention-based vision and language models presents a promising avenue for
+accurate and generalizable recipe generation, which has yet to be extensively
+explored. This paper proposes FIRE, a novel multimodal methodology tailored to
+recipe generation in the food computing domain, which generates the food title,
+ingredients, and cooking instructions based on input food images. FIRE
+leverages the BLIP model to generate titles, utilizes a Vision Transformer with
+a decoder for ingredient extraction, and employs the T5 model to generate
+recipes incorporating titles and ingredients as inputs. We showcase two
+practical applications that can benefit from integrating FIRE with large
+language model prompting: recipe customization to fit recipes to user
+preferences and recipe-to-code transformation to enable automated cooking
+processes. Our experimental findings validate the efficacy of our proposed
+approach, underscoring its potential for future advancements and widespread
+adoption in food computing.
+"
+Large language models can accurately predict searcher preferences,Paul Thomas,http://arxiv.org/pdf/2309.10621v1.pdf,2023-09-19,"['cs.ir', 'cs.ai', 'cs.cl', 'cs.lg']",2309.10621v1.pdf,"  Relevance labels, which indicate whether a search result is valuable to a
+searcher, are key to evaluating and optimising search systems. The best way to
+capture the true preferences of users is to ask them for their careful feedback
+on which results would be useful, but this approach does not scale to produce a
+large number of labels. Getting relevance labels at scale is usually done with
+third-party labellers, who judge on behalf of the user, but there is a risk of
+low-quality data if the labeller doesn't understand user needs. To improve
+quality, one standard approach is to study real users through interviews, user
+studies and direct feedback, find areas where labels are systematically
+disagreeing with users, then educate labellers about user needs through judging
+guidelines, training and monitoring. This paper introduces an alternate
+approach for improving label quality. It takes careful feedback from real
+users, which by definition is the highest-quality first-party gold data that
+can be derived, and develops an large language model prompt that agrees with
+that data.
+  We present ideas and observations from deploying language models for
+large-scale relevance labelling at Bing, and illustrate with data from TREC. We
+have found large language models can be effective, with accuracy as good as
+human labellers and similar capability to pick the hardest queries, best runs,
+and best groups. Systematic changes to the prompts make a difference in
+accuracy, but so too do simple paraphrases. To measure agreement with real
+searchers needs high-quality ``gold'' labels, but with these we find that
+models produce better labels than third-party workers, for a fraction of the
+cost, and these labels let us train notably better rankers.
+"
+Meta-in-context learning in large language models,Julian Coda-Forno,http://arxiv.org/pdf/2305.12907v1.pdf,2023-05-22,"['cs.cl', 'cs.ai', 'cs.lg']",2305.12907v1.pdf,"  Large language models have shown tremendous performance in a variety of
+tasks. In-context learning -- the ability to improve at a task after being
+provided with a number of demonstrations -- is seen as one of the main
+contributors to their success. In the present paper, we demonstrate that the
+in-context learning abilities of large language models can be recursively
+improved via in-context learning itself. We coin this phenomenon
+meta-in-context learning. Looking at two idealized domains, a one-dimensional
+regression task and a two-armed bandit task, we show that meta-in-context
+learning adaptively reshapes a large language model's priors over expected
+tasks. Furthermore, we find that meta-in-context learning modifies the
+in-context learning strategies of such models. Finally, we extend our approach
+to a benchmark of real-world regression problems where we observe competitive
+performance to traditional learning algorithms. Taken together, our work
+improves our understanding of in-context learning and paves the way toward
+adapting large language models to the environment they are applied purely
+through meta-in-context learning rather than traditional finetuning.
+"
+MetaVL: Transferring In-Context Learning Ability From Language Models to  Vision-Language Models,Masoud Monajatipoor,http://arxiv.org/pdf/2306.01311v1.pdf,2023-06-02,['cs.cl'],2306.01311v1.pdf,"  Large-scale language models have shown the ability to adapt to a new task via
+conditioning on a few demonstrations (i.e., in-context learning). However, in
+the vision-language domain, most large-scale pre-trained vision-language (VL)
+models do not possess the ability to conduct in-context learning. How can we
+enable in-context learning for VL models? In this paper, we study an
+interesting hypothesis: can we transfer the in-context learning ability from
+the language domain to VL domain? Specifically, we first meta-trains a language
+model to perform in-context learning on NLP tasks (as in MetaICL); then we
+transfer this model to perform VL tasks by attaching a visual encoder. Our
+experiments suggest that indeed in-context learning ability can be transferred
+cross modalities: our model considerably improves the in-context learning
+capability on VL tasks and can even compensate for the size of the model
+significantly. On VQA, OK-VQA, and GQA, our method could outperform the
+baseline model while having 20 times fewer parameters.
+"
+A Theory of Emergent In-Context Learning as Implicit Structure Induction,Michael Hahn,http://arxiv.org/pdf/2303.07971v1.pdf,2023-03-14,"['cs.cl', 'cs.lg']",2303.07971v1.pdf,"  Scaling large language models (LLMs) leads to an emergent capacity to learn
+in-context from example demonstrations. Despite progress, theoretical
+understanding of this phenomenon remains limited. We argue that in-context
+learning relies on recombination of compositional operations found in natural
+language data. We derive an information-theoretic bound showing how in-context
+learning abilities arise from generic next-token prediction when the
+pretraining distribution has sufficient amounts of compositional structure,
+under linguistically motivated assumptions. A second bound provides a
+theoretical justification for the empirical success of prompting LLMs to output
+intermediate steps towards an answer. To validate theoretical predictions, we
+introduce a controlled setup for inducing in-context learning; unlike previous
+approaches, it accounts for the compositional nature of language. Trained
+transformers can perform in-context learning for a range of tasks, in a manner
+consistent with the theoretical results. Mirroring real-world LLMs in a
+miniature setup, in-context learning emerges when scaling parameters and data,
+and models perform better when prompted to output intermediate steps. Probing
+shows that in-context learning is supported by a representation of the input's
+compositional structure. Taken together, these results provide a step towards
+theoretical understanding of emergent behavior in large language models.
+"
+Fine-tune Language Models to Approximate Unbiased In-context Learning,Timothy Chu,http://arxiv.org/pdf/2310.03331v1.pdf,2023-10-05,['cs.lg'],2310.03331v1.pdf,"  In-context learning (ICL) is an astonishing emergent ability of large
+language models (LLMs). By presenting a prompt that includes multiple
+input-output pairs as examples and introducing a new query input, models can
+generate the corresponding output. However, the performance of models heavily
+relies on the quality of the input prompt when implementing in-context
+learning. Biased or imbalanced input prompts can significantly degrade the
+performance of language models. To address this issue, we introduce a
+reweighted algorithm called RICL (Reweighted In-context Learning). This
+algorithm fine-tunes language models using an unbiased validation set to
+determine the optimal weight for each input-output example to approximate
+unbiased in-context learning. Furthermore, we also introduce a low-cost
+reweighted algorithm, a linear optimal weight approximation algorithm called
+LARICL (Linear Approximation of Reweighted In-context Learning). This algorithm
+requires minimal training cost while providing effective results. We prove the
+convergence of our algorithm and validate its performance through experiments
+conducted on a numerical dataset. The experimental findings reveal a
+substantial improvement in comparison to benchmarks including the performance
+of casual prompt-based in-context learning and the performance of a classic
+fine-tuning method.
+"
+PRODIGY: Enabling In-context Learning Over Graphs,Qian Huang,http://arxiv.org/pdf/2305.12600v1.pdf,2023-05-21,"['cs.lg', 'cs.ai']",2305.12600v1.pdf,"  In-context learning is the ability of a pretrained model to adapt to novel
+and diverse downstream tasks by conditioning on prompt examples, without
+optimizing any parameters. While large language models have demonstrated this
+ability, how in-context learning could be performed over graphs is unexplored.
+In this paper, we develop \textbf{Pr}etraining \textbf{O}ver \textbf{D}iverse
+\textbf{I}n-Context \textbf{G}raph S\textbf{y}stems (PRODIGY), the first
+pretraining framework that enables in-context learning over graphs. The key
+idea of our framework is to formulate in-context learning over graphs with a
+novel \emph{prompt graph} representation, which connects prompt examples and
+queries. We then propose a graph neural network architecture over the prompt
+graph and a corresponding family of in-context pretraining objectives. With
+PRODIGY, the pretrained model can directly perform novel downstream
+classification tasks on unseen graphs via in-context learning. We provide
+empirical evidence of the effectiveness of our framework by showcasing its
+strong in-context learning performance on tasks involving citation networks and
+knowledge graphs. Our approach outperforms the in-context learning accuracy of
+contrastive pretraining baselines with hard-coded adaptation by 18\% on average
+across all setups. Moreover, it also outperforms standard finetuning with
+limited data by 33\% on average with in-context learning.
+"
+An Explanation of In-context Learning as Implicit Bayesian Inference,Sang Michael Xie,http://arxiv.org/pdf/2111.02080v6.pdf,2021-11-03,"['cs.cl', 'cs.lg']",2111.02080v6.pdf,"  Large language models (LMs) such as GPT-3 have the surprising ability to do
+in-context learning, where the model learns to do a downstream task simply by
+conditioning on a prompt consisting of input-output examples. The LM learns
+from these examples without being explicitly pretrained to learn. Thus, it is
+unclear what enables in-context learning. In this paper, we study how
+in-context learning can emerge when pretraining documents have long-range
+coherence. Here, the LM must infer a latent document-level concept to generate
+coherent next tokens during pretraining. At test time, in-context learning
+occurs when the LM also infers a shared latent concept between examples in a
+prompt. We prove when this occurs despite a distribution mismatch between
+prompts and pretraining data in a setting where the pretraining distribution is
+a mixture of HMMs. In contrast to messy large-scale datasets used to train LMs
+capable of in-context learning, we generate a small-scale synthetic dataset
+(GINC) where Transformers and LSTMs both exhibit in-context learning. Beyond
+the theory, experiments on GINC exhibit large-scale real-world phenomena
+including improved in-context performance with model scaling (despite the same
+pretraining loss), sensitivity to example order, and instances where zero-shot
+is better than few-shot in-context learning.
+"
+Rethinking the Role of Scale for In-Context Learning: An  Interpretability-based Case Study at 66 Billion Scale,Hritik Bansal,http://arxiv.org/pdf/2212.09095v2.pdf,2022-12-18,"['cs.cl', 'cs.ai']",2212.09095v2.pdf,"  Language models have been shown to perform better with an increase in scale
+on a wide variety of tasks via the in-context learning paradigm. In this paper,
+we investigate the hypothesis that the ability of a large language model to
+in-context learn-perform a task is not uniformly spread across all of its
+underlying components. Using a 66 billion parameter language model (OPT-66B)
+across a diverse set of 14 downstream tasks, we find this is indeed the case:
+$\sim$70% of attention heads and $\sim$20% of feed forward networks can be
+removed with minimal decline in task performance. We find substantial overlap
+in the set of attention heads (un)important for in-context learning across
+tasks and number of in-context examples. We also address our hypothesis through
+a task-agnostic lens, finding that a small set of attention heads in OPT-66B
+score highly on their ability to perform primitive induction operations
+associated with in-context learning, namely, prefix matching and copying. These
+induction heads overlap with task-specific important heads, reinforcing
+arguments by Olsson et al. (arXiv:2209.11895) regarding induction head
+generality to more sophisticated behaviors associated with in-context learning.
+Overall, our study provides several insights that indicate large language
+models may be under-trained for in-context learning and opens up questions on
+how to pre-train language models to more effectively perform in-context
+learning.
+"
+A Closer Look at In-Context Learning under Distribution Shifts,Kartik Ahuja,http://arxiv.org/pdf/2305.16704v1.pdf,2023-05-26,"['cs.lg', 'stat.ml']",2305.16704v1.pdf,"  In-context learning, a capability that enables a model to learn from input
+examples on the fly without necessitating weight updates, is a defining
+characteristic of large language models. In this work, we follow the setting
+proposed in (Garg et al., 2022) to better understand the generality and
+limitations of in-context learning from the lens of the simple yet fundamental
+task of linear regression. The key question we aim to address is: Are
+transformers more adept than some natural and simpler architectures at
+performing in-context learning under varying distribution shifts? To compare
+transformers, we propose to use a simple architecture based on set-based
+Multi-Layer Perceptrons (MLPs). We find that both transformers and set-based
+MLPs exhibit in-context learning under in-distribution evaluations, but
+transformers more closely emulate the performance of ordinary least squares
+(OLS). Transformers also display better resilience to mild distribution shifts,
+where set-based MLPs falter. However, under severe distribution shifts, both
+models' in-context learning abilities diminish.
+"
+Exploring the Relationship Between Model Architecture and In-Context  Learning Ability,Ivan Lee,http://arxiv.org/pdf/2310.08049v1.pdf,2023-10-12,['cs.lg'],2310.08049v1.pdf,"  What is the relationship between model architecture and the ability to
+perform in-context learning? In this empirical study, we take the first steps
+towards answering this question. In particular, we evaluate fifteen model
+architectures across a suite of synthetic in-context learning tasks. The
+selected architectures represent a broad range of paradigms, including
+recurrent and convolution-based neural networks, transformers, and emerging
+attention alternatives. We discover that all considered architectures can
+perform in-context learning under certain conditions. However, contemporary
+architectures are found to be the best performing, especially as task
+complexity grows. Additionally, our follow-up experiments delve into various
+factors that influence in-context learning. We observe varied sensitivities
+among architectures with respect to hyperparameter settings. Our study of
+training dynamics reveals that certain architectures exhibit a smooth,
+progressive learning trajectory, while others demonstrate periods of stagnation
+followed by abrupt mastery of the task. Finally, and somewhat surprisingly, we
+find that several emerging attention alternatives are more robust in-context
+learners than transformers; since such approaches have constant-sized memory
+footprints at inference time, this result opens the future possibility of
+scaling up in-context learning to vastly larger numbers of in-context examples.
+"
+What Can Transformers Learn In-Context? A Case Study of Simple Function  Classes,Shivam Garg,http://arxiv.org/pdf/2208.01066v3.pdf,2022-08-01,"['cs.cl', 'cs.lg']",2208.01066v3.pdf,"  In-context learning refers to the ability of a model to condition on a prompt
+sequence consisting of in-context examples (input-output pairs corresponding to
+some task) along with a new query input, and generate the corresponding output.
+Crucially, in-context learning happens only at inference time without any
+parameter updates to the model. While large language models such as GPT-3
+exhibit some ability to perform in-context learning, it is unclear what the
+relationship is between tasks on which this succeeds and what is present in the
+training data. To make progress towards understanding in-context learning, we
+consider the well-defined problem of training a model to in-context learn a
+function class (e.g., linear functions): that is, given data derived from some
+functions in the class, can we train a model to in-context learn ""most""
+functions from this class? We show empirically that standard Transformers can
+be trained from scratch to perform in-context learning of linear functions --
+that is, the trained model is able to learn unseen linear functions from
+in-context examples with performance comparable to the optimal least squares
+estimator. In fact, in-context learning is possible even under two forms of
+distribution shift: (i) between the training data of the model and
+inference-time prompts, and (ii) between the in-context examples and the query
+input during inference. We also show that we can train Transformers to
+in-context learn more complex function classes -- namely sparse linear
+functions, two-layer neural networks, and decision trees -- with performance
+that matches or exceeds task-specific learning algorithms. Our code and models
+are available at https://github.com/dtsip/in-context-learning .
+"
+"Structured Prompting: Scaling In-Context Learning to 1,000 Examples",Yaru Hao,http://arxiv.org/pdf/2212.06713v1.pdf,2022-12-13,['cs.cl'],2212.06713v1.pdf,"  Large language models have exhibited intriguing in-context learning
+capability, achieving promising zero- and few-shot performance without updating
+the parameters. However, conventional in-context learning is usually restricted
+by length constraints, rendering it ineffective to absorb supervision from a
+large number of examples. In order to go beyond few shots, we introduce
+structured prompting that breaks the length limit and scales in-context
+learning to thousands of examples. Specifically, demonstration examples are
+separately encoded with well-designed position embeddings, and then they are
+jointly attended by the test example using a rescaled attention mechanism. So
+we can scale the number of exemplars with linear complexity instead of
+quadratic complexity with respect to length. Experimental results on a diverse
+set of tasks show that our approach improves end-task performance and reduces
+evaluation variance over conventional in-context learning as the number of
+demonstration examples increases. Code has been released at
+https://aka.ms/structured-prompting.
+"
+Pre-Training to Learn in Context,Yuxian Gu,http://arxiv.org/pdf/2305.09137v1.pdf,2023-05-16,['cs.cl'],2305.09137v1.pdf,"  In-context learning, where pre-trained language models learn to perform tasks
+from task examples and instructions in their contexts, has attracted much
+attention in the NLP community. However, the ability of in-context learning is
+not fully exploited because language models are not explicitly trained to learn
+in context. To this end, we propose PICL (Pre-training for In-Context
+Learning), a framework to enhance the language models' in-context learning
+ability by pre-training the model on a large collection of ""intrinsic tasks"" in
+the general plain-text corpus using the simple language modeling objective.
+PICL encourages the model to infer and perform tasks by conditioning on the
+contexts while maintaining task generalization of pre-trained models. We
+evaluate the in-context learning performance of the model trained with PICL on
+seven widely-used text classification datasets and the Super-NaturalInstrctions
+benchmark, which contains 100+ NLP tasks formulated to text generation. Our
+experiments show that PICL is more effective and task-generalizable than a
+range of baselines, outperforming larger language models with nearly 4x
+parameters. The code is publicly available at https://github.com/thu-coai/PICL.
+"
+EXnet: Efficient In-context Learning for Data-less Text classification,Debaditya Shome,http://arxiv.org/pdf/2305.14622v1.pdf,2023-05-24,"['cs.cl', 'cs.lg']",2305.14622v1.pdf,"  Large pre-trained language models (PLMs) have made significant progress in
+encoding world knowledge and spawned a new set of learning paradigms including
+zero-shot, few-shot, and in-context learning. Many language tasks can be
+modeled as a set of prompts (for example, is this text about geography?) and
+language models can provide binary answers, i.e., Yes or No. There is evidence
+to suggest that the next-word prediction used by many PLMs does not align well
+with zero-shot paradigms. Therefore, PLMs are fine-tuned as a
+question-answering system. In-context learning extends zero-shot learning by
+incorporating prompts and examples, resulting in increased task accuracy. Our
+paper presents EXnet, a model specifically designed to perform in-context
+learning without any limitations on the number of examples. We argue that
+in-context learning is an effective method to increase task accuracy, and
+providing examples facilitates cross-task generalization, especially when it
+comes to text classification tasks. With extensive experiments, we show that
+even our smallest model (15M parameters) generalizes to several unseen
+classification tasks and domains.
+"
+RAVEN: In-Context Learning with Retrieval Augmented Encoder-Decoder  Language Models,Jie Huang,http://arxiv.org/pdf/2308.07922v1.pdf,2023-08-15,"['cs.cl', 'cs.ai', 'cs.lg']",2308.07922v1.pdf,"  In this paper, we investigate the in-context learning ability of
+retrieval-augmented encoder-decoder language models. We first conduct a
+comprehensive analysis of the state-of-the-art ATLAS model and identify its
+limitations in in-context learning, primarily due to a mismatch between
+pretraining and testing, as well as a restricted context length. To address
+these issues, we propose RAVEN, a model that combines retrieval-augmented
+masked language modeling and prefix language modeling. We further introduce
+Fusion-in-Context Learning to enhance the few-shot performance by enabling the
+model to leverage more in-context examples without requiring additional
+training or model modifications. Through extensive experiments, we demonstrate
+that RAVEN significantly outperforms ATLAS and achieves results comparable to
+the most advanced language models in certain scenarios, despite having
+substantially fewer parameters. Our work underscores the potential of
+retrieval-augmented encoder-decoder language models for in-context learning and
+encourages further research in this direction.
+"
+Understanding In-Context Learning from Repetitions,Jianhao Yan,http://arxiv.org/pdf/2310.00297v2.pdf,2023-09-30,['cs.cl'],2310.00297v2.pdf,"  This paper explores the elusive mechanism underpinning in-context learning in
+Large Language Models (LLMs). Our work provides a novel perspective by
+examining in-context learning via the lens of surface repetitions. We
+quantitatively investigate the role of surface features in text generation, and
+empirically establish the existence of \emph{token co-occurrence
+reinforcement}, a principle that strengthens the relationship between two
+tokens based on their contextual co-occurrences. By investigating the dual
+impacts of these features, our research illuminates the internal workings of
+in-context learning and expounds on the reasons for its failures. This paper
+provides an essential contribution to the understanding of in-context learning
+and its potential limitations, providing a fresh perspective on this exciting
+capability.
+"
+In-Context Learning Dynamics with Random Binary Sequences,Eric J. Bigelow,http://arxiv.org/pdf/2310.17639v1.pdf,2023-10-26,"['cs.ai', 'cs.cl', 'cs.lg']",2310.17639v1.pdf,"  Large language models (LLMs) trained on huge corpora of text datasets
+demonstrate complex, emergent capabilities, achieving state-of-the-art
+performance on tasks they were not explicitly trained for. The precise nature
+of LLM capabilities is often mysterious, and different prompts can elicit
+different capabilities through in-context learning. We propose a Cognitive
+Interpretability framework that enables us to analyze in-context learning
+dynamics to understand latent concepts in LLMs underlying behavioral patterns.
+This provides a more nuanced understanding than success-or-failure evaluation
+benchmarks, but does not require observing internal activations as a
+mechanistic interpretation of circuits would. Inspired by the cognitive science
+of human randomness perception, we use random binary sequences as context and
+study dynamics of in-context learning by manipulating properties of context
+data, such as sequence length. In the latest GPT-3.5+ models, we find emergent
+abilities to generate pseudo-random numbers and learn basic formal languages,
+with striking in-context learning dynamics where model outputs transition
+sharply from pseudo-random behaviors to deterministic repetition.
+"
+In-Context Learning with Many Demonstration Examples,Mukai Li,http://arxiv.org/pdf/2302.04931v1.pdf,2023-02-09,"['cs.cl', 'cs.ai']",2302.04931v1.pdf,"  Large pre-training language models (PLMs) have shown promising in-context
+learning abilities. However, due to the backbone transformer architecture,
+existing PLMs are bottlenecked by the memory and computational cost when
+scaling up to a large context size, leaving instruction tuning and in-context
+learning of many demonstration examples, as well as long-range language
+modeling under-explored. In this study, we propose a long-range language model
+EVALM based on an efficient transformer mechanism. EVALM is trained with 8k
+tokens per batch line and can test up to 256k-lengthed contexts with
+extrapolation, 128 times to the limit of existing PLMs (e.g. GPT3). Based on
+EVALM, we scale up the size of examples efficiently in both instruction tuning
+and in-context learning to explore the boundary of the benefits from more
+annotated data. Experimental results on a diverse set of tasks show that EVALM
+achieves 4.1% higher accuracy on average, and the average length of achieving
+the best accuracy score over tasks is around 12k. We find that in-context
+learning can achieve higher performance with more demonstrations under
+many-shot instruction tuning (8k), and further extending the length of
+instructions (16k) can further improve the upper bound of scaling in-context
+learning.
+"
+The Learnability of In-Context Learning,Noam Wies,http://arxiv.org/pdf/2303.07895v1.pdf,2023-03-14,['cs.cl'],2303.07895v1.pdf,"  In-context learning is a surprising and important phenomenon that emerged
+when modern language models were scaled to billions of learned parameters.
+Without modifying a large language model's weights, it can be tuned to perform
+various downstream natural language tasks simply by including concatenated
+training examples of these tasks in its input. Though disruptive for many
+practical applications of large language models, this emergent learning
+paradigm is not well understood from a theoretical perspective. In this paper,
+we propose a first-of-its-kind PAC based framework for in-context learnability,
+and use it to provide the first finite sample complexity results for the
+in-context learning setup. Our framework includes an initial pretraining phase,
+which fits a function to the pretraining distribution, and then a second
+in-context learning phase, which keeps this function constant and concatenates
+training examples of the downstream task in its input. We use our framework in
+order to prove that, under mild assumptions, when the pretraining distribution
+is a mixture of latent tasks (a model often considered for natural language
+pretraining), these tasks can be efficiently learned via in-context learning,
+even though the model's weights are unchanged and the input significantly
+diverges from the pretraining distribution. Our theoretical analysis reveals
+that in this setting, in-context learning is more about identifying the task
+than about learning it, a result which is in line with a series of recent
+empirical findings. We hope that the in-context learnability framework
+presented in this paper will facilitate future progress towards a deeper
+understanding of this important new learning paradigm.
+"
+SINC: Self-Supervised In-Context Learning for Vision-Language Tasks,Yi-Syuan Chen,http://arxiv.org/pdf/2307.07742v2.pdf,2023-07-15,"['cs.cv', 'cs.ai']",2307.07742v2.pdf,"  Large Pre-trained Transformers exhibit an intriguing capacity for in-context
+learning. Without gradient updates, these models can rapidly construct new
+predictors from demonstrations presented in the inputs. Recent works promote
+this ability in the vision-language domain by incorporating visual information
+into large language models that can already make in-context predictions.
+However, these methods could inherit issues in the language domain, such as
+template sensitivity and hallucination. Also, the scale of these language
+models raises a significant demand for computations, making learning and
+operating these models resource-intensive. To this end, we raise a question:
+``How can we enable in-context learning without relying on the intrinsic
+in-context ability of large language models?"". To answer it, we propose a
+succinct and general framework, Self-supervised IN-Context learning (SINC),
+that introduces a meta-model to learn on self-supervised prompts consisting of
+tailored demonstrations. The learned models can be transferred to downstream
+tasks for making in-context predictions on-the-fly. Extensive experiments show
+that SINC outperforms gradient-based methods in various vision-language tasks
+under few-shot settings. Furthermore, the designs of SINC help us investigate
+the benefits of in-context learning across different tasks, and the analysis
+further reveals the essential components for the emergence of in-context
+learning in the vision-language domain.
+"
+Self-Generated In-Context Learning: Leveraging Auto-regressive Language  Models as a Demonstration Generator,Hyuhng Joon Kim,http://arxiv.org/pdf/2206.08082v1.pdf,2022-06-16,['cs.cl'],2206.08082v1.pdf,"  Large-scale pre-trained language models (PLMs) are well-known for being
+capable of solving a task simply by conditioning a few input-label pairs dubbed
+demonstrations on a prompt without being explicitly tuned for the desired
+downstream task. Such a process (i.e., in-context learning), however, naturally
+leads to high reliance on the demonstrations which are usually selected from
+external datasets. In this paper, we propose self-generated in-context learning
+(SG-ICL), which generates demonstrations for in-context learning from PLM
+itself to minimize the reliance on the external demonstration. We conduct
+experiments on four different text classification tasks and show SG-ICL
+significantly outperforms zero-shot learning and is generally worth
+approximately 0.6 gold training samples. Moreover, our generated demonstrations
+show more consistent performance with low variance compared to randomly
+selected demonstrations from the training dataset.
+"
+Active Example Selection for In-Context Learning,Yiming Zhang,http://arxiv.org/pdf/2211.04486v1.pdf,2022-11-08,"['cs.cl', 'cs.ai']",2211.04486v1.pdf,"  With a handful of demonstration examples, large-scale language models show
+strong capability to perform various tasks by in-context learning from these
+examples, without any fine-tuning. We demonstrate that in-context learning
+performance can be highly unstable across samples of examples, indicating the
+idiosyncrasies of how language models acquire information. We formulate example
+selection for in-context learning as a sequential decision problem, and propose
+a reinforcement learning algorithm for identifying generalizable policies to
+select demonstration examples. For GPT-2, our learned policies demonstrate
+strong abilities of generalizing to unseen tasks in training, with a $5.8\%$
+improvement on average. Examples selected from our learned policies can even
+achieve a small improvement on GPT-3 Ada. However, the improvement diminishes
+on larger GPT-3 models, suggesting emerging capabilities of large language
+models.
+"
+On the Compositional Generalization Gap of In-Context Learning,Arian Hosseini,http://arxiv.org/pdf/2211.08473v1.pdf,2022-11-15,"['cs.cl', 'cs.lg']",2211.08473v1.pdf,"  Pretrained large generative language models have shown great performance on
+many tasks, but exhibit low compositional generalization abilities. Scaling
+such models has been shown to improve their performance on various NLP tasks
+even just by conditioning them on a few examples to solve the task without any
+fine-tuning (also known as in-context learning). In this work, we look at the
+gap between the in-distribution (ID) and out-of-distribution (OOD) performance
+of such models in semantic parsing tasks with in-context learning. In the ID
+settings, the demonstrations are from the same split (test or train) that the
+model is being evaluated on, and in the OOD settings, they are from the other
+split. We look at how the relative generalization gap of in-context learning
+evolves as models are scaled up. We evaluate four model families, OPT, BLOOM,
+CodeGen and Codex on three semantic parsing datasets, CFQ, SCAN and GeoQuery
+with different number of exemplars, and observe a trend of decreasing relative
+generalization gap as models are scaled up.
+"
+Bayesian Optimization of Catalysts With In-context Learning,Mayk Caldas Ramos,http://arxiv.org/pdf/2304.05341v1.pdf,2023-04-11,"['physics.chem-ph', 'cs.lg']",2304.05341v1.pdf,"  Large language models (LLMs) are able to do accurate classification with zero
+or only a few examples (in-context learning). We show a prompting system that
+enables regression with uncertainty for in-context learning with frozen LLM
+(GPT-3, GPT-3.5, and GPT-4) models, allowing predictions without features or
+architecture tuning. By incorporating uncertainty, our approach enables
+Bayesian optimization for catalyst or molecule optimization using natural
+language, eliminating the need for training or simulation. Here, we performed
+the optimization using the synthesis procedure of catalysts to predict
+properties. Working with natural language mitigates difficulty synthesizability
+since the literal synthesis procedure is the model's input. We showed that
+in-context learning could improve past a model context window (maximum number
+of tokens the model can process at once) as data is gathered via example
+selection, allowing the model to scale better. Although our method does not
+outperform all baselines, it requires zero training, feature selection, and
+minimal computing while maintaining satisfactory performance. We also find
+Gaussian Process Regression on text embeddings is strong at Bayesian
+optimization. The code is available in our GitHub repository:
+https://github.com/ur-whitelab/BO-LIFT
+"
+In-Context Learning Unlocked for Diffusion Models,Zhendong Wang,http://arxiv.org/pdf/2305.01115v2.pdf,2023-05-01,['cs.cv'],2305.01115v2.pdf,"  We present Prompt Diffusion, a framework for enabling in-context learning in
+diffusion-based generative models. Given a pair of task-specific example
+images, such as depth from/to image and scribble from/to image, and a text
+guidance, our model automatically understands the underlying task and performs
+the same task on a new query image following the text guidance. To achieve
+this, we propose a vision-language prompt that can model a wide range of
+vision-language tasks and a diffusion model that takes it as input. The
+diffusion model is trained jointly over six different tasks using these
+prompts. The resulting Prompt Diffusion model is the first diffusion-based
+vision-language foundation model capable of in-context learning. It
+demonstrates high-quality in-context generation on the trained tasks and
+generalizes effectively to new, unseen vision tasks with their respective
+prompts. Our model also shows compelling text-guided image editing results. Our
+framework aims to facilitate research into in-context learning for computer
+vision. We share our code and pre-trained models at
+https://github.com/Zhendong-Wang/Prompt-Diffusion.
+"
+Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and  Evaluation,Marius Mosbach,http://arxiv.org/pdf/2305.16938v2.pdf,2023-05-26,['cs.cl'],2305.16938v2.pdf,"  Few-shot fine-tuning and in-context learning are two alternative strategies
+for task adaptation of pre-trained language models. Recently, in-context
+learning has gained popularity over fine-tuning due to its simplicity and
+improved out-of-domain generalization, and because extensive evidence shows
+that fine-tuned models pick up on spurious correlations. Unfortunately,
+previous comparisons of the two approaches were done using models of different
+sizes. This raises the question of whether the observed weaker out-of-domain
+generalization of fine-tuned models is an inherent property of fine-tuning or a
+limitation of the experimental setup. In this paper, we compare the
+generalization of few-shot fine-tuning and in-context learning to challenge
+datasets, while controlling for the models used, the number of examples, and
+the number of parameters, ranging from 125M to 30B. Our results show that
+fine-tuned language models can in fact generalize well out-of-domain. We find
+that both approaches generalize similarly; they exhibit large variation and
+depend on properties such as model size and the number of examples,
+highlighting that robust task adaptation remains a challenge.
+"
+Large Language Models Can be Lazy Learners: Analyze Shortcuts in  In-Context Learning,Ruixiang Tang,http://arxiv.org/pdf/2305.17256v2.pdf,2023-05-26,"['cs.cl', 'cs.ai', 'cs.lg']",2305.17256v2.pdf,"  Large language models (LLMs) have recently shown great potential for
+in-context learning, where LLMs learn a new task simply by conditioning on a
+few input-label pairs (prompts). Despite their potential, our understanding of
+the factors influencing end-task performance and the robustness of in-context
+learning remains limited. This paper aims to bridge this knowledge gap by
+investigating the reliance of LLMs on shortcuts or spurious correlations within
+prompts. Through comprehensive experiments on classification and extraction
+tasks, we reveal that LLMs are ""lazy learners"" that tend to exploit shortcuts
+in prompts for downstream tasks. Additionally, we uncover a surprising finding
+that larger models are more likely to utilize shortcuts in prompts during
+inference. Our findings provide a new perspective on evaluating robustness in
+in-context learning and pose new challenges for detecting and mitigating the
+use of shortcuts in prompts.
+"
+Multi-Dimensional Evaluation of Text Summarization with In-Context  Learning,Sameer Jain,http://arxiv.org/pdf/2306.01200v1.pdf,2023-06-01,['cs.cl'],2306.01200v1.pdf,"  Evaluation of natural language generation (NLG) is complex and
+multi-dimensional. Generated text can be evaluated for fluency, coherence,
+factuality, or any other dimensions of interest. Most frameworks that perform
+such multi-dimensional evaluation require training on large manually or
+synthetically generated datasets. In this paper, we study the efficacy of large
+language models as multi-dimensional evaluators using in-context learning,
+obviating the need for large training datasets. Our experiments show that
+in-context learning-based evaluators are competitive with learned evaluation
+frameworks for the task of text summarization, establishing state-of-the-art on
+dimensions such as relevance and factual consistency. We then analyze the
+effects of factors such as the selection and number of in-context examples on
+performance. Finally, we study the efficacy of in-context learning based
+evaluators in evaluating zero-shot summaries written by large language models
+such as GPT-3.
+"
+Exploring the Integration of Large Language Models into Automatic Speech  Recognition Systems: An Empirical Study,Zeping Min,http://arxiv.org/pdf/2307.06530v1.pdf,2023-07-13,"['cs.cl', 'cs.sd', 'eess.as']",2307.06530v1.pdf,"  This paper explores the integration of Large Language Models (LLMs) into
+Automatic Speech Recognition (ASR) systems to improve transcription accuracy.
+The increasing sophistication of LLMs, with their in-context learning
+capabilities and instruction-following behavior, has drawn significant
+attention in the field of Natural Language Processing (NLP). Our primary focus
+is to investigate the potential of using an LLM's in-context learning
+capabilities to enhance the performance of ASR systems, which currently face
+challenges such as ambient noise, speaker accents, and complex linguistic
+contexts. We designed a study using the Aishell-1 and LibriSpeech datasets,
+with ChatGPT and GPT-4 serving as benchmarks for LLM capabilities.
+Unfortunately, our initial experiments did not yield promising results,
+indicating the complexity of leveraging LLM's in-context learning for ASR
+applications. Despite further exploration with varied settings and models, the
+corrected sentences from the LLMs frequently resulted in higher Word Error
+Rates (WER), demonstrating the limitations of LLMs in speech applications. This
+paper provides a detailed overview of these experiments, their results, and
+implications, establishing that using LLMs' in-context learning capabilities to
+correct potential errors in speech recognition transcriptions is still a
+challenging task at the current stage.
+"
+ACT-SQL: In-Context Learning for Text-to-SQL with  Automatically-Generated Chain-of-Thought,Hanchong Zhang,http://arxiv.org/pdf/2310.17342v1.pdf,2023-10-26,['cs.cl'],2310.17342v1.pdf,"  Recently Large Language Models (LLMs) have been proven to have strong
+abilities in various domains and tasks. We study the problem of prompt
+designing in the text-to-SQL task and attempt to improve the LLMs' reasoning
+ability when generating SQL queries. Besides the trivial few-shot in-context
+learning setting, we design our chain-of-thought (CoT) prompt with a similar
+method to schema linking. We provide a method named ACT-SQL to automatically
+generate auto-CoT exemplars and thus the whole process doesn't need manual
+labeling. Our approach is cost-saving since we only use the LLMs' API call once
+when generating one SQL query. Furthermore, we extend our in-context learning
+method to the multi-turn text-to-SQL task. The experiment results show that the
+LLMs' performance can benefit from our ACT-SQL approach. Our approach achieves
+SOTA performance on the Spider dev set among existing in-context learning
+approaches.
+"
+COSMIC: Data Efficient Instruction-tuning For Speech In-Context Learning,Jing Pan,http://arxiv.org/pdf/2311.02248v1.pdf,2023-11-03,"['cs.cl', 'cs.ai', 'eess.as']",2311.02248v1.pdf,"  We present a data and cost efficient way of incorporating the speech modality
+into a large language model (LLM). The resulting multi-modal LLM is a
+COntextual Speech Model with Instruction-following/in-context-learning
+Capabilities - COSMIC. Speech comprehension test question-answer (SQA) pairs
+are generated using GPT-3.5 based on the speech transcriptions as a part of the
+supervision for the instruction tuning. With fewer than 20M trainable
+parameters and as little as 450 hours of English speech data for SQA
+generation, COSMIC exhibits emergent instruction-following and in-context
+learning capabilities in speech-to-text tasks. The model is able to follow the
+given text instructions to generate text response even on the unseen EN$\to$X
+speech-to-text translation (S2TT) task with zero-shot setting. We evaluate the
+model's in-context learning via various tasks such as EN$\to$X S2TT and
+few-shot domain adaptation. And instruction-following capabilities are
+evaluated through a contextual biasing benchmark. Our results demonstrate the
+efficacy of the proposed low cost recipe for building a speech LLM and that
+with the new instruction-tuning data.
+"
+Thinking about GPT-3 In-Context Learning for Biomedical IE? Think Again,Bernal Jiménez Gutiérrez,http://arxiv.org/pdf/2203.08410v3.pdf,2022-03-16,"['cs.cl', 'cs.ir']",2203.08410v3.pdf,"  The strong few-shot in-context learning capability of large pre-trained
+language models (PLMs) such as GPT-3 is highly appealing for application
+domains such as biomedicine, which feature high and diverse demands of language
+technologies but also high data annotation costs. In this paper, we present the
+first systematic and comprehensive study to compare the few-shot performance of
+GPT-3 in-context learning with fine-tuning smaller (i.e., BERT-sized) PLMs on
+two highly representative biomedical information extraction tasks, named entity
+recognition and relation extraction. We follow the true few-shot setting to
+avoid overestimating models' few-shot performance by model selection over a
+large validation set. We also optimize GPT-3's performance with known
+techniques such as contextual calibration and dynamic in-context example
+retrieval. However, our results show that GPT-3 still significantly
+underperforms compared to simply fine-tuning a smaller PLM. In addition, GPT-3
+in-context learning also yields smaller gains in accuracy when more training
+data becomes available. Our in-depth analyses further reveal issues of the
+in-context learning setting that may be detrimental to information extraction
+tasks in general. Given the high cost of experimenting with GPT-3, we hope our
+study provides guidance for biomedical researchers and practitioners towards
+more promising directions such as fine-tuning small PLMs.
+"
+Exploring Effective Factors for Improving Visual In-Context Learning,Yanpeng Sun,http://arxiv.org/pdf/2304.04748v1.pdf,2023-04-10,['cs.cv'],2304.04748v1.pdf,"  The In-Context Learning (ICL) is to understand a new task via a few
+demonstrations (aka. prompt) and predict new inputs without tuning the models.
+While it has been widely studied in NLP, it is still a relatively new area of
+research in computer vision. To reveal the factors influencing the performance
+of visual in-context learning, this paper shows that prompt selection and
+prompt fusion are two major factors that have a direct impact on the inference
+performance of visual context learning. Prompt selection is the process of
+identifying the most appropriate prompt or example to help the model understand
+new tasks. This is important because providing the model with relevant prompts
+can help it learn more effectively and efficiently. Prompt fusion involves
+combining knowledge from different positions within the large-scale visual
+model. By doing this, the model can leverage the diverse knowledge stored in
+different parts of the model to improve its performance on new tasks. Based
+these findings, we propose a simple framework prompt-SelF for visual in-context
+learning. Specifically, we first use the pixel-level retrieval method to select
+a suitable prompt, and then use different prompt fusion methods to activate all
+the knowledge stored in the large-scale model, and finally ensemble the
+prediction results obtained from different prompt fusion methods to obtain the
+final prediction results. And we conduct extensive experiments on single-object
+segmentation and detection tasks to demonstrate the effectiveness of
+prompt-SelF. Remarkably, the prompt-SelF has outperformed OSLSM based
+meta-learning in 1-shot segmentation for the first time. This indicated the
+great potential of visual in-context learning. The source code and models will
+be available at \url{https://github.com/syp2ysy/prompt-SelF}.
+"
+Dissecting Chain-of-Thought: Compositionality through In-Context  Filtering and Learning,Yingcong Li,http://arxiv.org/pdf/2305.18869v2.pdf,2023-05-30,"['cs.lg', 'cs.ai', 'cs.cl']",2305.18869v2.pdf,"  Chain-of-thought (CoT) is a method that enables language models to handle
+complex reasoning tasks by decomposing them into simpler steps. Despite its
+success, the underlying mechanics of CoT are not yet fully understood. In an
+attempt to shed light on this, our study investigates the impact of CoT on the
+ability of transformers to in-context learn a simple to study, yet general
+family of compositional functions: multi-layer perceptrons (MLPs). In this
+setting, we find that the success of CoT can be attributed to breaking down
+in-context learning of a compositional function into two distinct phases:
+focusing on and filtering data related to each step of the composition and
+in-context learning the single-step composition function. Through both
+experimental and theoretical evidence, we demonstrate how CoT significantly
+reduces the sample complexity of in-context learning (ICL) and facilitates the
+learning of complex functions that non-CoT methods struggle with. Furthermore,
+we illustrate how transformers can transition from vanilla in-context learning
+to mastering a compositional function with CoT by simply incorporating
+additional layers that perform the necessary data-filtering for CoT via the
+attention mechanism. In addition to these test-time benefits, we show CoT helps
+accelerate pretraining by learning shortcuts to represent complex functions and
+filtering plays an important role in this process. These findings collectively
+provide insights into the mechanics of CoT, inviting further investigation of
+its role in complex reasoning tasks.
+"
+In-Context Learning through the Bayesian Prism,Kabir Ahuja,http://arxiv.org/pdf/2306.04891v1.pdf,2023-06-08,"['cs.lg', 'cs.cl']",2306.04891v1.pdf,"  In-context learning is one of the surprising and useful features of large
+language models. How it works is an active area of research. Recently, stylized
+meta-learning-like setups have been devised that train these models on a
+sequence of input-output pairs $(x, f(x))$ from a function class using the
+language modeling loss and observe generalization to unseen functions from the
+same class. One of the main discoveries in this line of research has been that
+for several problems such as linear regression, trained transformers learn
+algorithms for learning functions in context. However, the inductive biases of
+these models resulting in this behavior are not clearly understood. A model
+with unlimited training data and compute is a Bayesian predictor: it learns the
+pretraining distribution. It has been shown that high-capacity transformers
+mimic the Bayesian predictor for linear regression. In this paper, we show
+empirical evidence of transformers exhibiting the behavior of this ideal
+learner across different linear and non-linear function classes. We also extend
+the previous setups to work in the multitask setting and verify that
+transformers can do in-context learning in this setup as well and the Bayesian
+perspective sheds light on this setting also. Finally, via the example of
+learning Fourier series, we study the inductive bias for in-context learning.
+We find that in-context learning may or may not have simplicity bias depending
+on the pretraining data distribution.
+"
+Explore In-Context Learning for 3D Point Cloud Understanding,Zhongbin Fang,http://arxiv.org/pdf/2306.08659v1.pdf,2023-06-14,['cs.cv'],2306.08659v1.pdf,"  With the rise of large-scale models trained on broad data, in-context
+learning has become a new learning paradigm that has demonstrated significant
+potential in natural language processing and computer vision tasks. Meanwhile,
+in-context learning is still largely unexplored in the 3D point cloud domain.
+Although masked modeling has been successfully applied for in-context learning
+in 2D vision, directly extending it to 3D point clouds remains a formidable
+challenge. In the case of point clouds, the tokens themselves are the point
+cloud positions (coordinates) that are masked during inference. Moreover,
+position embedding in previous works may inadvertently introduce information
+leakage. To address these challenges, we introduce a novel framework, named
+Point-In-Context, designed especially for in-context learning in 3D point
+clouds, where both inputs and outputs are modeled as coordinates for each task.
+Additionally, we propose the Joint Sampling module, carefully designed to work
+in tandem with the general point sampling operator, effectively resolving the
+aforementioned technical issues. We conduct extensive experiments to validate
+the versatility and adaptability of our proposed methods in handling a wide
+range of tasks. Furthermore, with a more effective prompt selection strategy,
+our framework surpasses the results of individually trained models.
+"
+Scaling In-Context Demonstrations with Structured Attention,Tianle Cai,http://arxiv.org/pdf/2307.02690v1.pdf,2023-07-05,"['cs.cl', 'cs.ai', 'cs.lg']",2307.02690v1.pdf,"  The recent surge of large language models (LLMs) highlights their ability to
+perform in-context learning, i.e., ""learning"" to perform a task from a few
+demonstrations in the context without any parameter updates. However, their
+capabilities of in-context learning are limited by the model architecture: 1)
+the use of demonstrations is constrained by a maximum sentence length due to
+positional embeddings; 2) the quadratic complexity of attention hinders users
+from using more demonstrations efficiently; 3) LLMs are shown to be sensitive
+to the order of the demonstrations. In this work, we tackle these challenges by
+proposing a better architectural design for in-context learning. We propose
+SAICL (Structured Attention for In-Context Learning), which replaces the
+full-attention by a structured attention mechanism designed for in-context
+learning, and removes unnecessary dependencies between individual
+demonstrations, while making the model invariant to the permutation of
+demonstrations. We evaluate SAICL in a meta-training framework and show that
+SAICL achieves comparable or better performance than full attention while
+obtaining up to 3.4x inference speed-up. SAICL also consistently outperforms a
+strong Fusion-in-Decoder (FiD) baseline which processes each demonstration
+independently. Finally, thanks to its linear nature, we demonstrate that SAICL
+can easily scale to hundreds of demonstrations with continuous performance
+gains with scaling.
+"
+DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for  In-Context Learning,Jing Xiong,http://arxiv.org/pdf/2310.02954v4.pdf,2023-10-04,['cs.cl'],2310.02954v4.pdf,"  Recent advances in natural language processing, primarily propelled by Large
+Language Models (LLMs), have showcased their remarkable capabilities grounded
+in in-context learning. A promising avenue for guiding LLMs in intricate
+reasoning tasks involves the utilization of intermediate reasoning steps within
+the Chain-of-Thought (CoT) paradigm. Nevertheless, the central challenge lies
+in the effective selection of exemplars for facilitating in-context learning.
+In this study, we introduce a framework that leverages Dual Queries and
+Low-rank approximation Re-ranking (DQ-LoRe) to automatically select exemplars
+for in-context learning. Dual Queries first query LLM to obtain LLM-generated
+knowledge such as CoT, then query the retriever to obtain the final exemplars
+via both question and the knowledge. Moreover, for the second query, LoRe
+employs dimensionality reduction techniques to refine exemplar selection,
+ensuring close alignment with the input question's knowledge. Through extensive
+experiments, we demonstrate that DQ-LoRe significantly outperforms prior
+state-of-the-art methods in the automatic selection of exemplars for GPT-4,
+enhancing performance from 92.5% to 94.2%. Our comprehensive analysis further
+reveals that DQ-LoRe consistently outperforms retrieval-based approaches in
+terms of both performance and adaptability, especially in scenarios
+characterized by distribution shifts. DQ-LoRe pushes the boundaries of
+in-context learning and opens up new avenues for addressing complex reasoning
+challenges. We will release the code soon.
+"
+OverPrompt: Enhancing ChatGPT Capabilities through an Efficient  In-Context Learning Approach,Jiazheng Li,http://arxiv.org/pdf/2305.14973v1.pdf,2023-05-24,['cs.cl'],2305.14973v1.pdf,"  The exceptional performance of pre-trained large language models has
+revolutionised various applications, but their adoption in production
+environments is hindered by prohibitive costs and inefficiencies, particularly
+when utilising long prompts. This paper proposes OverPrompt, an in-context
+learning method aimed at improving LLM efficiency and performance by processing
+multiple inputs in parallel. Evaluated across diverse datasets, OverPrompt
+enhances task efficiency and integrates a diverse range of examples for
+improved performance. Particularly, it amplifies fact-checking and sentiment
+analysis tasks when supplemented with contextual information. Synthetic data
+grouping further enhances performance, suggesting a viable approach for data
+augmentation.
+"
+Crosslingual Retrieval Augmented In-context Learning for Bangla,Xiaoqian Li,http://arxiv.org/pdf/2311.00587v1.pdf,2023-11-01,['cs.cl'],2311.00587v1.pdf,"  The promise of Large Language Models (LLMs) in Natural Language Processing
+has often been overshadowed by their limited performance in low-resource
+languages such as Bangla. To address this, our paper presents a pioneering
+approach that utilizes cross-lingual retrieval augmented in-context learning.
+By strategically sourcing semantically similar prompts from high-resource
+language, we enable multilingual pretrained language models (MPLMs), especially
+the generative model BLOOMZ, to successfully boost performance on Bangla tasks.
+Our extensive evaluation highlights that the cross-lingual retrieval augmented
+prompts bring steady improvements to MPLMs over the zero-shot performance.
+"
+Ground-Truth Labels Matter: A Deeper Look into Input-Label  Demonstrations,Kang Min Yoo,http://arxiv.org/pdf/2205.12685v2.pdf,2022-05-25,"['cs.cl', 'cs.ai', 'cs.lg']",2205.12685v2.pdf,"  Despite recent explosion of interests in in-context learning, the underlying
+mechanism and the precise impact of the quality of demonstrations remain
+elusive. Intuitively, ground-truth labels should have as much impact in
+in-context learning (ICL) as supervised learning, but recent work reported that
+the input-label correspondence is significantly less important than previously
+thought. Intrigued by this counter-intuitive observation, we re-examine the
+importance of ground-truth labels in in-context learning. With the introduction
+of two novel metrics, namely Label-Correctness Sensitivity and Ground-truth
+Label Effect Ratio (GLER), we were able to conduct quantifiable analysis on the
+impact of ground-truth label demonstrations. Through extensive analyses, we
+find that the correct input-label mappings can have varying impacts on the
+downstream in-context learning performances, depending on the experimental
+configuration. Through additional studies, we identify key components, such as
+the verbosity of prompt templates and the language model size, as the
+controlling factor to achieve more noise-resilient ICL.
+"
+In-context Learning and Induction Heads,Catherine Olsson,http://arxiv.org/pdf/2209.11895v1.pdf,2022-09-24,['cs.lg'],2209.11895v1.pdf,"  ""Induction heads"" are attention heads that implement a simple algorithm to
+complete token sequences like [A][B] ... [A] -> [B]. In this work, we present
+preliminary and indirect evidence for a hypothesis that induction heads might
+constitute the mechanism for the majority of all ""in-context learning"" in large
+transformer models (i.e. decreasing loss at increasing token indices). We find
+that induction heads develop at precisely the same point as a sudden sharp
+increase in in-context learning ability, visible as a bump in the training
+loss. We present six complementary lines of evidence, arguing that induction
+heads may be the mechanistic source of general in-context learning in
+transformer models of any size. For small attention-only models, we present
+strong, causal evidence; for larger models with MLPs, we present correlational
+evidence.
+"
+Transformers learn in-context by gradient descent,Johannes von Oswald,http://arxiv.org/pdf/2212.07677v2.pdf,2022-12-15,"['cs.lg', 'cs.ai', 'cs.cl']",2212.07677v2.pdf,"  At present, the mechanisms of in-context learning in Transformers are not
+well understood and remain mostly an intuition. In this paper, we suggest that
+training Transformers on auto-regressive objectives is closely related to
+gradient-based meta-learning formulations. We start by providing a simple
+weight construction that shows the equivalence of data transformations induced
+by 1) a single linear self-attention layer and by 2) gradient-descent (GD) on a
+regression loss. Motivated by that construction, we show empirically that when
+training self-attention-only Transformers on simple regression tasks either the
+models learned by GD and Transformers show great similarity or, remarkably, the
+weights found by optimization match the construction. Thus we show how trained
+Transformers become mesa-optimizers i.e. learn models by gradient descent in
+their forward pass. This allows us, at least in the domain of regression
+problems, to mechanistically understand the inner workings of in-context
+learning in optimized Transformers. Building on this insight, we furthermore
+identify how Transformers surpass the performance of plain gradient descent by
+learning an iterative curvature correction and learn linear models on deep data
+representations to solve non-linear regression tasks. Finally, we discuss
+intriguing parallels to a mechanism identified to be crucial for in-context
+learning termed induction-head (Olsson et al., 2022) and show how it could be
+understood as a specific case of in-context learning by gradient descent
+learning within Transformers. Code to reproduce the experiments can be found at
+https://github.com/google-research/self-organising-systems/tree/master/transformers_learn_icl_by_gd .
+"
+What Makes Good Examples for Visual In-Context Learning?,Yuanhan Zhang,http://arxiv.org/pdf/2301.13670v2.pdf,2023-01-31,['cs.cv'],2301.13670v2.pdf,"  Large-scale models trained on broad data have recently become the mainstream
+architecture in computer vision due to their strong generalization performance.
+In this paper, the main focus is on an emergent ability in large vision models,
+known as in-context learning, which allows inference on unseen tasks by
+conditioning on in-context examples (a.k.a.~prompt) without updating the model
+parameters. This concept has been well-known in natural language processing but
+has only been studied very recently for large vision models. We for the first
+time provide a comprehensive investigation on the impact of in-context examples
+in computer vision, and find that the performance is highly sensitive to the
+choice of in-context examples. To overcome the problem, we propose a prompt
+retrieval framework to automate the selection of in-context examples.
+Specifically, we present (1) an unsupervised prompt retrieval method based on
+nearest example search using an off-the-shelf model, and (2) a supervised
+prompt retrieval method, which trains a neural network to choose examples that
+directly maximize in-context learning performance. The results demonstrate that
+our methods can bring non-trivial improvements to visual in-context learning in
+comparison to the commonly-used random selection.
+"
+Compositional Exemplars for In-context Learning,Jiacheng Ye,http://arxiv.org/pdf/2302.05698v3.pdf,2023-02-11,"['cs.cl', 'cs.ai', 'cs.lg']",2302.05698v3.pdf,"  Large pretrained language models (LMs) have shown impressive In-Context
+Learning (ICL) ability, where the model learns to do an unseen task via a
+prompt consisting of input-output examples as the demonstration, without any
+parameter updates. The performance of ICL is highly dominated by the quality of
+the selected in-context examples. However, previous selection methods are
+mostly based on simple heuristics, leading to sub-optimal performance. In this
+work, we formulate in-context example selection as a subset selection problem.
+We propose CEIL (Compositional Exemplars for In-context Learning), which is
+instantiated by Determinantal Point Processes (DPPs) to model the interaction
+between the given input and in-context examples, and optimized through a
+carefully-designed contrastive learning objective to obtain preference from
+LMs. We validate CEIL on 12 classification and generation datasets from 7
+distinct NLP tasks, including sentiment analysis, paraphrase detection, natural
+language inference, commonsense reasoning, open-domain question answering, code
+generation, and semantic parsing. Extensive experiments demonstrate not only
+the state-of-the-art performance but also the transferability and
+compositionality of CEIL, shedding new light on effective and efficient
+in-context learning. Our code is released at
+https://github.com/HKUNLP/icl-ceil.
+"
+ICL-D3IE: In-Context Learning with Diverse Demonstrations Updating for  Document Information Extraction,Jiabang He,http://arxiv.org/pdf/2303.05063v4.pdf,2023-03-09,['cs.cl'],2303.05063v4.pdf,"  Large language models (LLMs), such as GPT-3 and ChatGPT, have demonstrated
+remarkable results in various natural language processing (NLP) tasks with
+in-context learning, which involves inference based on a few demonstration
+examples. Despite their successes in NLP tasks, no investigation has been
+conducted to assess the ability of LLMs to perform document information
+extraction (DIE) using in-context learning. Applying LLMs to DIE poses two
+challenges: the modality and task gap. To this end, we propose a simple but
+effective in-context learning framework called ICL-D3IE, which enables LLMs to
+perform DIE with different types of demonstration examples. Specifically, we
+extract the most difficult and distinct segments from hard training documents
+as hard demonstrations for benefiting all test instances. We design
+demonstrations describing relationships that enable LLMs to understand
+positional relationships. We introduce formatting demonstrations for easy
+answer extraction. Additionally, the framework improves diverse demonstrations
+by updating them iteratively. Our experiments on three widely used benchmark
+datasets demonstrate that the ICL-D3IE framework enables Davinci-003/ChatGPT to
+achieve superior performance when compared to previous pre-trained methods
+fine-tuned with full training in both the in-distribution (ID) setting and in
+the out-of-distribution (OOD) setting. Code is available at
+https://github.com/MAEHCM/ICL-D3IE.
+"
+The Closeness of In-Context Learning and Weight Shifting for Softmax  Regression,Shuai Li,http://arxiv.org/pdf/2304.13276v1.pdf,2023-04-26,"['cs.cl', 'cs.lg']",2304.13276v1.pdf,"  Large language models (LLMs) are known for their exceptional performance in
+natural language processing, making them highly effective in many human
+life-related or even job-related tasks. The attention mechanism in the
+Transformer architecture is a critical component of LLMs, as it allows the
+model to selectively focus on specific input parts. The softmax unit, which is
+a key part of the attention mechanism, normalizes the attention scores. Hence,
+the performance of LLMs in various NLP tasks depends significantly on the
+crucial role played by the attention mechanism with the softmax unit.
+  In-context learning, as one of the celebrated abilities of recent LLMs, is an
+important concept in querying LLMs such as ChatGPT. Without further parameter
+updates, Transformers can learn to predict based on few in-context examples.
+However, the reason why Transformers becomes in-context learners is not well
+understood. Recently, several works [ASA+22,GTLV22,ONR+22] have studied the
+in-context learning from a mathematical perspective based on a linear
+regression formulation $\min_x\| Ax - b \|_2$, which show Transformers'
+capability of learning linear functions in context.
+  In this work, we study the in-context learning based on a softmax regression
+formulation $\min_{x} \| \langle \exp(Ax), {\bf 1}_n \rangle^{-1} \exp(Ax) - b
+\|_2$ of Transformer's attention mechanism. We show the upper bounds of the
+data transformations induced by a single self-attention layer and by
+gradient-descent on a $\ell_2$ regression loss for softmax prediction function,
+which imply that when training self-attention-only Transformers for fundamental
+regression tasks, the models learned by gradient-descent and Transformers show
+great similarity.
+"
+MMICL: Empowering Vision-language Model with Multi-Modal In-Context  Learning,Haozhe Zhao,http://arxiv.org/pdf/2309.07915v2.pdf,2023-09-14,"['cs.cl', 'cs.ai', 'cs.cv']",2309.07915v2.pdf,"  Since the resurgence of deep learning, vision-language models (VLMs) enhanced
+by large language models (LLMs) have grown exponentially in popularity.
+However, while LLMs can utilize extensive background knowledge and task
+information with in-context learning, most VLMs still struggle with
+understanding complex multi-modal prompts with multiple images, making VLMs
+less effective in downstream vision-language tasks. In this paper, we address
+the limitation above by 1) introducing MMICL, a new approach to allow the VLM
+to deal with multi-modal inputs efficiently; 2) proposing a novel context
+scheme to augment the in-context learning ability of the VLM; 3) constructing
+the Multi-modal In-Context Learning (MIC) dataset, designed to enhance the
+VLM's ability to understand complex multi-modal prompts. Our experiments
+confirm that MMICL achieves new state-of-the-art zero-shot performance on a
+wide range of general vision-language tasks, especially for complex benchmarks,
+including MME and MMBench. Our analysis demonstrates that MMICL effectively
+tackles the challenge of complex multi-modal prompt understanding and emerges
+the impressive ICL ability. Furthermore, we observe that MMICL successfully
+alleviates language bias in VLMs, a common issue for VLMs that often leads to
+hallucination when faced with extensive textual context.
+"
+Visual In-Context Learning for Few-Shot Eczema Segmentation,Neelesh Kumar,http://arxiv.org/pdf/2309.16656v1.pdf,2023-09-28,"['cs.cv', 'cs.lg']",2309.16656v1.pdf,"  Automated diagnosis of eczema from digital camera images is crucial for
+developing applications that allow patients to self-monitor their recovery. An
+important component of this is the segmentation of eczema region from such
+images. Current methods for eczema segmentation rely on deep neural networks
+such as convolutional (CNN)-based U-Net or transformer-based Swin U-Net. While
+effective, these methods require high volume of annotated data, which can be
+difficult to obtain. Here, we investigate the capabilities of visual in-context
+learning that can perform few-shot eczema segmentation with just a handful of
+examples and without any need for retraining models. Specifically, we propose a
+strategy for applying in-context learning for eczema segmentation with a
+generalist vision model called SegGPT. When benchmarked on a dataset of
+annotated eczema images, we show that SegGPT with just 2 representative example
+images from the training dataset performs better (mIoU: 36.69) than a CNN U-Net
+trained on 428 images (mIoU: 32.60). We also discover that using more number of
+examples for SegGPT may in fact be harmful to its performance. Our result
+highlights the importance of visual in-context learning in developing faster
+and better solutions to skin imaging tasks. Our result also paves the way for
+developing inclusive solutions that can cater to minorities in the demographics
+who are typically heavily under-represented in the training data.
+"
+Learning To Retrieve Prompts for In-Context Learning,Ohad Rubin,http://arxiv.org/pdf/2112.08633v2.pdf,2021-12-16,"['cs.cl', 'cs.lg']",2112.08633v2.pdf,"  In-context learning is a recent paradigm in natural language understanding,
+where a large pre-trained language model (LM) observes a test instance and a
+few training examples as its input, and directly decodes the output without any
+update to its parameters. However, performance has been shown to strongly
+depend on the selected training examples (termed prompt). In this work, we
+propose an efficient method for retrieving prompts for in-context learning
+using annotated data and a LM. Given an input-output pair, we estimate the
+probability of the output given the input and a candidate training example as
+the prompt, and label training examples as positive or negative based on this
+probability. We then train an efficient dense retriever from this data, which
+is used to retrieve training examples as prompts at test time. We evaluate our
+approach on three sequence-to-sequence tasks where language utterances are
+mapped to meaning representations, and find that it substantially outperforms
+prior work and multiple baselines across the board.
+"
+Semantic-Oriented Unlabeled Priming for Large-Scale Language Models,Yanchen Liu,http://arxiv.org/pdf/2202.06133v1.pdf,2022-02-12,['cs.cl'],2202.06133v1.pdf,"  Due to the high costs associated with finetuning large language models,
+various recent works propose to adapt them to specific tasks without any
+parameter updates through in-context learning. Unfortunately, for in-context
+learning there is currently no way to leverage unlabeled data, which is often
+much easier to obtain in large quantities than labeled examples. In this work,
+we therefore investigate ways to make use of unlabeled examples to improve the
+zero-shot performance of pretrained language models without any finetuning: We
+introduce Semantic-Oriented Unlabeled Priming (SOUP), a method that classifies
+examples by retrieving semantically similar unlabeled examples, assigning
+labels to them in a zero-shot fashion, and then using them for in-context
+learning. We also propose bag-of-contexts priming, a new priming strategy that
+is more suitable for our setting and enables the usage of more examples than
+fit into the context window.
+"
+Complementary Explanations for Effective In-Context Learning,Xi Ye,http://arxiv.org/pdf/2211.13892v2.pdf,2022-11-25,['cs.cl'],2211.13892v2.pdf,"  Large language models (LLMs) have exhibited remarkable capabilities in
+learning from explanations in prompts, but there has been limited understanding
+of exactly how these explanations function or why they are effective. This work
+aims to better understand the mechanisms by which explanations are used for
+in-context learning. We first study the impact of two different factors on the
+performance of prompts with explanations: the computation trace (the way the
+solution is decomposed) and the natural language used to express the prompt. By
+perturbing explanations on three controlled tasks, we show that both factors
+contribute to the effectiveness of explanations. We further study how to form
+maximally effective sets of explanations for solving a given test query. We
+find that LLMs can benefit from the complementarity of the explanation set:
+diverse reasoning skills shown by different exemplars can lead to better
+performance. Therefore, we propose a maximal marginal relevance-based exemplar
+selection approach for constructing exemplar sets that are both relevant as
+well as complementary, which successfully improves the in-context learning
+performance across three real-world tasks on multiple LLMs.
+"
+Diverse Demonstrations Improve In-context Compositional Generalization,Itay Levy,http://arxiv.org/pdf/2212.06800v3.pdf,2022-12-13,['cs.cl'],2212.06800v3.pdf,"  In-context learning has shown great success in i.i.d semantic parsing splits,
+where the training and test sets are drawn from the same distribution. In this
+setup, models are typically prompted with demonstrations that are similar to
+the input utterance. However, in the setup of compositional generalization,
+where models are tested on outputs with structures that are absent from the
+training set, selecting similar demonstrations is insufficient, as often no
+example will be similar enough to the input. In this work, we propose a method
+to select diverse demonstrations that aims to collectively cover all of the
+structures required in the output program, in order to encourage the model to
+generalize to new structures from these demonstrations. We empirically show
+that combining diverse demonstrations with in-context learning substantially
+improves performance across three compositional generalization semantic parsing
+datasets in the pure in-context learning setup and when combined with
+finetuning.
+"
+The Impact of Symbolic Representations on In-context Learning for  Few-shot Reasoning,Hanlin Zhang,http://arxiv.org/pdf/2212.08686v1.pdf,2022-12-16,['cs.cl'],2212.08686v1.pdf,"  Pre-trained language models (LMs) have shown remarkable reasoning performance
+using explanations (or ``chain-of-thought'' (CoT)) for in-context learning. On
+the other hand, these reasoning tasks are usually presumed to be more
+approachable for symbolic programming. To make progress towards understanding
+in-context learning, we curate synthetic datasets containing equivalent
+(natural, symbolic) data pairs, where symbolic examples contain first-order
+logic rules and predicates from knowledge bases (KBs). Then we revisit
+neuro-symbolic approaches and use Language Models as Logic Programmer (LMLP)
+that learns from demonstrations containing logic rules and corresponding
+examples to iteratively reason over KBs, recovering Prolog's backward chaining
+algorithm. Comprehensive experiments are included to systematically compare
+LMLP with CoT in deductive reasoning settings, showing that LMLP enjoys more
+than 25% higher accuracy than CoT on length generalization benchmarks even with
+fewer parameters.
+"
+Self-Adaptive In-Context Learning: An Information Compression  Perspective for In-Context Example Selection and Ordering,Zhiyong Wu,http://arxiv.org/pdf/2212.10375v2.pdf,2022-12-20,"['cs.cl', 'cs.ai']",2212.10375v2.pdf,"  Despite the surprising few-shot performance of in-context learning (ICL), it
+is still a common practice to randomly sample examples to serve as context.
+This paper advocates a new principle for ICL: self-adaptive in-context
+learning. The self-adaption mechanism is introduced to help each sample find an
+in-context example permutation (i.e., selection and ordering) that can derive
+the correct prediction, thus maximizing performance. To validate the
+effectiveness of self-adaptive ICL, we propose a general select-then-rank
+framework and instantiate it with new selection and ranking algorithms. Upon
+extensive evaluation on eight different NLP datasets, our self-adaptive ICL
+method achieves a 40% relative improvement over the common practice setting.
+Further analysis reveals the enormous potential of self-adaptive ICL that it
+might be able to close the gap between ICL and finetuning given more advanced
+algorithms. Our code is released to facilitate future research in this area:
+https://github.com/Shark-NLP/self-adaptive-ICL
+"
+Privacy-Preserving In-Context Learning for Large Language Models,Tong Wu,http://arxiv.org/pdf/2305.01639v2.pdf,2023-05-02,"['cs.lg', 'cs.ai', 'cs.cr']",2305.01639v2.pdf,"  In-context learning (ICL) is an important capability of Large Language Models
+(LLMs), enabling these models to dynamically adapt based on specific,
+in-context exemplars, thereby improving accuracy and relevance. However, LLM's
+responses may leak the sensitive private information contained in in-context
+exemplars. To address this challenge, we propose Differentially Private
+In-context Learning (DP-ICL), a general paradigm for privatizing ICL tasks. The
+key idea for DP-ICL paradigm is generating differentially private responses
+through a noisy consensus among an ensemble of LLM's responses based on
+disjoint exemplar sets. Based on the general paradigm of DP-ICL, we instantiate
+several techniques showing how to privatize ICL for text classification and
+language generation. We evaluate DP-ICL on four text classification benchmarks
+and two language generation tasks, and our empirical results show that DP-ICL
+achieves a strong utility-privacy tradeoff.
+"
+In-context Learning as Maintaining Coherency: A Study of On-the-fly  Machine Translation Using Large Language Models,Suzanna Sia,http://arxiv.org/pdf/2305.03573v1.pdf,2023-05-05,"['cs.cl', 'cs.ai']",2305.03573v1.pdf,"  The phenomena of in-context learning has typically been thought of as
+""learning from examples"". In this work which focuses on Machine Translation, we
+present a perspective of in-context learning as the desired generation task
+maintaining coherency with its context, i.e., the prompt examples. We first
+investigate randomly sampled prompts across 4 domains, and find that
+translation performance improves when shown in-domain prompts. Next, we
+investigate coherency for the in-domain setting, which uses prompt examples
+from a moving window. We study this with respect to other factors that have
+previously been identified in the literature such as length, surface similarity
+and sentence embedding similarity. Our results across 3 models (GPTNeo2.7B,
+Bloom3B, XGLM2.9B), and three translation directions
+(\texttt{en}$\rightarrow$\{\texttt{pt, de, fr}\}) suggest that the long-term
+coherency of the prompts and the test sentence is a good indicator of
+downstream translation performance. In doing so, we demonstrate the efficacy of
+In-context Machine Translation for on-the-fly adaptation.
+"
+Small Models are Valuable Plug-ins for Large Language Models,Canwen Xu,http://arxiv.org/pdf/2305.08848v1.pdf,2023-05-15,"['cs.cl', 'cs.ai', 'cs.lg']",2305.08848v1.pdf,"  Large language models (LLMs) such as GPT-3 and GPT-4 are powerful but their
+weights are often publicly unavailable and their immense sizes make the models
+difficult to be tuned with common hardware. As a result, effectively tuning
+these models with large-scale supervised data can be challenging. As an
+alternative, In-Context Learning (ICL) can only use a small number of
+supervised examples due to context length limits. In this paper, we propose
+Super In-Context Learning (SuperICL) which allows black-box LLMs to work with
+locally fine-tuned smaller models, resulting in superior performance on
+supervised tasks. Our experiments demonstrate that SuperICL can improve
+performance beyond state-of-the-art fine-tuned models while addressing the
+instability problem of in-context learning. Furthermore, SuperICL can enhance
+the capabilities of smaller models, such as multilinguality and
+interpretability.
+"
+ScoNe: Benchmarking Negation Reasoning in Language Models With  Fine-Tuning and In-Context Learning,Jingyuan Selena She,http://arxiv.org/pdf/2305.19426v1.pdf,2023-05-30,"['cs.cl', 'cs.lg']",2305.19426v1.pdf,"  A number of recent benchmarks seek to assess how well models handle natural
+language negation. However, these benchmarks lack the controlled example
+paradigms that would allow us to infer whether a model had learned how negation
+morphemes semantically scope. To fill these analytical gaps, we present the
+Scoped Negation NLI (ScoNe-NLI) benchmark, which contains contrast sets of six
+examples with up to two negations where either zero, one, or both negative
+morphemes affect the NLI label. We use ScoNe-NLI to assess fine-tuning and
+in-context learning strategies. We find that RoBERTa and DeBERTa models solve
+ScoNe-NLI after many shot fine-tuning. For in-context learning, we test
+InstructGPT models and find that most prompt strategies are not successful,
+including those using step-by-step reasoning. To better understand this result,
+we extend ScoNe with ScoNe-NLG, a sentence completion test set that embeds
+negation reasoning in short narratives. Here, InstructGPT is successful, which
+reveals the model can correctly reason about negation, but struggles to do so
+on prompt-adapted NLI examples outside of its core pretraining regime.
+"
+GPT-FinRE: In-context Learning for Financial Relation Extraction using  Large Language Models,Pawan Kumar Rajpoot,http://arxiv.org/pdf/2306.17519v2.pdf,2023-06-30,['cs.cl'],2306.17519v2.pdf,"  Relation extraction (RE) is a crucial task in natural language processing
+(NLP) that aims to identify and classify relationships between entities
+mentioned in text. In the financial domain, relation extraction plays a vital
+role in extracting valuable information from financial documents, such as news
+articles, earnings reports, and company filings. This paper describes our
+solution to relation extraction on one such dataset REFinD. The dataset was
+released along with shared task as a part of the Fourth Workshop on Knowledge
+Discovery from Unstructured Data in Financial Services, co-located with SIGIR
+2023. In this paper, we employed OpenAI models under the framework of
+in-context learning (ICL). We utilized two retrieval strategies to find top K
+relevant in-context learning demonstrations / examples from training data for a
+given test example. The first retrieval mechanism, we employed, is a
+learning-free dense retriever and the other system is a learning-based
+retriever. We were able to achieve 3rd rank overall. Our best F1-score is
+0.718.
+"
+Code-Style In-Context Learning for Knowledge-Based Question Answering,Zhijie Nie,http://arxiv.org/pdf/2309.04695v1.pdf,2023-09-09,"['cs.cl', 'cs.ai']",2309.04695v1.pdf,"  Current methods for Knowledge-Based Question Answering (KBQA) usually rely on
+complex training techniques and model frameworks, leading to many limitations
+in practical applications. Recently, the emergence of In-Context Learning (ICL)
+capabilities in Large Language Models (LLMs) provides a simple and
+training-free semantic parsing paradigm for KBQA: Given a small number of
+questions and their labeled logical forms as demo examples, LLMs can understand
+the task intent and generate the logic form for a new question. However,
+current powerful LLMs have little exposure to logic forms during pre-training,
+resulting in a high format error rate. To solve this problem, we propose a
+code-style in-context learning method for KBQA, which converts the generation
+process of unfamiliar logical form into the more familiar code generation
+process for LLMs. Experimental results on three mainstream datasets show that
+our method dramatically mitigated the formatting error problem in generating
+logic forms while realizing a new SOTA on WebQSP, GrailQA, and GraphQ under the
+few-shot setting.
+"
+Can Whisper perform speech-based in-context learning,Siyin Wang,http://arxiv.org/pdf/2309.07081v1.pdf,2023-09-13,"['eess.as', 'cs.cl', 'cs.sd']",2309.07081v1.pdf,"  This paper investigates the in-context learning abilities of the Whisper
+automatic speech recognition (ASR) models released by OpenAI. A novel
+speech-based in-context learning (SICL) approach is proposed for test-time
+adaptation, which can reduce the word error rates (WERs) with only a small
+number of labelled speech samples without gradient descent. Language-level
+adaptation experiments using Chinese dialects showed that when applying SICL to
+isolated word ASR, consistent and considerable relative WER reductions can be
+achieved using Whisper models of any size on two dialects, which is on average
+32.3%. A k-nearest-neighbours-based in-context example selection technique can
+be applied to further improve the efficiency of SICL, which can increase the
+average relative WER reduction to 36.4%. The findings are verified using
+speaker adaptation or continuous speech recognition tasks, and both achieved
+considerable relative WER reductions. Detailed quantitative analyses are also
+provided to shed light on SICL's adaptability to phonological variances and
+dialect-specific lexical nuances.
+"
+ICLEF: In-Context Learning with Expert Feedback for Explainable Style  Transfer,Arkadiy Saakyan,http://arxiv.org/pdf/2309.08583v1.pdf,2023-09-15,['cs.cl'],2309.08583v1.pdf,"  While state-of-the-art language models excel at the style transfer task,
+current work does not address explainability of style transfer systems.
+Explanations could be generated using large language models such as GPT-3.5 and
+GPT-4, but the use of such complex systems is inefficient when smaller, widely
+distributed, and transparent alternatives are available. We propose a framework
+to augment and improve a formality style transfer dataset with explanations via
+model distillation from ChatGPT. To further refine the generated explanations,
+we propose a novel way to incorporate scarce expert human feedback using
+in-context learning (ICLEF: In-Context Learning from Expert Feedback) by
+prompting ChatGPT to act as a critic to its own outputs. We use the resulting
+dataset of 9,960 explainable formality style transfer instances (e-GYAFC) to
+show that current openly distributed instruction-tuned models (and, in some
+settings, ChatGPT) perform poorly on the task, and that fine-tuning on our
+high-quality dataset leads to significant improvements as shown by automatic
+evaluation. In human evaluation, we show that models much smaller than ChatGPT
+fine-tuned on our data align better with expert preferences. Finally, we
+discuss two potential applications of models fine-tuned on the explainable
+style transfer task: interpretable authorship verification and interpretable
+adversarial attacks on AI-generated text detectors.
+"
+SALM: Speech-augmented Language Model with In-context Learning for  Speech Recognition and Translation,Zhehuai Chen,http://arxiv.org/pdf/2310.09424v1.pdf,2023-10-13,"['cs.cl', 'cs.hc', 'cs.sd', 'eess.as', '68t10', 'i.2.7']",2310.09424v1.pdf,"  We present a novel Speech Augmented Language Model (SALM) with {\em
+multitask} and {\em in-context} learning capabilities. SALM comprises a frozen
+text LLM, a audio encoder, a modality adapter module, and LoRA layers to
+accommodate speech input and associated task instructions. The unified SALM not
+only achieves performance on par with task-specific Conformer baselines for
+Automatic Speech Recognition (ASR) and Speech Translation (AST), but also
+exhibits zero-shot in-context learning capabilities, demonstrated through
+keyword-boosting task for ASR and AST. Moreover, {\em speech supervised
+in-context training} is proposed to bridge the gap between LLM training and
+downstream speech tasks, which further boosts the in-context learning ability
+of speech-to-text models. Proposed model is open-sourced via NeMo toolkit.
+"
+Utilising a Large Language Model to Annotate Subject Metadata: A Case  Study in an Australian National Research Data Catalogue,Shiwei Zhang,http://arxiv.org/pdf/2310.11318v1.pdf,2023-10-17,"['cs.cl', 'cs.ai']",2310.11318v1.pdf,"  In support of open and reproducible research, there has been a rapidly
+increasing number of datasets made available for research. As the availability
+of datasets increases, it becomes more important to have quality metadata for
+discovering and reusing them. Yet, it is a common issue that datasets often
+lack quality metadata due to limited resources for data curation. Meanwhile,
+technologies such as artificial intelligence and large language models (LLMs)
+are progressing rapidly. Recently, systems based on these technologies, such as
+ChatGPT, have demonstrated promising capabilities for certain data curation
+tasks. This paper proposes to leverage LLMs for cost-effective annotation of
+subject metadata through the LLM-based in-context learning. Our method employs
+GPT-3.5 with prompts designed for annotating subject metadata, demonstrating
+promising performance in automatic metadata annotation. However, models based
+on in-context learning cannot acquire discipline-specific rules, resulting in
+lower performance in several categories. This limitation arises from the
+limited contextual information available for subject inference. To the best of
+our knowledge, we are introducing, for the first time, an in-context learning
+method that harnesses large language models for automated subject metadata
+annotation.
+"
+Hint-enhanced In-Context Learning wakes Large Language Models up for  knowledge-intensive tasks,Yifan Wang,http://arxiv.org/pdf/2311.01949v1.pdf,2023-11-03,['cs.cl'],2311.01949v1.pdf,"  In-context learning (ICL) ability has emerged with the increasing scale of
+large language models (LLMs), enabling them to learn input-label mappings from
+demonstrations and perform well on downstream tasks. However, under the
+standard ICL setting, LLMs may sometimes neglect query-related information in
+demonstrations, leading to incorrect predictions. To address this limitation,
+we propose a new paradigm called Hint-enhanced In-Context Learning (HICL) to
+explore the power of ICL in open-domain question answering, an important form
+in knowledge-intensive tasks. HICL leverages LLMs' reasoning ability to extract
+query-related knowledge from demonstrations, then concatenates the knowledge to
+prompt LLMs in a more explicit way. Furthermore, we track the source of this
+knowledge to identify specific examples, and introduce a Hint-related Example
+Retriever (HER) to select informative examples for enhanced demonstrations. We
+evaluate HICL with HER on 3 open-domain QA benchmarks, and observe average
+performance gains of 2.89 EM score and 2.52 F1 score on gpt-3.5-turbo, 7.62 EM
+score and 7.27 F1 score on LLaMA-2-Chat-7B compared with standard setting.
+"
+Rethinking the Role of Demonstrations: What Makes In-Context Learning  Work?,Sewon Min,http://arxiv.org/pdf/2202.12837v2.pdf,2022-02-25,"['cs.cl', 'cs.ai']",2202.12837v2.pdf,"  Large language models (LMs) are able to in-context learn -- perform a new
+task via inference alone by conditioning on a few input-label pairs
+(demonstrations) and making predictions for new inputs. However, there has been
+little understanding of how the model learns and which aspects of the
+demonstrations contribute to end task performance. In this paper, we show that
+ground truth demonstrations are in fact not required -- randomly replacing
+labels in the demonstrations barely hurts performance on a range of
+classification and multi-choce tasks, consistently over 12 different models
+including GPT-3. Instead, we find that other aspects of the demonstrations are
+the key drivers of end task performance, including the fact that they provide a
+few examples of (1) the label space, (2) the distribution of the input text,
+and (3) the overall format of the sequence. Together, our analysis provides a
+new way of understanding how and why in-context learning works, while opening
+up new questions about how much can be learned from large language models
+through inference alone.
+"
+Can Foundation Models Help Us Achieve Perfect Secrecy?,Simran Arora,http://arxiv.org/pdf/2205.13722v2.pdf,2022-05-27,"['cs.lg', 'cs.cl']",2205.13722v2.pdf,"  A key promise of machine learning is the ability to assist users with
+personal tasks. Because the personal context required to make accurate
+predictions is often sensitive, we require systems that protect privacy. A gold
+standard privacy-preserving system will satisfy perfect secrecy, meaning that
+interactions with the system provably reveal no private information. However,
+privacy and quality appear to be in tension in existing systems for personal
+tasks. Neural models typically require copious amounts of training to perform
+well, while individual users typically hold a limited scale of data, so
+federated learning (FL) systems propose to learn from the aggregate data of
+multiple users. FL does not provide perfect secrecy, but rather practitioners
+apply statistical notions of privacy -- i.e., the probability of learning
+private information about a user should be reasonably low. The strength of the
+privacy guarantee is governed by privacy parameters. Numerous privacy attacks
+have been demonstrated on FL systems and it can be challenging to reason about
+the appropriate privacy parameters for a privacy-sensitive use case. Therefore
+our work proposes a simple baseline for FL, which both provides the stronger
+perfect secrecy guarantee and does not require setting any privacy parameters.
+We initiate the study of when and where an emerging tool in ML -- the
+in-context learning abilities of recent pretrained models -- can be an
+effective baseline alongside FL. We find in-context learning is competitive
+with strong FL baselines on 6 of 7 popular benchmarks from the privacy
+literature and a real-world case study, which is disjoint from the pretraining
+data. We release our code here: https://github.com/simran-arora/focus
+"
+Few-Shot Anaphora Resolution in Scientific Protocols via Mixtures of  In-Context Experts,Nghia T. Le,http://arxiv.org/pdf/2210.03690v2.pdf,2022-10-07,"['cs.cl', 'cs.ai']",2210.03690v2.pdf,"  Anaphora resolution is an important task for information extraction across a
+range of languages, text genres, and domains, motivating the need for methods
+that do not require large annotated datasets. In-context learning has emerged
+as a promising approach, yet there are a number of challenges in applying
+in-context learning to resolve anaphora. For example, encoding a single
+in-context demonstration that consists of: an anaphor, a paragraph-length
+context, and a list of corresponding antecedents, requires conditioning a
+language model on a long sequence of tokens, limiting the number of
+demonstrations per prompt. In this paper, we present MICE (Mixtures of
+In-Context Experts), which we demonstrate is effective for few-shot anaphora
+resolution in scientific protocols (Tamari et al., 2021). Given only a handful
+of training examples, MICE combines the predictions of hundreds of in-context
+experts, yielding a 30% increase in F1 score over a competitive prompt
+retrieval baseline. Furthermore, we show MICE can be used to train compact
+student models without sacrificing performance. As far as we are aware, this is
+the first work to present experimental results demonstrating the effectiveness
+of in-context learning on the task of few-shot anaphora resolution in
+scientific protocols.
+"
+What learning algorithm is in-context learning? Investigations with  linear models,Ekin Akyürek,http://arxiv.org/pdf/2211.15661v3.pdf,2022-11-28,"['cs.lg', 'cs.cl']",2211.15661v3.pdf,"  Neural sequence models, especially transformers, exhibit a remarkable
+capacity for in-context learning. They can construct new predictors from
+sequences of labeled examples $(x, f(x))$ presented in the input without
+further parameter updates. We investigate the hypothesis that transformer-based
+in-context learners implement standard learning algorithms implicitly, by
+encoding smaller models in their activations, and updating these implicit
+models as new examples appear in the context. Using linear regression as a
+prototypical problem, we offer three sources of evidence for this hypothesis.
+First, we prove by construction that transformers can implement learning
+algorithms for linear models based on gradient descent and closed-form ridge
+regression. Second, we show that trained in-context learners closely match the
+predictors computed by gradient descent, ridge regression, and exact
+least-squares regression, transitioning between different predictors as
+transformer depth and dataset noise vary, and converging to Bayesian estimators
+for large widths and depths. Third, we present preliminary evidence that
+in-context learners share algorithmic features with these predictors: learners'
+late layers non-linearly encode weight vectors and moment matrices. These
+results suggest that in-context learning is understandable in algorithmic
+terms, and that (at least in the linear case) learners may rediscover standard
+estimation algorithms. Code and reference implementations are released at
+https://github.com/ekinakyurek/google-research/blob/master/incontext.
+"
+SE Factual Knowledge in Frozen Giant Code Model: A Study on FQN and its  Retrieval,Qing Huang,http://arxiv.org/pdf/2212.08221v1.pdf,2022-12-16,['cs.se'],2212.08221v1.pdf,"  Pre-trained giant code models (PCMs) start coming into the developers' daily
+practices. Understanding what types of and how much software knowledge is
+packed into PCMs is the foundation for incorporating PCMs into software
+engineering (SE) tasks and fully releasing their potential. In this work, we
+conduct the first systematic study on the SE factual knowledge in the
+state-of-the-art PCM CoPilot, focusing on APIs' Fully Qualified Names (FQNs),
+the fundamental knowledge for effective code analysis, search and reuse. Driven
+by FQNs' data distribution properties, we design a novel lightweight in-context
+learning on Copilot for FQN inference, which does not require code compilation
+as traditional methods or gradient update by recent FQN prompt-tuning. We
+systematically experiment with five in-context-learning design factors to
+identify the best in-context learning configuration that developers can adopt
+in practice. With this best configuration, we investigate the effects of amount
+of example prompts and FQN data properties on Copilot's FQN inference
+capability. Our results confirm that Copilot stores diverse FQN knowledge and
+can be applied for the FQN inference due to its high inference accuracy and
+non-reliance on code analysis. Based on our experience interacting with
+Copilot, we discuss various opportunities to improve human-CoPilot interaction
+in the FQN inference task.
+"
+Transformers as Algorithms: Generalization and Stability in In-context  Learning,Yingcong Li,http://arxiv.org/pdf/2301.07067v2.pdf,2023-01-17,"['cs.lg', 'cs.cl', 'stat.ml']",2301.07067v2.pdf,"  In-context learning (ICL) is a type of prompting where a transformer model
+operates on a sequence of (input, output) examples and performs inference
+on-the-fly. In this work, we formalize in-context learning as an algorithm
+learning problem where a transformer model implicitly constructs a hypothesis
+function at inference-time. We first explore the statistical aspects of this
+abstraction through the lens of multitask learning: We obtain generalization
+bounds for ICL when the input prompt is (1) a sequence of i.i.d. (input, label)
+pairs or (2) a trajectory arising from a dynamical system. The crux of our
+analysis is relating the excess risk to the stability of the algorithm
+implemented by the transformer. We characterize when transformer/attention
+architecture provably obeys the stability condition and also provide empirical
+verification. For generalization on unseen tasks, we identify an inductive bias
+phenomenon in which the transfer learning risk is governed by the task
+complexity and the number of MTL tasks in a highly predictable manner. Finally,
+we provide numerical evaluations that (1) demonstrate transformers can indeed
+implement near-optimal algorithms on classical regression problems with i.i.d.
+and dynamic data, (2) provide insights on stability, and (3) verify our
+theoretical predictions.
+"
+Adaptive Machine Translation with Large Language Models,Yasmin Moslem,http://arxiv.org/pdf/2301.13294v3.pdf,2023-01-30,['cs.cl'],2301.13294v3.pdf,"  Consistency is a key requirement of high-quality translation. It is
+especially important to adhere to pre-approved terminology and adapt to
+corrected translations in domain-specific projects. Machine translation (MT)
+has achieved significant progress in the area of domain adaptation. However,
+real-time adaptation remains challenging. Large-scale language models (LLMs)
+have recently shown interesting capabilities of in-context learning, where they
+learn to replicate certain input-output text generation patterns, without
+further fine-tuning. By feeding an LLM at inference time with a prompt that
+consists of a list of translation pairs, it can then simulate the domain and
+style characteristics. This work aims to investigate how we can utilize
+in-context learning to improve real-time adaptive MT. Our extensive experiments
+show promising results at translation time. For example, LLMs can adapt to a
+set of in-domain sentence pairs and/or terminology while translating a new
+sentence. We observe that the translation quality with few-shot in-context
+learning can surpass that of strong encoder-decoder MT systems, especially for
+high-resource languages. Moreover, we investigate whether we can combine MT
+from strong encoder-decoder models with fuzzy matches, which can further
+improve translation quality, especially for less supported languages. We
+conduct our experiments across five diverse language pairs, namely
+English-to-Arabic (EN-AR), English-to-Chinese (EN-ZH), English-to-French
+(EN-FR), English-to-Kinyarwanda (EN-RW), and English-to-Spanish (EN-ES).
+"
+ScatterShot: Interactive In-context Example Curation for Text  Transformation,Tongshuang Wu,http://arxiv.org/pdf/2302.07346v1.pdf,2023-02-14,"['cs.hc', 'cs.cl']",2302.07346v1.pdf,"  The in-context learning capabilities of LLMs like GPT-3 allow annotators to
+customize an LLM to their specific tasks with a small number of examples.
+However, users tend to include only the most obvious patterns when crafting
+examples, resulting in underspecified in-context functions that fall short on
+unseen cases. Further, it is hard to know when ""enough"" examples have been
+included even for known patterns. In this work, we present ScatterShot, an
+interactive system for building high-quality demonstration sets for in-context
+learning. ScatterShot iteratively slices unlabeled data into task-specific
+patterns, samples informative inputs from underexplored or not-yet-saturated
+slices in an active learning manner, and helps users label more efficiently
+with the help of an LLM and the current example set. In simulation studies on
+two text perturbation scenarios, ScatterShot sampling improves the resulting
+few-shot functions by 4-5 percentage points over random sampling, with less
+variance as more examples are added. In a user study, ScatterShot greatly helps
+users in covering different patterns in the input space and labeling in-context
+examples more efficiently, resulting in better in-context learning and less
+user effort.
+"
+Resources and Few-shot Learners for In-context Learning in Slavic  Languages,Michal Štefánik,http://arxiv.org/pdf/2304.01922v1.pdf,2023-04-04,['cs.cl'],2304.01922v1.pdf,"  Despite the rapid recent progress in creating accurate and compact in-context
+learners, most recent work focuses on in-context learning (ICL) for tasks in
+English. However, the ability to interact with users of languages outside
+English presents a great potential for broadening the applicability of language
+technologies to non-English speakers.
+  In this work, we collect the infrastructure necessary for training and
+evaluation of ICL in a selection of Slavic languages: Czech, Polish, and
+Russian. We link a diverse set of datasets and cast these into a unified
+instructional format through a set of transformations and newly-crafted
+templates written purely in target languages. Using the newly-curated dataset,
+we evaluate a set of the most recent in-context learners and compare their
+results to the supervised baselines. Finally, we train, evaluate and publish a
+set of in-context learning models that we train on the collected resources and
+compare their performance to previous work.
+  We find that ICL models tuned in English are also able to learn some tasks
+from non-English contexts, but multilingual instruction fine-tuning
+consistently improves the ICL ability. We also find that the massive multitask
+training can be outperformed by single-task training in the target language,
+uncovering the potential for specializing in-context learners to the
+language(s) of their application.
+"
+Boosting Theory-of-Mind Performance in Large Language Models via  Prompting,Shima Rahimi Moghaddam,http://arxiv.org/pdf/2304.11490v3.pdf,2023-04-22,"['cs.ai', 'cs.cl']",2304.11490v3.pdf,"  Large language models (LLMs) excel in many tasks in 2023, but they still face
+challenges in complex reasoning. Theory-of-mind (ToM) tasks, which require
+understanding agents' beliefs, goals, and mental states, are essential for
+common-sense reasoning involving humans, making it crucial to enhance LLM
+performance in this area. This study measures the ToM performance of GPT-4 and
+three GPT-3.5 variants (Davinci-2, Davinci-3, GPT-3.5-Turbo), and investigates
+the effectiveness of in-context learning in improving their ToM comprehension.
+We evaluated prompts featuring two-shot chain of thought reasoning and
+step-by-step thinking instructions. We found that LLMs trained with
+Reinforcement Learning from Human Feedback (RLHF) (all models excluding
+Davinci-2) improved their ToM accuracy via in-context learning. GPT-4 performed
+best in zero-shot settings, reaching nearly 80% ToM accuracy, but still fell
+short of the 87% human accuracy on the test set. However, when supplied with
+prompts for in-context learning, all RLHF-trained LLMs exceeded 80% ToM
+accuracy, with GPT-4 reaching 100%. These results demonstrate that appropriate
+prompting enhances LLM ToM reasoning, and they underscore the context-dependent
+nature of LLM cognitive capacities.
+"
+Unified Demonstration Retriever for In-Context Learning,Xiaonan Li,http://arxiv.org/pdf/2305.04320v2.pdf,2023-05-07,['cs.cl'],2305.04320v2.pdf,"  In-context learning is a new learning paradigm where a language model
+conditions on a few input-output pairs (demonstrations) and a test input, and
+directly outputs the prediction. It has been shown highly dependent on the
+provided demonstrations and thus promotes the research of demonstration
+retrieval: given a test input, relevant examples are retrieved from the
+training set to serve as informative demonstrations for in-context learning.
+While previous works focus on training task-specific retrievers for several
+tasks separately, these methods are often hard to transfer and scale on various
+tasks, and separately trained retrievers incur a lot of parameter storage and
+deployment cost. In this paper, we propose Unified Demonstration Retriever
+(\textbf{UDR}), a single model to retrieve demonstrations for a wide range of
+tasks. To train UDR, we cast various tasks' training signals into a unified
+list-wise ranking formulation by language model's feedback. Then we propose a
+multi-task list-wise ranking training framework, with an iterative mining
+strategy to find high-quality candidates, which can help UDR fully incorporate
+various tasks' signals. Experiments on 30+ tasks across 13 task families and
+multiple data domains show that UDR significantly outperforms baselines.
+Further analyses show the effectiveness of each proposed component and UDR's
+strong ability in various scenarios including different LMs (1.3B - 175B),
+unseen datasets, varying demonstration quantities, etc.
+"
+Efficient Prompting via Dynamic In-Context Learning,Wangchunshu Zhou,http://arxiv.org/pdf/2305.11170v1.pdf,2023-05-18,"['cs.cl', 'cs.ai', 'cs.lg']",2305.11170v1.pdf,"  The primary way of building AI applications is shifting from training
+specialist models to prompting generalist models. A common practice for
+prompting generalist models, often referred to as in-context learning, is to
+append a few examples (demonstrations) to the prompt to help the model better
+understand the task. While effective, in-context learning can be inefficient
+because it makes the input prompt much longer, consuming valuable space in the
+context window and leading to larger computational costs. In this paper, we
+propose DynaICL, a recipe for efficient prompting with black-box generalist
+models that dynamically allocate in-context examples according to the input
+complexity and the computational budget. To achieve this, we train a meta
+controller that predicts the number of in-context examples suitable for the
+generalist model to make a good prediction based on the performance-efficiency
+trade-off for a specific input. We then dynamically allocate the number of
+demonstrations for an input according to predictions from the meta controller
+and the given computation budget. Experimental results show that dynamic
+example allocation helps achieve a better performance-efficiency trade-off in
+two practical settings where computational resources or the required
+performance is constrained. Specifically, DynaICL saves up to 46% token budget
+compared to the common practice that allocates the same number of in-context
+examples to each input. We also find that a meta controller trained on a
+certain backbone model and tasks can successfully generalize to unseen models
+and tasks.
+"
+Post Hoc Explanations of Language Models Can Improve Language Models,Satyapriya Krishna,http://arxiv.org/pdf/2305.11426v2.pdf,2023-05-19,"['cs.cl', 'cs.ai']",2305.11426v2.pdf,"  Large Language Models (LLMs) have demonstrated remarkable capabilities in
+performing complex tasks. Moreover, recent research has shown that
+incorporating human-annotated rationales (e.g., Chain-of-Thought prompting)
+during in-context learning can significantly enhance the performance of these
+models, particularly on tasks that require reasoning capabilities. However,
+incorporating such rationales poses challenges in terms of scalability as this
+requires a high degree of human involvement. In this work, we present a novel
+framework, Amplifying Model Performance by Leveraging In-Context Learning with
+Post Hoc Explanations (AMPLIFY), which addresses the aforementioned challenges
+by automating the process of rationale generation. To this end, we leverage
+post hoc explanation methods which output attribution scores (explanations)
+capturing the influence of each of the input features on model predictions.
+More specifically, we construct automated natural language rationales that
+embed insights from post hoc explanations to provide corrective signals to
+LLMs. Extensive experimentation with real-world datasets demonstrates that our
+framework, AMPLIFY, leads to prediction accuracy improvements of about 10-25%
+over a wide range of tasks, including those where prior approaches which rely
+on human-annotated rationales such as Chain-of-Thought prompting fall short.
+Our work makes one of the first attempts at highlighting the potential of post
+hoc explanations as valuable tools for enhancing the effectiveness of LLMs.
+Furthermore, we conduct additional empirical analyses and ablation studies to
+demonstrate the impact of each of the components of AMPLIFY, which, in turn,
+leads to critical insights for refining in-context learning.
+"
+Explaining Emergent In-Context Learning as Kernel Regression,Chi Han,http://arxiv.org/pdf/2305.12766v2.pdf,2023-05-22,"['cs.cl', 'cs.ai', 'cs.lg']",2305.12766v2.pdf,"  Large language models (LLMs) have initiated a paradigm shift in transfer
+learning. In contrast to the classic pretraining-then-finetuning procedure, in
+order to use LLMs for downstream prediction tasks, one only needs to provide a
+few demonstrations, known as in-context examples, without adding more or
+updating existing model parameters. This in-context learning (ICL) capability
+of LLMs is intriguing, and it is not yet fully understood how pretrained LLMs
+acquire such capabilities. In this paper, we investigate the reason why a
+transformer-based language model can accomplish in-context learning after
+pre-training on a general language corpus by proposing one hypothesis that LLMs
+can simulate kernel regression with internal representations when faced with
+in-context examples. More concretely, we first prove that Bayesian inference on
+in-context prompts can be asymptotically understood as kernel regression $\hat
+y = \sum_i y_i K(x, x_i)/\sum_i K(x, x_i)$ as the number of in-context
+demonstrations grows. Then, we empirically investigate the in-context behaviors
+of language models. We find that during ICL, the attention and hidden features
+in LLMs match the behaviors of a kernel regression. Finally, our theory
+provides insights into multiple phenomena observed in the ICL field: why
+retrieving demonstrative samples similar to test samples can help, why ICL
+performance is sensitive to the output formats, and why ICL accuracy benefits
+from selecting in-distribution and representative samples.
+"
+RetICL: Sequential Retrieval of In-Context Examples with Reinforcement  Learning,Alexander Scarlatos,http://arxiv.org/pdf/2305.14502v1.pdf,2023-05-23,"['cs.cl', 'cs.ai', 'cs.lg']",2305.14502v1.pdf,"  Many recent developments in large language models focus on prompting them to
+perform specific tasks. One effective prompting method is in-context learning,
+where the model performs a (possibly new) generation/prediction task given one
+(or more) examples. Past work has shown that the choice of examples can make a
+large impact on task performance. However, finding good examples is not
+straightforward since the definition of a representative group of examples can
+vary greatly depending on the task. While there are many existing methods for
+selecting in-context examples, they generally score examples independently,
+ignoring the dependency between them and the order in which they are provided
+to the large language model. In this work, we propose Retrieval for In-Context
+Learning (RetICL), a learnable method for modeling and optimally selecting
+examples sequentially for in-context learning. We frame the problem of
+sequential example selection as a Markov decision process, design an example
+retriever model using an LSTM, and train it using proximal policy optimization
+(PPO). We validate RetICL on math problem solving datasets and show that it
+outperforms both heuristic and learnable baselines, and achieves
+state-of-the-art accuracy on the TabMWP dataset. We also use case studies to
+show that RetICL implicitly learns representations of math problem solving
+strategies.
+"
+In-Context Learning for Attention Scheme: from Single Softmax Regression  to Multiple Softmax Regression via a Tensor Trick,Yeqi Gao,http://arxiv.org/pdf/2307.02419v1.pdf,2023-07-05,['cs.lg'],2307.02419v1.pdf,"  Large language models (LLMs) have brought significant and transformative
+changes in human society. These models have demonstrated remarkable
+capabilities in natural language understanding and generation, leading to
+various advancements and impacts across several domains.
+  We consider the in-context learning under two formulation for attention
+related regression in this work. Given matrices $A_1 \in \mathbb{R}^{n \times
+d}$, and $A_2 \in \mathbb{R}^{n \times d}$ and $B \in \mathbb{R}^{n \times n}$,
+the purpose is to solve some certain optimization problems: Normalized version
+$\min_{X} \| D(X)^{-1} \exp(A_1 X A_2^\top) - B \|_F^2$ and Rescaled version
+$\| \exp(A_1 X A_2^\top) - D(X) \cdot B \|_F^2$. Here $D(X) := \mathrm{diag}(
+\exp(A_1 X A_2^\top) {\bf 1}_n )$.
+  Our regression problem shares similarities with previous studies on
+softmax-related regression. Prior research has extensively investigated
+regression techniques related to softmax regression: Normalized version $\|
+\langle \exp(Ax) , {\bf 1}_n \rangle^{-1} \exp(Ax) - b \|_2^2$ and Resscaled
+version $\| \exp(Ax) - \langle \exp(Ax), {\bf 1}_n \rangle b \|_2^2 $
+  In contrast to previous approaches, we adopt a vectorization technique to
+address the regression problem in matrix formulation. This approach expands the
+dimension from $d$ to $d^2$, resembling the formulation of the regression
+problem mentioned earlier.
+  Upon completing the lipschitz analysis of our regression function, we have
+derived our main result concerning in-context learning.
+"
+SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction  and Drug Design,Carl Edwards,http://arxiv.org/pdf/2307.11694v2.pdf,2023-06-19,"['cs.ai', 'cs.lg', 'q-bio.bm', 'q-bio.mn']",2307.11694v2.pdf,"  Predicting synergistic drug combinations can help accelerate discovery of
+cancer treatments, particularly therapies personalized to a patient's specific
+tumor via biopsied cells. In this paper, we propose a novel setting and models
+for in-context drug synergy learning. We are given a small ""personalized
+dataset"" of 10-20 drug synergy relationships in the context of specific cancer
+cell targets. Our goal is to predict additional drug synergy relationships in
+that context. Inspired by recent work that pre-trains a GPT language model (LM)
+to ""in-context learn"" common function classes, we devise novel pre-training
+schemes that enable a GPT model to in-context learn ""drug synergy functions"".
+Our model -- which does not use any textual corpora, molecular fingerprints,
+protein interaction or any other domain-specific knowledge -- is able to
+achieve competitive results. We further integrate our in-context approach with
+a genetic algorithm to optimize model prompts and select synergy candidates to
+test after conducting a patient biopsy. Finally, we explore a novel task of
+inverse drug design which can potentially enable the design of drugs that
+synergize specifically to target a given patient's ""personalized dataset"". Our
+findings can potentially have an important impact on precision cancer medicine,
+and also raise intriguing questions on non-textual pre-training for LMs.
+"
+OUTFOX: LLM-generated Essay Detection through In-context Learning with  Adversarially Generated Examples,Ryuto Koike,http://arxiv.org/pdf/2307.11729v2.pdf,2023-07-21,['cs.cl'],2307.11729v2.pdf,"  Large Language Models (LLMs) have achieved human-level fluency in text
+generation, making it difficult to distinguish between human-written and
+LLM-generated texts. This poses a growing risk of misuse of LLMs and demands
+the development of detectors to identify LLM-generated texts. However, existing
+detectors lack robustness against attacks: they degrade detection accuracy by
+simply paraphrasing LLM-generated texts. Furthermore, a malicious user might
+attempt to deliberately evade the detectors based on detection results, but
+this has not been assumed in previous studies. In this paper, we propose
+OUTFOX, a framework that improves the robustness of LLM-generated-text
+detectors by allowing both the detector and the attacker to consider each
+other's output. In this framework, the attacker uses the detector's prediction
+labels as examples for in-context learning and adversarially generates essays
+that are harder to detect, while the detector uses the adversarially generated
+essays as examples for in-context learning to learn to detect essays from a
+strong attacker. Experiments in the domain of student essays show that the
+proposed detector improves the detection performance on the attacker-generated
+texts by up to +41.3 points in F1-score. Furthermore, the proposed detector
+shows a state-of-the-art detection performance: up to 96.9 points in F1-score,
+beating existing detectors on non-attacked texts. Finally, the proposed
+attacker drastically degrades the performance of detectors by up to -57.0
+points F1-score, massively outperforming the baseline paraphrasing method for
+evading detection.
+"
+Metric-Based In-context Learning: A Case Study in Text Simplification,Subha Vadlamannati,http://arxiv.org/pdf/2307.14632v1.pdf,2023-07-27,"['cs.cl', 'cs.ai']",2307.14632v1.pdf,"  In-context learning (ICL) for large language models has proven to be a
+powerful approach for many natural language processing tasks. However,
+determining the best method to select examples for ICL is nontrivial as the
+results can vary greatly depending on the quality, quantity, and order of
+examples used. In this paper, we conduct a case study on text simplification
+(TS) to investigate how to select the best and most robust examples for ICL. We
+propose Metric-Based in-context Learning (MBL) method that utilizes commonly
+used TS metrics such as SARI, compression ratio, and BERT-Precision for
+selection. Through an extensive set of experiments with various-sized GPT
+models on standard TS benchmarks such as TurkCorpus and ASSET, we show that
+examples selected by the top SARI scores perform the best on larger models such
+as GPT-175B, while the compression ratio generally performs better on smaller
+models such as GPT-13B and GPT-6.7B. Furthermore, we demonstrate that MBL is
+generally robust to example orderings and out-of-domain test sets, and
+outperforms strong baselines and state-of-the-art finetuned language models.
+Finally, we show that the behaviour of large GPT models can be implicitly
+controlled by the chosen metric. Our research provides a new framework for
+selecting examples in ICL, and demonstrates its effectiveness in text
+simplification tasks, breaking new ground for more accurate and efficient NLG
+systems.
+"
+HICL: Hashtag-Driven In-Context Learning for Social Media Natural  Language Understanding,Hanzhuo Tan,http://arxiv.org/pdf/2308.09985v1.pdf,2023-08-19,['cs.cl'],2308.09985v1.pdf,"  Natural language understanding (NLU) is integral to various social media
+applications. However, existing NLU models rely heavily on context for semantic
+learning, resulting in compromised performance when faced with short and noisy
+social media content. To address this issue, we leverage in-context learning
+(ICL), wherein language models learn to make inferences by conditioning on a
+handful of demonstrations to enrich the context and propose a novel
+hashtag-driven in-context learning (HICL) framework. Concretely, we pre-train a
+model #Encoder, which employs #hashtags (user-annotated topic labels) to drive
+BERT-based pre-training through contrastive learning. Our objective here is to
+enable #Encoder to gain the ability to incorporate topic-related semantic
+information, which allows it to retrieve topic-related posts to enrich contexts
+and enhance social media NLU with noisy contexts. To further integrate the
+retrieved context with the source text, we employ a gradient-based method to
+identify trigger terms useful in fusing information from both sources. For
+empirical studies, we collected 45M tweets to set up an in-context NLU
+benchmark, and the experimental results on seven downstream tasks show that
+HICL substantially advances the previous state-of-the-art results. Furthermore,
+we conducted extensive analyzes and found that: (1) combining source input with
+a top-retrieved post from #Encoder is more effective than using semantically
+similar posts; (2) trigger words can largely benefit in merging context from
+the source and retrieved posts.
+"
+Improving the Reliability of Large Language Models by Leveraging  Uncertainty-Aware In-Context Learning,Yuchen Yang,http://arxiv.org/pdf/2310.04782v1.pdf,2023-10-07,['cs.cl'],2310.04782v1.pdf,"  In recent years, large-scale language models (LLMs) have gained attention for
+their impressive text generation capabilities. However, these models often face
+the challenge of ""hallucination,"" which undermines their reliability. In this
+study, we introduce an uncertainty-aware in-context learning framework to
+empower the model to enhance or reject its output in response to uncertainty.
+Human-defined methods for estimating uncertainty typically assume that
+""uncertainty is lower when the model's response is correct compared to when it
+is incorrect."" However, setting a precise threshold to distinguish correctness
+is challenging. Therefore, we introduce uncertainty information as an
+intermediary variable that implicitly influences the model's behavior. Our
+innovative uncertainty-aware in-context learning framework involves fine-tuning
+the LLM using a calibration dataset. Our aim is to improve the model's
+responses by filtering out answers with high uncertainty while considering the
+model's knowledge limitations. We evaluate the model's knowledge by examining
+multiple responses to the same question for the presence of a correct answer.
+When the model lacks relevant knowledge, the response should indicate that the
+question cannot be answered. Conversely, when the model has relevant knowledge,
+the response should provide the correct answer. Extensive experiments confirm
+the effectiveness of our framework, leading to two key findings. First, the
+logit output values of the LLM partly reflect inherent uncertainty. Second, our
+model autonomously recognizes uncertainty, resulting in improved responses.
+"
+In-Context Convergence of Transformers,Yu Huang,http://arxiv.org/pdf/2310.05249v1.pdf,2023-10-08,"['cs.lg', 'cs.ai', 'math.oc', 'stat.ml']",2310.05249v1.pdf,"  Transformers have recently revolutionized many domains in modern machine
+learning and one salient discovery is their remarkable in-context learning
+capability, where models can solve an unseen task by utilizing task-specific
+prompts without further parameters fine-tuning. This also inspired recent
+theoretical studies aiming to understand the in-context learning mechanism of
+transformers, which however focused only on linear transformers. In this work,
+we take the first step toward studying the learning dynamics of a one-layer
+transformer with softmax attention trained via gradient descent in order to
+in-context learn linear function classes. We consider a structured data model,
+where each token is randomly sampled from a set of feature vectors in either
+balanced or imbalanced fashion. For data with balanced features, we establish
+the finite-time convergence guarantee with near-zero prediction error by
+navigating our analysis over two phases of the training dynamics of the
+attention map. More notably, for data with imbalanced features, we show that
+the learning dynamics take a stage-wise convergence process, where the
+transformer first converges to a near-zero prediction error for the query
+tokens of dominant features, and then converges later to a near-zero prediction
+error for the query tokens of under-represented features, respectively via one
+and four training phases. Our proof features new techniques for analyzing the
+competing strengths of two types of attention weights, the change of which
+determines different training phases.
+"
+Large Language Model-Aware In-Context Learning for Code Generation,Jia Li,http://arxiv.org/pdf/2310.09748v1.pdf,2023-10-15,"['cs.se', 'cs.cl']",2310.09748v1.pdf,"  Large language models (LLMs) have shown impressive in-context learning (ICL)
+ability in code generation. LLMs take a prompt consisting of requirement-code
+examples and a new requirement as input, and output new programs. Existing
+studies have found that ICL is highly dominated by the examples and thus arises
+research on example selection. However, existing approaches randomly select
+examples or only consider the textual similarity of requirements to retrieve,
+leading to sub-optimal performance. In this paper, we propose a novel
+learning-based selection approach named LAIL (LLM-Aware In-context Learning)
+for code generation. Given a candidate example, we exploit LLMs themselves to
+estimate it by considering the generation probabilities of ground-truth
+programs given a requirement and the example. We then label candidate examples
+as positive or negative through the probability feedback. Based on the labeled
+data, we import a contrastive learning objective to train an effective
+retriever that acquires the preference of LLMs in code generation. We apply
+LAIL to three LLMs and evaluate it on three representative datasets (e.g.,
+MBJP, MBPP, and MBCPP). LATA outperforms the state-of-the-art baselines by
+11.58%, 6.89%, and 5.07% on CodeGen, and 4.38%, 2.85%, and 2.74% on GPT-3.5 in
+terms of Pass@1, respectively.
+"
+Two-stage LLM Fine-tuning with Less Specialization and More  Generalization,Yihan Wang,http://arxiv.org/pdf/2211.00635v2.pdf,2022-11-01,"['cs.cl', 'cs.lg']",2211.00635v2.pdf,"  Pretrained large language models (LLMs) are general purpose problem solvers
+applicable to a diverse set of tasks with prompts. They can be further improved
+towards a specific task by fine-tuning on a specialized dataset. However,
+fine-tuning usually makes the model narrowly specialized on this dataset with
+reduced general in-context learning performances, which is undesirable whenever
+the fine-tuned model needs to handle additional tasks where no fine-tuning data
+is available. In this work, we first demonstrate that fine-tuning on a single
+task indeed decreases LLMs' general in-context learning performance. We
+discover one important cause of such forgetting, format specialization, where
+the model overfits to the format of the fine-tuned task. We further show that
+format specialization happens at the very beginning of fine-tuning. To solve
+this problem, we propose Prompt Tuning with MOdel Tuning (ProMoT), a simple yet
+effective two-stage fine-tuning framework that reduces format specialization
+and improves generalization. ProMoT offloads task-specific format learning into
+additional and removable parameters by first doing prompt tuning and then
+fine-tuning the model itself with this soft prompt attached. With experiments
+on several fine-tuning tasks and 8 in-context evaluation tasks, we show that
+ProMoT achieves comparable performance on fine-tuned tasks to standard
+fine-tuning, but with much less loss of in-context learning performances across
+a board range of out-of-domain evaluation tasks. More importantly, ProMoT can
+even enhance generalization on in-context learning tasks that are semantically
+related to the fine-tuned task, e.g. ProMoT on En-Fr translation significantly
+improves performance on other language pairs, and ProMoT on NLI improves
+performance on summarization. Experiments also show that ProMoT can improve the
+generalization performance of multi-task training.
+"
+On the Relation between Sensitivity and Accuracy in In-context Learning,Yanda Chen,http://arxiv.org/pdf/2209.07661v2.pdf,2022-09-16,"['cs.cl', 'cs.ai', 'cs.lg']",2209.07661v2.pdf,"  In-context learning (ICL) suffers from oversensitivity to the prompt, making
+it unreliable in real-world scenarios. We study the sensitivity of ICL with
+respect to multiple perturbation types. First, we find that label bias obscures
+the true sensitivity, and therefore prior work may have significantly
+underestimated ICL sensitivity. Second, we observe a strong negative
+correlation between ICL sensitivity and accuracy: predictions sensitive to
+perturbations are less likely to be correct. Motivated by these findings, we
+propose \textsc{SenSel}, a few-shot selective prediction method that abstains
+from sensitive predictions. Experiments on ten classification datasets show
+that \textsc{SenSel} consistently outperforms two commonly used
+confidence-based and entropy-based baselines on abstention decisions.
+"
+WinoDict: Probing language models for in-context word acquisition,Julian Martin Eisenschlos,http://arxiv.org/pdf/2209.12153v1.pdf,2022-09-25,"['cs.cl', 'cs.ai']",2209.12153v1.pdf,"  We introduce a new in-context learning paradigm to measure Large Language
+Models' (LLMs) ability to learn novel words during inference. In particular, we
+rewrite Winograd-style co-reference resolution problems by replacing the key
+concept word with a synthetic but plausible word that the model must understand
+to complete the task. Solving this task requires the model to make use of the
+dictionary definition of the new word given in the prompt. This benchmark
+addresses word acquisition, one important aspect of the diachronic degradation
+known to afflict LLMs. As LLMs are frozen in time at the moment they are
+trained, they are normally unable to reflect the way language changes over
+time. We show that the accuracy of LLMs compared to the original Winograd tasks
+decreases radically in our benchmark, thus identifying a limitation of current
+models and providing a benchmark to measure future improvements in LLMs ability
+to do in-context learning.
+"
+Data Curation Alone Can Stabilize In-context Learning,Ting-Yun Chang,http://arxiv.org/pdf/2212.10378v2.pdf,2022-12-20,['cs.cl'],2212.10378v2.pdf,"  In-context learning (ICL) enables large language models (LLMs) to perform new
+tasks by prompting them with a sequence of training examples. However, it is
+known that ICL is very sensitive to the choice of training examples: randomly
+sampling examples from a training set leads to high variance in performance. In
+this paper, we show that carefully curating a subset of training data greatly
+stabilizes ICL performance without any other changes to the ICL algorithm
+(e.g., prompt retrieval or calibration). We introduce two methods to choose
+training subsets -- both score training examples individually, then select the
+highest-scoring ones. CondAcc scores a training example by its average dev-set
+ICL accuracy when combined with random training examples, while Datamodels
+learns linear regressors that estimate how the presence of each training
+example influences LLM outputs. Across five tasks and two LLMs, sampling from
+stable subsets selected by CondAcc and Datamodels improves average accuracy
+over sampling from the entire training set by 7.7% and 6.3%, respectively.
+Surprisingly, the stable subset examples are not especially diverse in content
+or low in perplexity, in contrast with other work suggesting that diversity and
+perplexity are important when prompting LLMs.
+"
+A Survey on In-context Learning,Qingxiu Dong,http://arxiv.org/pdf/2301.00234v3.pdf,2022-12-31,"['cs.cl', 'cs.ai']",2301.00234v3.pdf,"  With the increasing ability of large language models (LLMs), in-context
+learning (ICL) has become a new paradigm for natural language processing (NLP),
+where LLMs make predictions only based on contexts augmented with a few
+examples. It has been a new trend to explore ICL to evaluate and extrapolate
+the ability of LLMs. In this paper, we aim to survey and summarize the progress
+and challenges of ICL. We first present a formal definition of ICL and clarify
+its correlation to related studies. Then, we organize and discuss advanced
+techniques, including training strategies, demonstration designing strategies,
+as well as related analysis. Finally, we discuss the challenges of ICL and
+provide potential directions for further research. We hope that our work can
+encourage more research on uncovering how ICL works and improving ICL.
+"
+Using In-Context Learning to Improve Dialogue Safety,Nicholas Meade,http://arxiv.org/pdf/2302.00871v3.pdf,2023-02-02,['cs.cl'],2302.00871v3.pdf,"  While large neural-based conversational models have become increasingly
+proficient dialogue agents, recent work has highlighted safety issues with
+these systems. For example, these systems can be goaded into generating toxic
+content, which often perpetuates social biases or stereotypes. We investigate a
+retrieval-based method for reducing bias and toxicity in responses from
+chatbots. It uses in-context learning to steer a model towards safer
+generations. Concretely, to generate a response to an unsafe dialogue context,
+we retrieve demonstrations of safe responses to similar dialogue contexts. We
+find our method performs competitively with strong baselines without requiring
+training. For instance, using automatic evaluation, we find our best fine-tuned
+baseline only generates safe responses to unsafe dialogue contexts from
+DiaSafety 4.04% more than our approach. Finally, we also propose a re-ranking
+procedure which can further improve response safeness.
+"
+Towards Few-Shot Identification of Morality Frames using In-Context  Learning,Shamik Roy,http://arxiv.org/pdf/2302.02029v1.pdf,2023-02-03,['cs.cl'],2302.02029v1.pdf,"  Data scarcity is a common problem in NLP, especially when the annotation
+pertains to nuanced socio-linguistic concepts that require specialized
+knowledge. As a result, few-shot identification of these concepts is desirable.
+Few-shot in-context learning using pre-trained Large Language Models (LLMs) has
+been recently applied successfully in many NLP tasks. In this paper, we study
+few-shot identification of a psycho-linguistic concept, Morality Frames (Roy et
+al., 2021), using LLMs. Morality frames are a representation framework that
+provides a holistic view of the moral sentiment expressed in text, identifying
+the relevant moral foundation (Haidt and Graham, 2007) and at a finer level of
+granularity, the moral sentiment expressed towards the entities mentioned in
+the text. Previous studies relied on human annotation to identify morality
+frames in text which is expensive. In this paper, we propose prompting-based
+approaches using pretrained Large Language Models for identification of
+morality frames, relying only on few-shot exemplars. We compare our models'
+performance with few-shot RoBERTa and found promising results.
+"
+OpenICL: An Open-Source Framework for In-context Learning,Zhenyu Wu,http://arxiv.org/pdf/2303.02913v1.pdf,2023-03-06,['cs.cl'],2303.02913v1.pdf,"  In recent years, In-context Learning (ICL) has gained increasing attention
+and emerged as the new paradigm for large language model (LLM) evaluation.
+Unlike traditional fine-tuning methods, ICL instead adapts the pre-trained
+models to unseen tasks without any parameter updates. However, the
+implementation of ICL is sophisticated due to the diverse retrieval and
+inference methods involved, as well as the varying pre-processing requirements
+for different models, datasets, and tasks. A unified and flexible framework for
+ICL is urgently needed to ease the implementation of the aforementioned
+components. To facilitate ICL research, we introduce OpenICL, an open-source
+toolkit for ICL and LLM evaluation. OpenICL is research-friendly with a highly
+flexible architecture that users can easily combine different components to
+suit their needs. It also provides various state-of-the-art retrieval and
+inference methods to streamline the process of adapting ICL to cutting-edge
+research. The effectiveness of OpenICL has been validated on a wide range of
+NLP tasks, including classification, QA, machine translation, and semantic
+parsing. As a side-product, we found OpenICL to be an efficient yet robust tool
+for LLMs evaluation. OpenICL is released at
+https://github.com/Shark-NLP/OpenICL
+"
+The Scope of In-Context Learning for the Extraction of Medical Temporal  Constraints,Parker Seegmiller,http://arxiv.org/pdf/2303.09366v2.pdf,2023-03-16,"['cs.cl', 'cs.lg']",2303.09366v2.pdf,"  Medications often impose temporal constraints on everyday patient activity.
+Violations of such medical temporal constraints (MTCs) lead to a lack of
+treatment adherence, in addition to poor health outcomes and increased
+healthcare expenses. These MTCs are found in drug usage guidelines (DUGs) in
+both patient education materials and clinical texts. Computationally
+representing MTCs in DUGs will advance patient-centric healthcare applications
+by helping to define safe patient activity patterns. We define a novel taxonomy
+of MTCs found in DUGs and develop a novel context-free grammar (CFG) based
+model to computationally represent MTCs from unstructured DUGs. Additionally,
+we release three new datasets with a combined total of N = 836 DUGs labeled
+with normalized MTCs. We develop an in-context learning (ICL) solution for
+automatically extracting and normalizing MTCs found in DUGs, achieving an
+average F1 score of 0.62 across all datasets. Finally, we rigorously
+investigate ICL model performance against a baseline model, across datasets and
+MTC types, and through in-depth error analysis.
+"
+How to Unleash the Power of Large Language Models for Few-shot Relation  Extraction?,Xin Xu,http://arxiv.org/pdf/2305.01555v4.pdf,2023-05-02,"['cs.cl', 'cs.ai', 'cs.db', 'cs.ir', 'cs.lg']",2305.01555v4.pdf,"  Scaling language models have revolutionized widespread NLP tasks, yet little
+comprehensively explored few-shot relation extraction with large language
+models. In this paper, we investigate principal methodologies, in-context
+learning and data generation, for few-shot relation extraction via GPT-3.5
+through exhaustive experiments. To enhance few-shot performance, we further
+propose task-related instructions and schema-constrained data generation. We
+observe that in-context learning can achieve performance on par with previous
+prompt learning approaches, and data generation with the large language model
+can boost previous solutions to obtain new state-of-the-art few-shot results on
+four widely-studied relation extraction datasets. We hope our work can inspire
+future research for the capabilities of large language models in few-shot
+relation extraction. Code is available in
+https://github.com/zjunlp/DeepKE/tree/main/example/llm.
+"
+GPT-RE: In-context Learning for Relation Extraction using Large Language  Models,Zhen Wan,http://arxiv.org/pdf/2305.02105v2.pdf,2023-05-03,['cs.cl'],2305.02105v2.pdf,"  In spite of the potential for ground-breaking achievements offered by large
+language models (LLMs) (e.g., GPT-3), they still lag significantly behind
+fully-supervised baselines (e.g., fine-tuned BERT) in relation extraction (RE).
+This is due to the two major shortcomings of LLMs in RE: (1) low relevance
+regarding entity and relation in retrieved demonstrations for in-context
+learning; and (2) the strong inclination to wrongly classify NULL examples into
+other pre-defined labels.
+  In this paper, we propose GPT-RE to bridge the gap between LLMs and
+fully-supervised baselines. GPT-RE successfully addresses the aforementioned
+issues by (1) incorporating task-specific entity representations in
+demonstration retrieval; and (2) enriching the demonstrations with gold
+label-induced reasoning logic. We evaluate GPT-RE on four widely-used RE
+datasets, and observe that GPT-RE achieves improvements over not only existing
+GPT-3 baselines, but also fully-supervised baselines. Specifically, GPT-RE
+achieves SOTA performances on the Semeval and SciERC datasets, and competitive
+performances on the TACRED and ACE05 datasets.
+"
+GersteinLab at MEDIQA-Chat 2023: Clinical Note Summarization from  Doctor-Patient Conversations through Fine-tuning and In-context Learning,Xiangru Tang,http://arxiv.org/pdf/2305.05001v1.pdf,2023-05-08,['cs.cl'],2305.05001v1.pdf,"  This paper presents our contribution to the MEDIQA-2023 Dialogue2Note shared
+task, encompassing both subtask A and subtask B. We approach the task as a
+dialogue summarization problem and implement two distinct pipelines: (a) a
+fine-tuning of a pre-trained dialogue summarization model and GPT-3, and (b)
+few-shot in-context learning (ICL) using a large language model, GPT-4. Both
+methods achieve excellent results in terms of ROUGE-1 F1, BERTScore F1
+(deberta-xlarge-mnli), and BLEURT, with scores of 0.4011, 0.7058, and 0.5421,
+respectively. Additionally, we predict the associated section headers using
+RoBERTa and SciBERT based classification models. Our team ranked fourth among
+all teams, while each team is allowed to submit three runs as part of their
+submission. We also utilize expert annotations to demonstrate that the notes
+generated through the ICL GPT-4 are better than all other baselines. The code
+for our submission is available.
+"
+Can We Edit Factual Knowledge by In-Context Learning?,Ce Zheng,http://arxiv.org/pdf/2305.12740v1.pdf,2023-05-22,['cs.cl'],2305.12740v1.pdf,"  Previous studies have shown that large language models (LLMs) like GPTs store
+massive factual knowledge in their parameters. However, the stored knowledge
+could be false or out-dated. Traditional knowledge editing methods refine LLMs
+via fine-tuning on texts containing specific knowledge. However, with the
+increasing scales of LLMs, these gradient-based approaches bring large
+computation costs. The trend of model-as-a-service also makes it impossible to
+modify knowledge in black-box LMs. Inspired by in-context learning (ICL), a new
+paradigm based on demonstration contexts without parameter updating, we explore
+whether ICL can edit factual knowledge. To answer this question, we give a
+comprehensive empirical study of ICL strategies. Experiments show that
+in-context knowledge editing (IKE), without any gradient and parameter
+updating, achieves a competitive success rate compared to gradient-based
+methods on GPT-J (6B) but with much fewer side effects, including less
+over-editing on similar but unrelated facts and less knowledge forgetting on
+previously stored knowledge. We also apply the method to larger LMs with tens
+or hundreds of parameters like OPT-175B, which shows the scalability of our
+method. The code is available at https://github.com/Zce1112zslx/IKE.
+"
+Concept-aware Training Improves In-context Learning Ability of Language  Models,Michal Štefánik,http://arxiv.org/pdf/2305.13775v1.pdf,2023-05-23,"['cs.cl', 'cs.ai']",2305.13775v1.pdf,"  Many recent language models (LMs) of Transformers family exhibit so-called
+in-context learning (ICL) ability, manifested in the LMs' ability to modulate
+their function by a task described in a natural language input. Previous work
+curating these models assumes that ICL emerges from vast over-parametrization
+or the scale of multi-task training. However, a complementary branch of recent
+theoretical work attributes ICL emergence to specific properties of training
+data and creates functional in-context learners in small-scale, synthetic
+settings.
+  Inspired by recent findings on data properties driving the emergence of ICL,
+we propose a method to create LMs able to better utilize the in-context
+information, by constructing training scenarios where it is beneficial for the
+LM to capture the analogical reasoning concepts. We measure that data sampling
+of Concept-aware Training (CoAT) consistently improves models' reasoning
+ability. As a result, the in-context learners trained with CoAT on only two
+datasets of a single (QA) task perform comparably to larger models trained on
+1600+ tasks.
+"
+Dr.ICL: Demonstration-Retrieved In-context Learning,Man Luo,http://arxiv.org/pdf/2305.14128v1.pdf,2023-05-23,"['cs.cl', 'cs.ai']",2305.14128v1.pdf,"  In-context learning (ICL), teaching a large language model (LLM) to perform a
+task with few-shot demonstrations rather than adjusting the model parameters,
+has emerged as a strong paradigm for using LLMs. While early studies primarily
+used a fixed or random set of demonstrations for all test queries, recent
+research suggests that retrieving semantically similar demonstrations to the
+input from a pool of available demonstrations results in better performance.
+This work expands the applicability of retrieval-based ICL approaches by
+demonstrating that even simple word-overlap similarity measures such as BM25
+outperform randomly selected demonstrations. Furthermore, we extend the success
+of retrieval-based ICL to instruction-finetuned LLMs as well as
+Chain-of-Thought (CoT) prompting. For instruction-finetuned LLMs, we find that
+although a model has already seen the training data at training time,
+retrieving demonstrations from the training data at test time yields better
+results compared to using no demonstrations or random demonstrations. Last but
+not least, we train a task-specific demonstration retriever that outperforms
+off-the-shelf retrievers.
+"
+Label Words are Anchors: An Information Flow Perspective for  Understanding In-Context Learning,Lean Wang,http://arxiv.org/pdf/2305.14160v1.pdf,2023-05-23,"['cs.cl', 'cs.lg']",2305.14160v1.pdf,"  In-context learning (ICL) emerges as a promising capability of large language
+models (LLMs) by providing them with demonstration examples to perform diverse
+tasks. However, the underlying mechanism of how LLMs learn from the provided
+context remains under-explored. In this paper, we investigate the working
+mechanism of ICL through an information flow lens. Our findings reveal that
+label words in the demonstration examples function as anchors: (1) semantic
+information aggregates into label word representations during the shallow
+computation layers' processing; (2) the consolidated information in label words
+serves as a reference for LLMs' final predictions. Based on these insights, we
+introduce an anchor re-weighting method to improve ICL performance, a
+demonstration compression technique to expedite inference, and an analysis
+framework for diagnosing ICL errors in GPT2-XL. The promising applications of
+our findings again validate the uncovered ICL working mechanism and pave the
+way for future studies.
+"
+Probing in Context: Toward Building Robust Classifiers via Probing Large  Language Models,Afra Amini,http://arxiv.org/pdf/2305.14171v2.pdf,2023-05-23,['cs.cl'],2305.14171v2.pdf,"  Large language models are able to learn new tasks in context, where they are
+provided with instructions and a few annotated examples. However, the
+effectiveness of in-context learning is dependent on the provided context, and
+the performance on a downstream task can vary considerably, depending on the
+instruction. Importantly, such dependency on the context can surface in
+unpredictable ways, e.g., a seemingly more informative instruction might lead
+to a worse performance. In this paper, we propose an alternative approach,
+which we term in-context probing. Similar to in-context learning, we
+contextualize the representation of the input with an instruction, but instead
+of decoding the output prediction, we probe the contextualized representation
+to predict the label. Through a series of experiments on a diverse set of
+classification tasks, we show that in-context probing is significantly more
+robust to changes in instructions. We further show that probing performs
+competitive or superior to finetuning and can be particularly helpful to build
+classifiers on top of smaller models, and with only a hundred training
+examples.
+"
+Coverage-based Example Selection for In-Context Learning,Shivanshu Gupta,http://arxiv.org/pdf/2305.14907v3.pdf,2023-05-24,['cs.cl'],2305.14907v3.pdf,"  In-context learning (ICL), the ability of large language models to perform
+novel tasks by conditioning on a prompt with a few task examples, requires
+these examples to be informative about the test instance. The standard approach
+of independently ranking and selecting the most similar examples selects
+redundant examples while omitting important information. In this work, we show
+that BERTScore-Recall (BSR) selects better examples that demonstrate more of
+the salient aspects, e.g. reasoning patterns, of the test input. We further
+extend BSR and many standard metrics to easily optimizable set-level metrics,
+giving still better coverage of those salient aspects. On 15 datasets spanning
+6 tasks and with 7 diverse LLMs, we show that (1) BSR is the superior metric
+for in-context example selection across the board, and (2) for compositional
+tasks, set selection using Set-BSR outperforms independent ranking by up to 17
+points on average and, despite being training-free, surpasses methods that
+leverage task or LLM-specific training.
+"
+Transformers learn to implement preconditioned gradient descent for  in-context learning,Kwangjun Ahn,http://arxiv.org/pdf/2306.00297v1.pdf,2023-06-01,"['cs.lg', 'cs.ai']",2306.00297v1.pdf,"  Motivated by the striking ability of transformers for in-context learning,
+several works demonstrate that transformers can implement algorithms like
+gradient descent. By a careful construction of weights, these works show that
+multiple layers of transformers are expressive enough to simulate gradient
+descent iterations. Going beyond the question of expressivity, we ask: Can
+transformers learn to implement such algorithms by training over random problem
+instances? To our knowledge, we make the first theoretical progress toward this
+question via analysis of the loss landscape for linear transformers trained
+over random instances of linear regression. For a single attention layer, we
+prove the global minimum of the training objective implements a single
+iteration of preconditioned gradient descent. Notably, the preconditioning
+matrix not only adapts to the input distribution but also to the variance
+induced by data inadequacy. For a transformer with $k$ attention layers, we
+prove certain critical points of the training objective implement $k$
+iterations of preconditioned gradient descent. Our results call for future
+theoretical studies on learning algorithms by training transformers.
+"
+In-Context Learning User Simulators for Task-Oriented Dialog Systems,Silvia Terragni,http://arxiv.org/pdf/2306.00774v1.pdf,2023-06-01,"['cs.cl', 'cs.lg']",2306.00774v1.pdf,"  This paper presents a novel application of large language models in user
+simulation for task-oriented dialog systems, specifically focusing on an
+in-context learning approach. By harnessing the power of these models, the
+proposed approach generates diverse utterances based on user goals and limited
+dialog examples. Unlike traditional simulators, this method eliminates the need
+for labor-intensive rule definition or extensive annotated data, making it more
+efficient and accessible. Additionally, an error analysis of the interaction
+between the user simulator and dialog system uncovers common mistakes,
+providing valuable insights into areas that require improvement. Our
+implementation is available at
+https://github.com/telepathylabsai/prompt-based-user-simulator.
+"
+Towards In-context Scene Understanding,Ivana Balažević,http://arxiv.org/pdf/2306.01667v2.pdf,2023-06-02,['cs.cv'],2306.01667v2.pdf,"  In-context learning$\unicode{x2013}$the ability to configure a model's
+behavior with different prompts$\unicode{x2013}$has revolutionized the field of
+natural language processing, alleviating the need for task-specific models and
+paving the way for generalist models capable of assisting with any query.
+Computer vision, in contrast, has largely stayed in the former regime:
+specialized decoders and finetuning protocols are generally required to perform
+dense tasks such as semantic segmentation and depth estimation. In this work we
+explore a simple mechanism for in-context learning of such scene understanding
+tasks: nearest neighbor retrieval from a prompt of annotated features. We
+propose a new pretraining protocol$\unicode{x2013}$leveraging attention within
+and across images$\unicode{x2013}$which yields representations particularly
+useful in this regime. The resulting Hummingbird model, suitably prompted,
+performs various scene understanding tasks without modification while
+approaching the performance of specialists that have been finetuned for each
+task. Moreover, Hummingbird can be configured to perform new tasks much more
+efficiently than finetuned models, raising the possibility of scene
+understanding in the interactive assistant regime.
+"
+Leveraging Large Language Models for Scalable Vector Graphics-Driven  Image Understanding,Mu Cai,http://arxiv.org/pdf/2306.06094v1.pdf,2023-06-09,"['cs.cv', 'cs.ai', 'cs.cl', 'cs.lg']",2306.06094v1.pdf,"  Recently, large language models (LLMs) have made significant advancements in
+natural language understanding and generation. However, their potential in
+computer vision remains largely unexplored. In this paper, we introduce a new,
+exploratory approach that enables LLMs to process images using the Scalable
+Vector Graphics (SVG) format. By leveraging the XML-based textual descriptions
+of SVG representations instead of raster images, we aim to bridge the gap
+between the visual and textual modalities, allowing LLMs to directly understand
+and manipulate images without the need for parameterized visual components. Our
+method facilitates simple image classification, generation, and in-context
+learning using only LLM capabilities. We demonstrate the promise of our
+approach across discriminative and generative tasks, highlighting its (i)
+robustness against distribution shift, (ii) substantial improvements achieved
+by tapping into the in-context learning abilities of LLMs, and (iii) image
+understanding and generation capabilities with human guidance. Our code, data,
+and models can be found here https://github.com/mu-cai/svg-llm.
+"
+Exploring the In-context Learning Ability of Large Language Model for  Biomedical Concept Linking,Qinyong Wang,http://arxiv.org/pdf/2307.01137v1.pdf,2023-07-03,"['cs.cl', 'cs.ai']",2307.01137v1.pdf,"  The biomedical field relies heavily on concept linking in various areas such
+as literature mining, graph alignment, information retrieval,
+question-answering, data, and knowledge integration. Although large language
+models (LLMs) have made significant strides in many natural language processing
+tasks, their effectiveness in biomedical concept mapping is yet to be fully
+explored. This research investigates a method that exploits the in-context
+learning (ICL) capabilities of large models for biomedical concept linking. The
+proposed approach adopts a two-stage retrieve-and-rank framework. Initially,
+biomedical concepts are embedded using language models, and then embedding
+similarity is utilized to retrieve the top candidates. These candidates'
+contextual information is subsequently incorporated into the prompt and
+processed by a large language model to re-rank the concepts. This approach
+achieved an accuracy of 90.% in BC5CDR disease entity normalization and 94.7%
+in chemical entity normalization, exhibiting a competitive performance relative
+to supervised learning methods. Further, it showed a significant improvement,
+with an over 20-point absolute increase in F1 score on an oncology matching
+dataset. Extensive qualitative assessments were conducted, and the benefits and
+potential shortcomings of using large language models within the biomedical
+domain were discussed. were discussed.
+"
+Learning to Retrieve In-Context Examples for Large Language Models,Liang Wang,http://arxiv.org/pdf/2307.07164v1.pdf,2023-07-14,"['cs.cl', 'cs.ir']",2307.07164v1.pdf,"  Large language models (LLMs) have demonstrated their ability to learn
+in-context, allowing them to perform various tasks based on a few input-output
+examples. However, the effectiveness of in-context learning is heavily reliant
+on the quality of the selected examples. In this paper, we propose a novel
+framework to iteratively train dense retrievers that can identify high-quality
+in-context examples for LLMs. Our framework initially trains a reward model
+based on LLM feedback to evaluate the quality of candidate examples, followed
+by knowledge distillation to train a bi-encoder based dense retriever. Our
+experiments on a suite of 30 tasks demonstrate that our framework significantly
+enhances in-context learning performance. Furthermore, we show the
+generalization ability of our framework to unseen tasks during training. An
+in-depth analysis reveals that our model improves performance by retrieving
+examples with similar patterns, and the gains are consistent across LLMs of
+varying sizes.
+"
+In-Context Learning Learns Label Relationships but Is Not Conventional  Learning,Jannik Kossen,http://arxiv.org/pdf/2307.12375v3.pdf,2023-07-23,"['cs.cl', 'cs.ai', 'cs.lg']",2307.12375v3.pdf,"  The predictions of Large Language Models (LLMs) on downstream tasks often
+improve significantly when including examples of the input--label relationship
+in the context. However, there is currently no consensus about how this
+in-context learning (ICL) ability of LLMs works. For example, while Xie et al.
+(2021) liken ICL to a general-purpose learning algorithm, Min et al. (2022)
+argue ICL does not even learn label relationships from in-context examples. In
+this paper, we provide novel insights into how ICL leverages label information,
+revealing both capabilities and limitations. To ensure we obtain a
+comprehensive picture of ICL behavior, we study probabilistic aspects of ICL
+predictions and thoroughly examine the dynamics of ICL as more examples are
+provided. Our experiments show that ICL predictions almost always depend on
+in-context labels, and that ICL can learn truly novel tasks in-context.
+However, we also find that ICL struggles to fully overcome prediction
+preferences acquired from pre-training data, and, further, that ICL does not
+consider all in-context information equally.
+"
+Investigating the Learning Behaviour of In-context Learning: A  Comparison with Supervised Learning,Xindi Wang,http://arxiv.org/pdf/2307.15411v2.pdf,2023-07-28,['cs.cl'],2307.15411v2.pdf,"  Large language models (LLMs) have shown remarkable capacity for in-context
+learning (ICL), where learning a new task from just a few training examples is
+done without being explicitly pre-trained. However, despite the success of
+LLMs, there has been little understanding of how ICL learns the knowledge from
+the given prompts. In this paper, to make progress toward understanding the
+learning behaviour of ICL, we train the same LLMs with the same demonstration
+examples via ICL and supervised learning (SL), respectively, and investigate
+their performance under label perturbations (i.e., noisy labels and label
+imbalance) on a range of classification tasks. First, via extensive
+experiments, we find that gold labels have significant impacts on the
+downstream in-context performance, especially for large language models;
+however, imbalanced labels matter little to ICL across all model sizes. Second,
+when comparing with SL, we show empirically that ICL is less sensitive to label
+perturbations than SL, and ICL gradually attains comparable performance to SL
+as the model size increases.
+"
+Exploring Automated Distractor and Feedback Generation for Math  Multiple-choice Questions via In-context Learning,Hunter McNichols,http://arxiv.org/pdf/2308.03234v1.pdf,2023-08-07,['cs.cl'],2308.03234v1.pdf,"  Multiple-choice questions (MCQs) are ubiquitous in almost all levels of
+education since they are easy to administer, grade, and are a reliable format
+in both assessments and practices. An important aspect of MCQs is the
+distractors, i.e., incorrect options that are designed to target specific
+misconceptions or insufficient knowledge among students. To date, the task of
+crafting high-quality distractors has largely remained a labor-intensive
+process for teachers and learning content designers, which has limited
+scalability. In this work, we explore the task of automated distractor and
+corresponding feedback message generation in math MCQs using large language
+models. We establish a formulation of these two tasks and propose a simple,
+in-context learning-based solution. Moreover, we explore using two non-standard
+metrics to evaluate the quality of the generated distractors and feedback
+messages. We conduct extensive experiments on these tasks using a real-world
+MCQ dataset that contains student response information. Our findings suggest
+that there is a lot of room for improvement in automated distractor and
+feedback generation. We also outline several directions for future work
+"
+CausalLM is not optimal for in-context learning,Nan Ding,http://arxiv.org/pdf/2308.06912v2.pdf,2023-08-14,"['cs.lg', 'cs.cl']",2308.06912v2.pdf,"  Recent empirical evidence indicates that transformer based in-context
+learning performs better when using a prefix language model (prefixLM), in
+which in-context samples can all attend to each other, compared to causal
+language models (causalLM), which use auto-regressive attention that prohibits
+in-context samples to attend to future samples. While this result is intuitive,
+it is not understood from a theoretical perspective. In this paper we take a
+theoretical approach and analyze the convergence behavior of prefixLM and
+causalLM under a certain parameter construction. Our analysis shows that both
+LM types converge to their stationary points at a linear rate, but that while
+prefixLM converges to the optimal solution of linear regression, causalLM
+convergence dynamics follows that of an online gradient descent algorithm,
+which is not guaranteed to be optimal even as the number of samples grows
+infinitely. We supplement our theoretical claims with empirical experiments
+over synthetic and real tasks and using various types of transformers. Our
+experiments verify that causalLM consistently underperforms prefixLM in all
+settings.
+"
+Exploring Demonstration Ensembling for In-context Learning,Muhammad Khalifa,http://arxiv.org/pdf/2308.08780v2.pdf,2023-08-17,"['cs.cl', 'cs.ai']",2308.08780v2.pdf,"  In-context learning (ICL) operates by showing language models (LMs) examples
+of input-output pairs for a given task, i.e., demonstrations. The standard
+approach for ICL is to prompt the LM with concatenated demonstrations followed
+by the test input. This approach suffers from some issues. First, concatenation
+offers almost no control over the contribution of each demo to the model
+prediction. This can be sub-optimal when some demonstrations are irrelevant to
+the test example. Second, due to the input length limit of some transformer
+models, it might be infeasible to fit many examples into the context,
+especially when dealing with long-input tasks. In this work, we explore
+Demonstration Ensembling (DENSE) as an alternative to simple concatenation.
+DENSE predicts outputs using subsets (i.e., buckets) of the demonstrations and
+then combines the output probabilities resulting from each subset to produce
+the final prediction. We study different ensembling methods using GPT-j and
+experiment on 12 language tasks. Our experiments show weighted max ensembling
+to outperform vanilla concatenation by as large as 2.4 average points. Code
+available at https://github.com/mukhal/icl-ensembling.
+"
+Context is Environment,Sharut Gupta,http://arxiv.org/pdf/2309.09888v2.pdf,2023-09-18,"['cs.lg', 'cs.ai', 'stat.ml']",2309.09888v2.pdf,"  Two lines of work are taking the central stage in AI research. On the one
+hand, the community is making increasing efforts to build models that discard
+spurious correlations and generalize better in novel test environments.
+Unfortunately, the bitter lesson so far is that no proposal convincingly
+outperforms a simple empirical risk minimization baseline. On the other hand,
+large language models (LLMs) have erupted as algorithms able to learn
+in-context, generalizing on-the-fly to eclectic contextual circumstances that
+users enforce by means of prompting. In this paper, we argue that context is
+environment, and posit that in-context learning holds the key to better domain
+generalization. Via extensive theory and experiments, we show that paying
+attention to context$\unicode{x2013}\unicode{x2013}$unlabeled examples as they
+arrive$\unicode{x2013}\unicode{x2013}$allows our proposed In-Context Risk
+Minimization (ICRM) algorithm to zoom-in on the test environment risk
+minimizer, leading to significant out-of-distribution performance improvements.
+From all of this, two messages are worth taking home. Researchers in domain
+generalization should consider environment as context, and harness the adaptive
+power of in-context learning. Researchers in LLMs should consider context as
+environment, to better structure data towards generalization.
+"
+"Prompt, Condition, and Generate: Classification of Unsupported Claims  with In-Context Learning",Peter Ebert Christensen,http://arxiv.org/pdf/2309.10359v1.pdf,2023-09-19,['cs.cl'],2309.10359v1.pdf,"  Unsupported and unfalsifiable claims we encounter in our daily lives can
+influence our view of the world. Characterizing, summarizing, and -- more
+generally -- making sense of such claims, however, can be challenging. In this
+work, we focus on fine-grained debate topics and formulate a new task of
+distilling, from such claims, a countable set of narratives. We present a
+crowdsourced dataset of 12 controversial topics, comprising more than 120k
+arguments, claims, and comments from heterogeneous sources, each annotated with
+a narrative label. We further investigate how large language models (LLMs) can
+be used to synthesise claims using In-Context Learning. We find that generated
+claims with supported evidence can be used to improve the performance of
+narrative classification models and, additionally, that the same model can
+infer the stance and aspect using a few training examples. Such a model can be
+useful in applications which rely on narratives , e.g. fact-checking.
+"
+In-Context Learning for Text Classification with Many Labels,Aristides Milios,http://arxiv.org/pdf/2309.10954v1.pdf,2023-09-19,"['cs.cl', 'cs.lg']",2309.10954v1.pdf,"  In-context learning (ICL) using large language models for tasks with many
+labels is challenging due to the limited context window, which makes it
+difficult to fit a sufficient number of examples in the prompt. In this paper,
+we use a pre-trained dense retrieval model to bypass this limitation, giving
+the model only a partial view of the full label space for each inference call.
+Testing with recent open-source LLMs (OPT, LLaMA), we set new state of the art
+performance in few-shot settings for three common intent classification
+datasets, with no finetuning. We also surpass fine-tuned performance on
+fine-grained sentiment classification in certain cases. We analyze the
+performance across number of in-context examples and different model scales,
+showing that larger models are necessary to effectively and consistently make
+use of larger context lengths for ICL. By running several ablations, we analyze
+the model's use of: a) the similarity of the in-context examples to the current
+input, b) the semantic content of the class names, and c) the correct
+correspondence between examples and labels. We demonstrate that all three are
+needed to varying degrees depending on the domain, contrary to certain recent
+works.
+"
+Privacy-Preserving In-Context Learning with Differentially Private  Few-Shot Generation,Xinyu Tang,http://arxiv.org/pdf/2309.11765v1.pdf,2023-09-21,"['cs.lg', 'cs.cr']",2309.11765v1.pdf,"  We study the problem of in-context learning (ICL) with large language models
+(LLMs) on private datasets. This scenario poses privacy risks, as LLMs may leak
+or regurgitate the private examples demonstrated in the prompt. We propose a
+novel algorithm that generates synthetic few-shot demonstrations from the
+private dataset with formal differential privacy (DP) guarantees, and show
+empirically that it can achieve effective ICL. We conduct extensive experiments
+on standard benchmarks and compare our algorithm with non-private ICL and
+zero-shot solutions. Our results demonstrate that our algorithm can achieve
+competitive performance with strong privacy levels. These results open up new
+possibilities for ICL with privacy protection for a broad range of
+applications.
+"
+HRoT: Hybrid prompt strategy and Retrieval of Thought for Table-Text  Hybrid Question Answering,Tongxu Luo,http://arxiv.org/pdf/2309.12669v1.pdf,2023-09-22,['cs.cl'],2309.12669v1.pdf,"  Answering numerical questions over hybrid contents from the given tables and
+text(TextTableQA) is a challenging task. Recently, Large Language Models (LLMs)
+have gained significant attention in the NLP community. With the emergence of
+large language models, In-Context Learning and Chain-of-Thought prompting have
+become two particularly popular research topics in this field. In this paper,
+we introduce a new prompting strategy called Hybrid prompt strategy and
+Retrieval of Thought for TextTableQA. Through In-Context Learning, we prompt
+the model to develop the ability of retrieval thinking when dealing with hybrid
+data. Our method achieves superior performance compared to the fully-supervised
+SOTA on the MultiHiertt dataset in the few-shot setting.
+"
+ALLURE: Auditing and Improving LLM-based Evaluation of Text using  Iterative In-Context-Learning,Hosein Hasanbeig,http://arxiv.org/pdf/2309.13701v2.pdf,2023-09-24,"['cs.cl', 'cs.ai', 'cs.hc']",2309.13701v2.pdf,"  From grading papers to summarizing medical documents, large language models
+(LLMs) are evermore used for evaluation of text generated by humans and AI
+alike. However, despite their extensive utility, LLMs exhibit distinct failure
+modes, necessitating a thorough audit and improvement of their text evaluation
+capabilities. Here we introduce ALLURE, a systematic approach to Auditing Large
+Language Models Understanding and Reasoning Errors. ALLURE involves comparing
+LLM-generated evaluations with annotated data, and iteratively incorporating
+instances of significant deviation into the evaluator, which leverages
+in-context learning (ICL) to enhance and improve robust evaluation of text by
+LLMs. Through this iterative process, we refine the performance of the
+evaluator LLM, ultimately reducing reliance on human annotators in the
+evaluation process. We anticipate ALLURE to serve diverse applications of LLMs
+in various domains related to evaluation of textual data, such as medical
+summarization, education, and and productivity.
+"
+Dynamic Demonstrations Controller for In-Context Learning,Fei Zhao,http://arxiv.org/pdf/2310.00385v1.pdf,2023-09-30,"['cs.cl', 'cs.ai']",2310.00385v1.pdf,"  In-Context Learning (ICL) is a new paradigm for natural language processing
+(NLP), where a large language model (LLM) observes a small number of
+demonstrations and a test instance as its input, and directly makes predictions
+without updating model parameters. Previous studies have revealed that ICL is
+sensitive to the selection and the ordering of demonstrations. However, there
+are few studies regarding the impact of the demonstration number on the ICL
+performance within a limited input length of LLM, because it is commonly
+believed that the number of demonstrations is positively correlated with model
+performance. In this paper, we found this conclusion does not always hold true.
+Through pilot experiments, we discover that increasing the number of
+demonstrations does not necessarily lead to improved performance. Building upon
+this insight, we propose a Dynamic Demonstrations Controller (D$^2$Controller),
+which can improve the ICL performance by adjusting the number of demonstrations
+dynamically. The experimental results show that D$^2$Controller yields a 5.4%
+relative improvement on eight different sizes of LLMs across ten datasets.
+Moreover, we also extend our method to previous ICL models and achieve
+competitive results.
+"
+The Cost of Down-Scaling Language Models: Fact Recall Deteriorates  before In-Context Learning,Tian Jin,http://arxiv.org/pdf/2310.04680v1.pdf,2023-10-07,"['cs.cl', 'cs.ai', 'cs.lg']",2310.04680v1.pdf,"  How does scaling the number of parameters in large language models (LLMs)
+affect their core capabilities? We study two natural scaling techniques --
+weight pruning and simply training a smaller or larger model, which we refer to
+as dense scaling -- and their effects on two core capabilities of LLMs: (a)
+recalling facts presented during pre-training and (b) processing information
+presented in-context during inference. By curating a suite of tasks that help
+disentangle these two capabilities, we find a striking difference in how these
+two abilities evolve due to scaling. Reducing the model size by more than 30\%
+(via either scaling approach) significantly decreases the ability to recall
+facts seen in pre-training. Yet, a 60--70\% reduction largely preserves the
+various ways the model can process in-context information, ranging from
+retrieving answers from a long context to learning parameterized functions from
+in-context exemplars. The fact that both dense scaling and weight pruning
+exhibit this behavior suggests that scaling model size has an inherently
+disparate effect on fact recall and in-context learning.
+"
+Not All Demonstration Examples are Equally Beneficial: Reweighting  Demonstration Examples for In-Context Learning,Zhe Yang,http://arxiv.org/pdf/2310.08309v1.pdf,2023-10-12,['cs.cl'],2310.08309v1.pdf,"  Large Language Models (LLMs) have recently gained the In-Context Learning
+(ICL) ability with the models scaling up, allowing them to quickly adapt to
+downstream tasks with only a few demonstration examples prepended in the input
+sequence. Nonetheless, the current practice of ICL treats all demonstration
+examples equally, which still warrants improvement, as the quality of examples
+is usually uneven. In this paper, we investigate how to determine approximately
+optimal weights for demonstration examples and how to apply them during ICL. To
+assess the quality of weights in the absence of additional validation data, we
+design a masked self-prediction (MSP) score that exhibits a strong correlation
+with the final ICL performance. To expedite the weight-searching process, we
+discretize the continuous weight space and adopt beam search. With
+approximately optimal weights obtained, we further propose two strategies to
+apply them to demonstrations at different model positions. Experimental results
+on 8 text classification tasks show that our approach outperforms conventional
+ICL by a large margin. Our code are publicly available at
+https:github.com/Zhe-Young/WICL.
+"
+How Many Pretraining Tasks Are Needed for In-Context Learning of Linear  Regression?,Jingfeng Wu,http://arxiv.org/pdf/2310.08391v1.pdf,2023-10-12,"['stat.ml', 'cs.lg']",2310.08391v1.pdf,"  Transformers pretrained on diverse tasks exhibit remarkable in-context
+learning (ICL) capabilities, enabling them to solve unseen tasks solely based
+on input contexts without adjusting model parameters. In this paper, we study
+ICL in one of its simplest setups: pretraining a linearly parameterized
+single-layer linear attention model for linear regression with a Gaussian
+prior. We establish a statistical task complexity bound for the attention model
+pretraining, showing that effective pretraining only requires a small number of
+independent tasks. Furthermore, we prove that the pretrained model closely
+matches the Bayes optimal algorithm, i.e., optimally tuned ridge regression, by
+achieving nearly Bayes optimal risk on unseen tasks under a fixed context
+length. These theoretical findings complement prior experimental research and
+shed light on the statistical foundations of ICL.
+"
+Generative Calibration for In-context Learning,Zhongtao Jiang,http://arxiv.org/pdf/2310.10266v1.pdf,2023-10-16,['cs.cl'],2310.10266v1.pdf,"  As one of the most exciting features of large language models (LLMs),
+in-context learning is a mixed blessing. While it allows users to
+fast-prototype a task solver with only a few training examples, the performance
+is generally sensitive to various configurations of the prompt such as the
+choice or order of the training examples. In this paper, we for the first time
+theoretically and empirically identify that such a paradox is mainly due to the
+label shift of the in-context model to the data distribution, in which LLMs
+shift the label marginal $p(y)$ while having a good label conditional $p(x|y)$.
+With this understanding, we can simply calibrate the in-context predictive
+distribution by adjusting the label marginal, which is estimated via
+Monte-Carlo sampling over the in-context model, i.e., generation of LLMs. We
+call our approach as generative calibration. We conduct exhaustive experiments
+with 12 text classification tasks and 12 LLMs scaling from 774M to 33B,
+generally find that the proposed method greatly and consistently outperforms
+the ICL as well as state-of-the-art calibration methods, by up to 27% absolute
+in macro-F1. Meanwhile, the proposed method is also stable under different
+prompt configurations.
+"
+"Last One Standing: A Comparative Analysis of Security and Privacy of  Soft Prompt Tuning, LoRA, and In-Context Learning",Rui Wen,http://arxiv.org/pdf/2310.11397v1.pdf,2023-10-17,"['cs.cr', 'cs.lg']",2310.11397v1.pdf,"  Large Language Models (LLMs) are powerful tools for natural language
+processing, enabling novel applications and user experiences. However, to
+achieve optimal performance, LLMs often require adaptation with private data,
+which poses privacy and security challenges. Several techniques have been
+proposed to adapt LLMs with private data, such as Low-Rank Adaptation (LoRA),
+Soft Prompt Tuning (SPT), and In-Context Learning (ICL), but their comparative
+privacy and security properties have not been systematically investigated. In
+this work, we fill this gap by evaluating the robustness of LoRA, SPT, and ICL
+against three types of well-established attacks: membership inference, which
+exposes data leakage (privacy); backdoor, which injects malicious behavior
+(security); and model stealing, which can violate intellectual property
+(privacy and security). Our results show that there is no silver bullet for
+privacy and security in LLM adaptation and each technique has different
+strengths and weaknesses.
+"
+MAGNIFICo: Evaluating the In-Context Learning Ability of Large Language  Models to Generalize to Novel Interpretations,Arkil Patel,http://arxiv.org/pdf/2310.11634v1.pdf,2023-10-18,['cs.cl'],2310.11634v1.pdf,"  Humans possess a remarkable ability to assign novel interpretations to
+linguistic expressions, enabling them to learn new words and understand
+community-specific connotations. However, Large Language Models (LLMs) have a
+knowledge cutoff and are costly to finetune repeatedly. Therefore, it is
+crucial for LLMs to learn novel interpretations in-context. In this paper, we
+systematically analyse the ability of LLMs to acquire novel interpretations
+using in-context learning. To facilitate our study, we introduce MAGNIFICo, an
+evaluation suite implemented within a text-to-SQL semantic parsing framework
+that incorporates diverse tokens and prompt settings to simulate real-world
+complexity. Experimental results on MAGNIFICo demonstrate that LLMs exhibit a
+surprisingly robust capacity for comprehending novel interpretations from
+natural language descriptions as well as from discussions within long
+conversations. Nevertheless, our findings also highlight the need for further
+improvements, particularly when interpreting unfamiliar words or when composing
+multiple novel interpretations simultaneously in the same example.
+Additionally, our analysis uncovers the semantic predispositions in LLMs and
+reveals the impact of recency bias for information presented in long contexts.
+"
+In-context Learning with Transformer Is Really Equivalent to a  Contrastive Learning Pattern,Ruifeng Ren,http://arxiv.org/pdf/2310.13220v1.pdf,2023-10-20,['cs.lg'],2310.13220v1.pdf,"  Pre-trained large language models based on Transformers have demonstrated
+amazing in-context learning (ICL) abilities. Given several demonstration
+examples, the models can implement new tasks without any parameter updates.
+However, it is still an open question to understand the mechanism of ICL. In
+this paper, we interpret the inference process of ICL as a gradient descent
+process in a contrastive learning pattern. Firstly, leveraging kernel methods,
+we establish the relationship between gradient descent and self-attention
+mechanism under generally used softmax attention setting instead of linear
+attention setting. Then, we analyze the corresponding gradient descent process
+of ICL from the perspective of contrastive learning without negative samples
+and discuss possible improvements of this contrastive learning pattern, based
+on which the self-attention layer can be further modified. Finally, we design
+experiments to support our opinions. To the best of our knowledge, our work is
+the first to provide the understanding of ICL from the perspective of
+contrastive learning and has the potential to facilitate future model design by
+referring to related works on contrastive learning.
+"
+In-Context Learning Creates Task Vectors,Roee Hendel,http://arxiv.org/pdf/2310.15916v1.pdf,2023-10-24,['cs.cl'],2310.15916v1.pdf,"  In-context learning (ICL) in Large Language Models (LLMs) has emerged as a
+powerful new learning paradigm. However, its underlying mechanism is still not
+well understood. In particular, it is challenging to map it to the ""standard""
+machine learning framework, where one uses a training set $S$ to find a
+best-fitting function $f(x)$ in some hypothesis class. Here we make progress on
+this problem by showing that the functions learned by ICL often have a very
+simple structure: they correspond to the transformer LLM whose only inputs are
+the query $x$ and a single ""task vector"" calculated from the training set.
+Thus, ICL can be seen as compressing $S$ into a single task vector
+$\boldsymbol{\theta}(S)$ and then using this task vector to modulate the
+transformer to produce the output. We support the above claim via comprehensive
+experiments across a range of models and tasks.
+"
+When Do Prompting and Prefix-Tuning Work? A Theory of Capabilities and  Limitations,Aleksandar Petrov,http://arxiv.org/pdf/2310.19698v1.pdf,2023-10-30,"['cs.lg', 'cs.cl']",2310.19698v1.pdf,"  Context-based fine-tuning methods, including prompting, in-context learning,
+soft prompting (also known as prompt tuning), and prefix-tuning, have gained
+popularity due to their ability to often match the performance of full
+fine-tuning with a fraction of the parameters. Despite their empirical
+successes, there is little theoretical understanding of how these techniques
+influence the internal computation of the model and their expressiveness
+limitations. We show that despite the continuous embedding space being more
+expressive than the discrete token space, soft-prompting and prefix-tuning are
+strictly less expressive than full fine-tuning, even with the same number of
+learnable parameters. Concretely, context-based fine-tuning cannot change the
+relative attention pattern over the content and can only bias the outputs of an
+attention layer in a fixed direction. This suggests that while techniques like
+prompting, in-context learning, soft prompting, and prefix-tuning can
+effectively elicit skills present in the pretrained model, they cannot learn
+novel tasks that require new attention patterns.
+"
+Which Examples to Annotate for In-Context Learning? Towards Effective  and Efficient Selection,Costas Mavromatis,http://arxiv.org/pdf/2310.20046v1.pdf,2023-10-30,['cs.cl'],2310.20046v1.pdf,"  Large Language Models (LLMs) can adapt to new tasks via in-context learning
+(ICL). ICL is efficient as it does not require any parameter updates to the
+trained LLM, but only few annotated examples as input for the LLM. In this
+work, we investigate an active learning approach for ICL, where there is a
+limited budget for annotating examples. We propose a model-adaptive
+optimization-free algorithm, termed AdaICL, which identifies examples that the
+model is uncertain about, and performs semantic diversity-based example
+selection. Diversity-based sampling improves overall effectiveness, while
+uncertainty sampling improves budget efficiency and helps the LLM learn new
+information. Moreover, AdaICL poses its sampling strategy as a Maximum Coverage
+problem, that dynamically adapts based on the model's feedback and can be
+approximately solved via greedy algorithms. Extensive experiments on nine
+datasets and seven LLMs show that AdaICL improves performance by 4.4% accuracy
+points over SOTA (7.7% relative improvement), is up to 3x more budget-efficient
+than performing annotations uniformly at random, while it outperforms SOTA with
+2x fewer ICL examples.
+"
+DAIL: Data Augmentation for In-Context Learning via Self-Paraphrase,Dawei Li,http://arxiv.org/pdf/2311.03319v1.pdf,2023-11-06,"['cs.cl', 'cs.ai']",2311.03319v1.pdf,"  In-Context Learning (ICL) combined with pre-trained large language models has
+achieved promising results on various NLP tasks. However, ICL requires
+high-quality annotated demonstrations which might not be available in
+real-world scenarios. To overcome this limitation, we propose \textbf{D}ata
+\textbf{A}ugmentation for \textbf{I}n-Context \textbf{L}earning
+(\textbf{DAIL}). DAIL leverages the intuition that large language models are
+more familiar with the content generated by themselves. It first utilizes the
+language model to generate paraphrases of the test sample and employs majority
+voting to determine the final result based on individual predictions. Our
+extensive empirical evaluation shows that DAIL outperforms the standard ICL
+method and other ensemble-based methods in the low-resource scenario.
+Additionally, we explore the use of voting consistency as a confidence score of
+the model when the logits of predictions are inaccessible. We believe our work
+will stimulate further research on ICL in low-resource settings.
+"
+In-Context Exemplars as Clues to Retrieving from Large Associative  Memory,Jiachen Zhao,http://arxiv.org/pdf/2311.03498v1.pdf,2023-11-06,"['cs.cl', 'cs.lg']",2311.03498v1.pdf,"  Recently, large language models (LLMs) have made remarkable progress in
+natural language processing. The most representative ability of LLMs is
+in-context learning (ICL), which enables LLMs to learn patterns from in-context
+exemplars without training. The performance of ICL greatly depends on the
+exemplars used. However, how to choose exemplars remains unclear due to the
+lack of understanding of how in-context learning works. In this paper, we
+present a novel perspective on ICL by conceptualizing it as contextual
+retrieval from a model of associative memory. We establish a theoretical
+framework of ICL based on Hopfield Networks. Based on our framework, we look
+into how in-context exemplars influence the performance of ICL and propose more
+efficient active exemplar selection. Our study sheds new light on the mechanism
+of ICL by connecting it to memory retrieval, with potential implications for
+advancing the understanding of LLMs.
+"
+Instruct Me More! Random Prompting for Visual In-Context Learning,Jiahao Zhang,http://arxiv.org/pdf/2311.03648v1.pdf,2023-11-07,['cs.cv'],2311.03648v1.pdf,"  Large-scale models trained on extensive datasets, have emerged as the
+preferred approach due to their high generalizability across various tasks.
+In-context learning (ICL), a popular strategy in natural language processing,
+uses such models for different tasks by providing instructive prompts but
+without updating model parameters. This idea is now being explored in computer
+vision, where an input-output image pair (called an in-context pair) is
+supplied to the model with a query image as a prompt to exemplify the desired
+output. The efficacy of visual ICL often depends on the quality of the prompts.
+We thus introduce a method coined Instruct Me More (InMeMo), which augments
+in-context pairs with a learnable perturbation (prompt), to explore its
+potential. Our experiments on mainstream tasks reveal that InMeMo surpasses the
+current state-of-the-art performance. Specifically, compared to the baseline
+without learnable prompt, InMeMo boosts mIoU scores by 7.35 and 15.13 for
+foreground segmentation and single object detection tasks, respectively. Our
+findings suggest that InMeMo offers a versatile and efficient way to enhance
+the performance of visual ICL with lightweight training. Code is available at
+https://github.com/Jackieam/InMeMo.
+"
+Selective Annotation Makes Language Models Better Few-Shot Learners,Hongjin Su,http://arxiv.org/pdf/2209.01975v1.pdf,2022-09-05,['cs.cl'],2209.01975v1.pdf,"  Many recent approaches to natural language tasks are built on the remarkable
+abilities of large language models. Large language models can perform
+in-context learning, where they learn a new task from a few task
+demonstrations, without any parameter updates. This work examines the
+implications of in-context learning for the creation of datasets for new
+natural language tasks. Departing from recent in-context learning methods, we
+formulate an annotation-efficient, two-step framework: selective annotation
+that chooses a pool of examples to annotate from unlabeled data in advance,
+followed by prompt retrieval that retrieves task examples from the annotated
+pool at test time. Based on this framework, we propose an unsupervised,
+graph-based selective annotation method, voke-k, to select diverse,
+representative examples to annotate. Extensive experiments on 10 datasets
+(covering classification, commonsense reasoning, dialogue, and text/code
+generation) demonstrate that our selective annotation method improves the task
+performance by a large margin. On average, vote-k achieves a 12.9%/11.4%
+relative gain under an annotation budget of 18/100, as compared to randomly
+selecting examples to annotate. Compared to state-of-the-art supervised
+finetuning approaches, it yields similar performance with 10-100x less
+annotation cost across 10 tasks. We further analyze the effectiveness of our
+framework in various scenarios: language models with varying sizes, alternative
+selective annotation methods, and cases where there is a test data domain
+shift. We hope that our studies will serve as a basis for data annotations as
+large language models are increasingly applied to new tasks. Our code is
+available at https://github.com/HKUNLP/icl-selective-annotation.
+"
+In-context Example Selection with Influences,Tai Nguyen,http://arxiv.org/pdf/2302.11042v2.pdf,2023-02-21,"['cs.cl', 'cs.lg']",2302.11042v2.pdf,"  In-context learning (ICL) is a powerful paradigm emerged from large language
+models (LLMs). Despite its promises, ICL performance is known to be highly
+sensitive to input examples. In this work, we use $\textit{in-context
+influences}$ to analyze few-shot ICL performance directly from the in-context
+examples. Our proposed influence-based example selection method can identify
+both positive and negative examples, outperforming several baselines when
+evaluated on 9 SuperGLUE tasks. Our analysis uncovers up to a $16.3\%$
+performance gap between using the most negative in-context examples compared to
+the most positive. In a case study, we apply our influence-based framework to
+quantify the phenomena of recency bias in example ordering for few-shot ICL.
+"
+In-Context Alignment: Chat with Vanilla Language Models Before  Fine-Tuning,Xiaochuang Han,http://arxiv.org/pdf/2308.04275v1.pdf,2023-08-08,"['cs.cl', 'cs.ai', 'cs.lg']",2308.04275v1.pdf,"  In this note, we explore inference-time alignment through in-context
+learning. We consider a vanilla pretrained language model Llama-2 before any
+fine-tuning and retrieve an average of 9 demonstration alignment examples when
+the model is prompted to follow chat-style instructions. Compared to direct
+prompting, the in-context alignment without changing model weights leads to a
+7x increase in win-rate w.r.t. the text-davinci-003 model from OpenAI, making
+the vanilla language model comparable to strong baselines with alignment
+fine-tuning.
+"
+"Tabular Representation, Noisy Operators, and Impacts on Table Structure  Understanding Tasks in LLMs",Ananya Singha,http://arxiv.org/pdf/2310.10358v1.pdf,2023-10-16,"['cs.cl', 'cs.ai']",2310.10358v1.pdf,"  Large language models (LLMs) are increasingly applied for tabular tasks using
+in-context learning. The prompt representation for a table may play a role in
+the LLMs ability to process the table. Inspired by prior work, we generate a
+collection of self-supervised structural tasks (e.g. navigate to a cell and
+row; transpose the table) and evaluate the performance differences when using 8
+formats. In contrast to past work, we introduce 8 noise operations inspired by
+real-world messy data and adversarial inputs, and show that such operations can
+impact LLM performance across formats for different structural understanding
+tasks.
+"
+GPT-4 Vision on Medical Image Classification -- A Case Study on COVID-19  Dataset,Ruibo Chen,http://arxiv.org/pdf/2310.18498v1.pdf,2023-10-27,"['eess.iv', 'cs.cv', 'cs.lg']",2310.18498v1.pdf,"  This technical report delves into the application of GPT-4 Vision (GPT-4V) in
+the nuanced realm of COVID-19 image classification, leveraging the
+transformative potential of in-context learning to enhance diagnostic
+processes.
+"
+Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than  In-Context Learning,Haokun Liu,http://arxiv.org/pdf/2205.05638v2.pdf,2022-05-11,"['cs.lg', 'cs.ai', 'cs.cl']",2205.05638v2.pdf,"  Few-shot in-context learning (ICL) enables pre-trained language models to
+perform a previously-unseen task without any gradient-based training by feeding
+a small number of training examples as part of the input. ICL incurs
+substantial computational, memory, and storage costs because it involves
+processing all of the training examples every time a prediction is made.
+Parameter-efficient fine-tuning (PEFT) (e.g. adapter modules, prompt tuning,
+sparse update methods, etc.) offers an alternative paradigm where a small set
+of parameters are trained to enable a model to perform the new task. In this
+paper, we rigorously compare few-shot ICL and PEFT and demonstrate that the
+latter offers better accuracy as well as dramatically lower computational
+costs. Along the way, we introduce a new PEFT method called (IA)$^3$ that
+scales activations by learned vectors, attaining stronger performance while
+only introducing a relatively tiny amount of new parameters. We also propose a
+simple recipe based on the T0 model called T-Few that can be applied to new
+tasks without task-specific tuning or modifications. We validate the
+effectiveness of T-Few on completely unseen tasks by applying it to the RAFT
+benchmark, attaining super-human performance for the first time and
+outperforming the state-of-the-art by 6% absolute. All of the code used in our
+experiments is publicly available.
+"
+Evaluating the Impact of Model Scale for Compositional Generalization in  Semantic Parsing,Linlu Qiu,http://arxiv.org/pdf/2205.12253v2.pdf,2022-05-24,['cs.cl'],2205.12253v2.pdf,"  Despite their strong performance on many tasks, pre-trained language models
+have been shown to struggle on out-of-distribution compositional
+generalization. Meanwhile, recent work has shown considerable improvements on
+many NLP tasks from model scaling. Can scaling up model size also improve
+compositional generalization in semantic parsing? We evaluate encoder-decoder
+models up to 11B parameters and decoder-only models up to 540B parameters, and
+compare model scaling curves for three different methods for applying a
+pre-trained language model to a new task: fine-tuning all parameters, prompt
+tuning, and in-context learning. We observe that fine-tuning generally has flat
+or negative scaling curves on out-of-distribution compositional generalization
+in semantic parsing evaluations. In-context learning has positive scaling
+curves, but is generally outperformed by much smaller fine-tuned models.
+Prompt-tuning can outperform fine-tuning, suggesting further potential
+improvements from scaling as it exhibits a more positive scaling curve.
+Additionally, we identify several error trends that vary with model scale. For
+example, larger models are generally better at modeling the syntax of the
+output space, but are also more prone to certain types of overfitting. Overall,
+our study highlights limitations of current techniques for effectively
+leveraging model scale for compositional generalization, while our analysis
+also suggests promising directions for future work.
+"
+Controllable Dialogue Simulation with In-Context Learning,Zekun Li,http://arxiv.org/pdf/2210.04185v4.pdf,2022-10-09,"['cs.cl', 'cs.ai']",2210.04185v4.pdf,"  Building dialogue systems requires a large corpus of annotated dialogues.
+Such datasets are usually created via crowdsourcing, which is expensive and
+time-consuming. In this paper, we propose \textsc{Dialogic}, a novel dialogue
+simulation method based on large language model in-context learning to automate
+dataset creation. Seeded with a few annotated dialogues, \textsc{Dialogic}
+automatically selects in-context examples for demonstration and prompts GPT-3
+to generate new dialogues and annotations in a controllable way. Our method can
+rapidly expand a small set of dialogue data with minimum or zero \textit{human
+involvement} and \textit{parameter update} and is thus much more cost-efficient
+and time-saving than crowdsourcing. Experimental results on the MultiWOZ
+dataset demonstrate that training a model on the simulated dialogues leads to
+even better performance than using the same amount of human-generated dialogues
+under the challenging low-resource settings, with as few as 85 dialogues as a
+seed. When enough data is available, our method can still serve as an effective
+data augmentation method. Human evaluation results also show that our simulated
+dialogues have near-human fluency and annotation accuracy. The code and data
+are available at \textbf{\url{https://github.com/Leezekun/dialogic}}.
+"
+XRICL: Cross-lingual Retrieval-Augmented In-Context Learning for  Cross-lingual Text-to-SQL Semantic Parsing,Peng Shi,http://arxiv.org/pdf/2210.13693v1.pdf,2022-10-25,['cs.cl'],2210.13693v1.pdf,"  In-context learning using large language models has recently shown surprising
+results for semantic parsing tasks such as Text-to-SQL translation. Prompting
+GPT-3 or Codex using several examples of question-SQL pairs can produce
+excellent results, comparable to state-of-the-art finetuning-based models.
+However, existing work primarily focuses on English datasets, and it is unknown
+whether large language models can serve as competitive semantic parsers for
+other languages. To bridge this gap, our work focuses on cross-lingual
+Text-to-SQL semantic parsing for translating non-English utterances into SQL
+queries based on an English schema. We consider a zero-shot transfer learning
+setting with the assumption that we do not have any labeled examples in the
+target language (but have annotated examples in English). This work introduces
+the XRICL framework, which learns to retrieve relevant English exemplars for a
+given query to construct prompts. We also include global translation exemplars
+for a target language to facilitate the translation process for large language
+models. To systematically evaluate our model, we construct two new benchmark
+datasets, XSpider and XKaggle-dbqa, which include questions in Chinese,
+Vietnamese, Farsi, and Hindi. Our experiments show that XRICL effectively
+leverages large pre-trained language models to outperform existing baselines.
+Data and code are publicly available at https://github.com/Impavidity/XRICL.
+"
+Images Speak in Images: A Generalist Painter for In-Context Visual  Learning,Xinlong Wang,http://arxiv.org/pdf/2212.02499v2.pdf,2022-12-05,['cs.cv'],2212.02499v2.pdf,"  In-context learning, as a new paradigm in NLP, allows the model to rapidly
+adapt to various tasks with only a handful of prompts and examples. But in
+computer vision, the difficulties for in-context learning lie in that tasks
+vary significantly in the output representations, thus it is unclear how to
+define the general-purpose task prompts that the vision model can understand
+and transfer to out-of-domain tasks. In this work, we present Painter, a
+generalist model which addresses these obstacles with an ""image""-centric
+solution, that is, to redefine the output of core vision tasks as images, and
+specify task prompts as also images. With this idea, our training process is
+extremely simple, which performs standard masked image modeling on the stitch
+of input and output image pairs. This makes the model capable of performing
+tasks conditioned on visible image patches. Thus, during inference, we can
+adopt a pair of input and output images from the same task as the input
+condition, to indicate which task to perform. Without bells and whistles, our
+generalist Painter can achieve competitive performance compared to
+well-established task-specific models, on seven representative vision tasks
+ranging from high-level visual understanding to low-level image processing. In
+addition, Painter significantly outperforms recent generalist models on several
+challenging tasks.
+"
+General-Purpose In-Context Learning by Meta-Learning Transformers,Louis Kirsch,http://arxiv.org/pdf/2212.04458v1.pdf,2022-12-08,"['cs.lg', 'cs.ai', 'cs.ne', 'stat.ml']",2212.04458v1.pdf,"  Modern machine learning requires system designers to specify aspects of the
+learning pipeline, such as losses, architectures, and optimizers.
+Meta-learning, or learning-to-learn, instead aims to learn those aspects, and
+promises to unlock greater capabilities with less manual effort. One
+particularly ambitious goal of meta-learning is to train general-purpose
+in-context learning algorithms from scratch, using only black-box models with
+minimal inductive bias. Such a model takes in training data, and produces
+test-set predictions across a wide range of problems, without any explicit
+definition of an inference model, training loss, or optimization algorithm. In
+this paper we show that Transformers and other black-box models can be
+meta-trained to act as general-purpose in-context learners. We characterize
+phase transitions between algorithms that generalize, algorithms that memorize,
+and algorithms that fail to meta-train at all, induced by changes in model
+size, number of tasks, and meta-optimization. We further show that the
+capabilities of meta-trained algorithms are bottlenecked by the accessible
+state size (memory) determining the next prediction, unlike standard models
+which are thought to be bottlenecked by parameter count. Finally, we propose
+practical interventions such as biasing the training distribution that improve
+the meta-training and meta-generalization of general-purpose learning
+algorithms.
+"
+Demonstrate-Search-Predict: Composing retrieval and language models for  knowledge-intensive NLP,Omar Khattab,http://arxiv.org/pdf/2212.14024v2.pdf,2022-12-28,"['cs.cl', 'cs.ir']",2212.14024v2.pdf,"  Retrieval-augmented in-context learning has emerged as a powerful approach
+for addressing knowledge-intensive tasks using frozen language models (LM) and
+retrieval models (RM). Existing work has combined these in simple
+""retrieve-then-read"" pipelines in which the RM retrieves passages that are
+inserted into the LM prompt. To begin to fully realize the potential of frozen
+LMs and RMs, we propose Demonstrate-Search-Predict (DSP), a framework that
+relies on passing natural language texts in sophisticated pipelines between an
+LM and an RM. DSP can express high-level programs that bootstrap pipeline-aware
+demonstrations, search for relevant passages, and generate grounded
+predictions, systematically breaking down problems into small transformations
+that the LM and RM can handle more reliably. We have written novel DSP programs
+for answering questions in open-domain, multi-hop, and conversational settings,
+establishing in early evaluations new state-of-the-art in-context learning
+results and delivering 37-120%, 8-39%, and 80-290% relative gains against the
+vanilla LM (GPT-3.5), a standard retrieve-then-read pipeline, and a
+contemporaneous self-ask pipeline, respectively. We release DSP at
+https://github.com/stanfordnlp/dsp
+"
+How Does In-Context Learning Help Prompt Tuning?,Simeng Sun,http://arxiv.org/pdf/2302.11521v1.pdf,2023-02-22,['cs.cl'],2302.11521v1.pdf,"  Fine-tuning large language models is becoming ever more impractical due to
+their rapidly-growing scale. This motivates the use of parameter-efficient
+adaptation methods such as prompt tuning (PT), which adds a small number of
+tunable embeddings to an otherwise frozen model, and in-context learning (ICL),
+in which demonstrations of the task are provided to the model in natural
+language without any additional training. Recently, Singhal et al. (2022)
+propose ``instruction prompt tuning'' (IPT), which combines PT with ICL by
+concatenating a natural language demonstration with learned prompt embeddings.
+While all of these methods have proven effective on different tasks, how they
+interact with each other remains unexplored. In this paper, we empirically
+study when and how in-context examples improve prompt tuning by measuring the
+effectiveness of ICL, PT, and IPT on five text generation tasks with multiple
+base language models. We observe that (1) IPT does \emph{not} always outperform
+PT, and in fact requires the in-context demonstration to be semantically
+similar to the test input to yield improvements; (2) PT is unstable and
+exhibits high variance, but combining PT and ICL (into IPT) consistently
+reduces variance across all five tasks; and (3) prompts learned for a specific
+source task via PT exhibit positive transfer when paired with in-context
+examples of a different target task. Our results offer actionable insights on
+choosing a suitable parameter-efficient adaptation method for a given task.
+"
+Larger language models do in-context learning differently,Jerry Wei,http://arxiv.org/pdf/2303.03846v2.pdf,2023-03-07,['cs.cl'],2303.03846v2.pdf,"  We study how in-context learning (ICL) in language models is affected by
+semantic priors versus input-label mappings. We investigate two setups-ICL with
+flipped labels and ICL with semantically-unrelated labels-across various model
+families (GPT-3, InstructGPT, Codex, PaLM, and Flan-PaLM). First, experiments
+on ICL with flipped labels show that overriding semantic priors is an emergent
+ability of model scale. While small language models ignore flipped labels
+presented in-context and thus rely primarily on semantic priors from
+pretraining, large models can override semantic priors when presented with
+in-context exemplars that contradict priors, despite the stronger semantic
+priors that larger models may hold. We next study semantically-unrelated label
+ICL (SUL-ICL), in which labels are semantically unrelated to their inputs
+(e.g., foo/bar instead of negative/positive), thereby forcing language models
+to learn the input-label mappings shown in in-context exemplars in order to
+perform the task. The ability to do SUL-ICL also emerges primarily with scale,
+and large-enough language models can even perform linear classification in a
+SUL-ICL setting. Finally, we evaluate instruction-tuned models and find that
+instruction tuning strengthens both the use of semantic priors and the capacity
+to learn input-label mappings, but more of the former.
+"
+How Many Demonstrations Do You Need for In-context Learning?,Jiuhai Chen,http://arxiv.org/pdf/2303.08119v3.pdf,2023-03-14,['cs.ai'],2303.08119v3.pdf,"  Large language models (LLMs) are capable to perform complex reasoning by
+in-context learning (ICL) when provided with a few input-output demonstrations
+(demos) and more powerful when intermediate reasoning steps (""chain of thoughts
+(CoT)"") of the demos are given. Is it necessary to use multi-demo in ICL? In
+this paper, we study ICL using fewer demos for each test query on the tasks
+in~\cite{wei2022chain}. Surprisingly, we do not observe significant degradation
+when using only one randomly chosen demo. To study this phenomenon, for each
+test query, we categorize demos into ""correct demos"" leading to the correct
+answer, and ""wrong demos"" resulting in wrong answers. Our analysis reveals an
+inherent bias in those widely studied datasets: most demos are correct for a
+majority of test queries, which explains the good performance of using one
+random demo. Moreover, ICL (with and w/o CoT) using only one correct demo
+significantly outperforms all-demo ICL adopted by most previous works,
+indicating the weakness of LLMs in finding correct demo(s) for input queries,
+which is difficult to evaluate on the biased datasets. Furthermore, we observe
+a counterintuitive behavior of ICL using multi-demo, i.e., its accuracy
+degrades(improves) when given more correct(wrong) demos. This implies that ICL
+can be easily misguided by interference among demos and their spurious
+correlations. Our analyses highlight several fundamental challenges that need
+to be addressed in LLMs training, ICL, and benchmark design.
+"
+Improving Visual Question Answering Models through Robustness Analysis  and In-Context Learning with a Chain of Basic Questions,Jia-Hong Huang,http://arxiv.org/pdf/2304.03147v1.pdf,2023-04-06,"['cs.cv', 'cs.ai']",2304.03147v1.pdf,"  Deep neural networks have been critical in the task of Visual Question
+Answering (VQA), with research traditionally focused on improving model
+accuracy. Recently, however, there has been a trend towards evaluating the
+robustness of these models against adversarial attacks. This involves assessing
+the accuracy of VQA models under increasing levels of noise in the input, which
+can target either the image or the proposed query question, dubbed the main
+question. However, there is currently a lack of proper analysis of this aspect
+of VQA. This work proposes a new method that utilizes semantically related
+questions, referred to as basic questions, acting as noise to evaluate the
+robustness of VQA models. It is hypothesized that as the similarity of a basic
+question to the main question decreases, the level of noise increases. To
+generate a reasonable noise level for a given main question, a pool of basic
+questions is ranked based on their similarity to the main question, and this
+ranking problem is cast as a LASSO optimization problem. Additionally, this
+work proposes a novel robustness measure, R_score, and two basic question
+datasets to standardize the analysis of VQA model robustness. The experimental
+results demonstrate that the proposed evaluation method effectively analyzes
+the robustness of VQA models. Moreover, the experiments show that in-context
+learning with a chain of basic questions can enhance model accuracy.
+"
+GeneGPT: Augmenting Large Language Models with Domain Tools for Improved  Access to Biomedical Information,Qiao Jin,http://arxiv.org/pdf/2304.09667v3.pdf,2023-04-19,"['cs.cl', 'cs.ai', 'q-bio.gn']",2304.09667v3.pdf,"  While large language models (LLMs) have been successfully applied to various
+tasks, they still face challenges with hallucinations. Augmenting LLMs with
+domain-specific tools such as database utilities can facilitate easier and more
+precise access to specialized knowledge. In this paper, we present GeneGPT, a
+novel method for teaching LLMs to use the Web APIs of the National Center for
+Biotechnology Information (NCBI) for answering genomics questions.
+Specifically, we prompt Codex to solve the GeneTuring tests with NCBI Web APIs
+by in-context learning and an augmented decoding algorithm that can detect and
+execute API calls. Experimental results show that GeneGPT achieves
+state-of-the-art performance on eight tasks in the GeneTuring benchmark with an
+average score of 0.83, largely surpassing retrieval-augmented LLMs such as the
+new Bing (0.44), biomedical LLMs such as BioMedLM (0.08) and BioGPT (0.04), as
+well as GPT-3 (0.16) and ChatGPT (0.12). Our further analyses suggest that: (1)
+API demonstrations have good cross-task generalizability and are more useful
+than documentations for in-context learning; (2) GeneGPT can generalize to
+longer chains of API calls and answer multi-hop questions in GeneHop, a novel
+dataset introduced in this work; (3) Different types of errors are enriched in
+different tasks, providing valuable insights for future improvements.
+"
+DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with  Self-Correction,Mohammadreza Pourreza,http://arxiv.org/pdf/2304.11015v3.pdf,2023-04-21,"['cs.cl', 'cs.ai', 'cs.db', 'cs.hc']",2304.11015v3.pdf,"  There is currently a significant gap between the performance of fine-tuned
+models and prompting approaches using Large Language Models (LLMs) on the
+challenging task of text-to-SQL, as evaluated on datasets such as Spider. To
+improve the performance of LLMs in the reasoning process, we study how
+decomposing the task into smaller sub-tasks can be effective. In particular, we
+show that breaking down the generation problem into sub-problems and feeding
+the solutions of those sub-problems into LLMs can be an effective approach for
+significantly improving their performance. Our experiments with three LLMs show
+that this approach consistently improves their simple few-shot performance by
+roughly 10%, pushing the accuracy of LLMs towards SOTA or surpassing it. On the
+holdout test set of Spider, the SOTA, in terms of execution accuracy, was 79.9
+and the new SOTA at the time of this writing using our approach is 85.3. Our
+approach with in-context learning beats many heavily fine-tuned models by at
+least 5%. Additionally, when evaluated on the BIRD benchmark, our approach
+achieved an execution accuracy of 55.9%, setting a new SOTA on its holdout test
+set.
+"
+Few-shot In-context Learning for Knowledge Base Question Answering,Tianle Li,http://arxiv.org/pdf/2305.01750v2.pdf,2023-05-02,"['cs.cl', 'cs.ai']",2305.01750v2.pdf,"  Question answering over knowledge bases is considered a difficult problem due
+to the challenge of generalizing to a wide variety of possible natural language
+questions. Additionally, the heterogeneity of knowledge base schema items
+between different knowledge bases often necessitates specialized training for
+different knowledge base question-answering (KBQA) datasets. To handle
+questions over diverse KBQA datasets with a unified training-free framework, we
+propose KB-BINDER, which for the first time enables few-shot in-context
+learning over KBQA tasks. Firstly, KB-BINDER leverages large language models
+like Codex to generate logical forms as the draft for a specific question by
+imitating a few demonstrations. Secondly, KB-BINDER grounds on the knowledge
+base to bind the generated draft to an executable one with BM25 score matching.
+The experimental results on four public heterogeneous KBQA datasets show that
+KB-BINDER can achieve a strong performance with only a few in-context
+demonstrations. Especially on GraphQA and 3-hop MetaQA, KB-BINDER can even
+outperform the state-of-the-art trained models. On GrailQA and WebQSP, our
+model is also on par with other fully-trained models. We believe KB-BINDER can
+serve as an important baseline for future research. Our code is available at
+https://github.com/ltl3A87/KB-BINDER.
+"
+How Do In-Context Examples Affect Compositional Generalization?,Shengnan An,http://arxiv.org/pdf/2305.04835v3.pdf,2023-05-08,"['cs.cl', 'cs.ai']",2305.04835v3.pdf,"  Compositional generalization--understanding unseen combinations of seen
+primitives--is an essential reasoning capability in human intelligence. The AI
+community mainly studies this capability by fine-tuning neural networks on lots
+of training samples, while it is still unclear whether and how in-context
+learning--the prevailing few-shot paradigm based on large language
+models--exhibits compositional generalization. In this paper, we present CoFe,
+a test suite to investigate in-context compositional generalization. We find
+that the compositional generalization performance can be easily affected by the
+selection of in-context examples, thus raising the research question what the
+key factors are to make good in-context examples for compositional
+generalization. We study three potential factors: similarity, diversity and
+complexity. Our systematic experiments indicate that in-context examples should
+be structurally similar to the test case, diverse from each other, and
+individually simple. Furthermore, two strong limitations are observed:
+in-context compositional generalization on fictional words is much weaker than
+that on commonly used ones; it is still critical that the in-context examples
+should cover required linguistic structures, even though the backbone model has
+been pre-trained on large corpus. We hope our analysis would facilitate the
+understanding and utilization of in-context learning paradigm.
+"
+Symbol tuning improves in-context learning in language models,Jerry Wei,http://arxiv.org/pdf/2305.08298v1.pdf,2023-05-15,['cs.cl'],2305.08298v1.pdf,"  We present symbol tuning - finetuning language models on in-context
+input-label pairs where natural language labels (e.g., ""positive/negative
+sentiment"") are replaced with arbitrary symbols (e.g., ""foo/bar""). Symbol
+tuning leverages the intuition that when a model cannot use instructions or
+natural language labels to figure out a task, it must instead do so by learning
+the input-label mappings.
+  We experiment with symbol tuning across Flan-PaLM models up to 540B
+parameters and observe benefits across various settings. First, symbol tuning
+boosts performance on unseen in-context learning tasks and is much more robust
+to underspecified prompts, such as those without instructions or without
+natural language labels. Second, symbol-tuned models are much stronger at
+algorithmic reasoning tasks, with up to 18.2% better performance on the List
+Functions benchmark and up to 15.3% better performance on the Simple Turing
+Concepts benchmark. Finally, symbol-tuned models show large improvements in
+following flipped-labels presented in-context, meaning that they are more
+capable of using in-context information to override prior semantic knowledge.
+"
+Text Classification via Large Language Models,Xiaofei Sun,http://arxiv.org/pdf/2305.08377v3.pdf,2023-05-15,['cs.cl'],2305.08377v3.pdf,"  Despite the remarkable success of large-scale Language Models (LLMs) such as
+GPT-3, their performances still significantly underperform fine-tuned models in
+the task of text classification. This is due to (1) the lack of reasoning
+ability in addressing complex linguistic phenomena (e.g., intensification,
+contrast, irony etc); (2) limited number of tokens allowed in in-context
+learning.
+  In this paper, we introduce Clue And Reasoning Prompting (CARP). CARP adopts
+a progressive reasoning strategy tailored to addressing the complex linguistic
+phenomena involved in text classification: CARP first prompts LLMs to find
+superficial clues (e.g., keywords, tones, semantic relations, references, etc),
+based on which a diagnostic reasoning process is induced for final decisions.
+To further address the limited-token issue, CARP uses a fine-tuned model on the
+supervised dataset for $k$NN demonstration search in the in-context learning,
+allowing the model to take the advantage of both LLM's generalization ability
+and the task-specific evidence provided by the full labeled dataset.
+Remarkably, CARP yields new SOTA performances on 4 out of 5 widely-used
+text-classification benchmarks, 97.39 (+1.24) on SST-2, 96.40 (+0.72) on
+AGNews, 98.78 (+0.25) on R8 and 96.95 (+0.6) on R52, and a performance
+comparable to SOTA on MR (92.39 v.s. 93.3). More importantly, we find that CARP
+delivers impressive abilities on low-resource and domain-adaptation setups.
+Specifically, using 16 examples per class, CARP achieves comparable
+performances to supervised models with 1,024 examples per class.
+"
+Exploring In-Context Learning Capabilities of Foundation Models for  Generating Knowledge Graphs from Text,Hanieh Khorashadizadeh,http://arxiv.org/pdf/2305.08804v1.pdf,2023-05-15,['cs.cl'],2305.08804v1.pdf,"  Knowledge graphs can represent information about the real-world using
+entities and their relations in a structured and semantically rich manner and
+they enable a variety of downstream applications such as question-answering,
+recommendation systems, semantic search, and advanced analytics. However, at
+the moment, building a knowledge graph involves a lot of manual effort and thus
+hinders their application in some situations and the automation of this process
+might benefit especially for small organizations. Automatically generating
+structured knowledge graphs from a large volume of natural language is still a
+challenging task and the research on sub-tasks such as named entity extraction,
+relation extraction, entity and relation linking, and knowledge graph
+construction aims to improve the state of the art of automatic construction and
+completion of knowledge graphs from text. The recent advancement of foundation
+models with billions of parameters trained in a self-supervised manner with
+large volumes of training data that can be adapted to a variety of downstream
+tasks has helped to demonstrate high performance on a large range of Natural
+Language Processing (NLP) tasks. In this context, one emerging paradigm is
+in-context learning where a language model is used as it is with a prompt that
+provides instructions and some examples to perform a task without changing the
+parameters of the model using traditional approaches such as fine-tuning. This
+way, no computing resources are needed for re-training/fine-tuning the models
+and the engineering effort is minimal. Thus, it would be beneficial to utilize
+such capabilities for generating knowledge graphs from text.
+"
+"What In-Context Learning ""Learns"" In-Context: Disentangling Task  Recognition and Task Learning",Jane Pan,http://arxiv.org/pdf/2305.09731v1.pdf,2023-05-16,"['cs.cl', 'cs.lg']",2305.09731v1.pdf,"  Large language models (LLMs) exploit in-context learning (ICL) to solve tasks
+with only a few demonstrations, but its mechanisms are not yet well-understood.
+Some works suggest that LLMs only recall already learned concepts from
+pre-training, while others hint that ICL performs implicit learning over
+demonstrations. We characterize two ways through which ICL leverages
+demonstrations. Task recognition (TR) captures the extent to which LLMs can
+recognize a task through demonstrations -- even without ground-truth labels --
+and apply their pre-trained priors, whereas task learning (TL) is the ability
+to capture new input-label mappings unseen in pre-training. Using a wide range
+of classification datasets and three LLM families (GPT-3, LLaMA and OPT), we
+design controlled experiments to disentangle the roles of TR and TL in ICL. We
+show that (1) models can achieve non-trivial performance with only TR, and TR
+does not further improve with larger models or more demonstrations; (2) LLMs
+acquire TL as the model scales, and TL's performance consistently improves with
+more demonstrations in context. Our findings unravel two different forces
+behind ICL and we advocate for discriminating them in future ICL research due
+to their distinct nature.
+"
+Temporal Knowledge Graph Forecasting Without Knowledge Using In-Context  Learning,Dong-Ho Lee,http://arxiv.org/pdf/2305.10613v3.pdf,2023-05-17,['cs.cl'],2305.10613v3.pdf,"  Temporal knowledge graph (TKG) forecasting benchmarks challenge models to
+predict future facts using knowledge of past facts. In this paper, we apply
+large language models (LLMs) to these benchmarks using in-context learning
+(ICL). We investigate whether and to what extent LLMs can be used for TKG
+forecasting, especially without any fine-tuning or explicit modules for
+capturing structural and temporal information. For our experiments, we present
+a framework that converts relevant historical facts into prompts and generates
+ranked predictions using token probabilities. Surprisingly, we observe that
+LLMs, out-of-the-box, perform on par with state-of-the-art TKG models carefully
+designed and trained for TKG forecasting. Our extensive evaluation presents
+performances across several models and datasets with different characteristics,
+compares alternative heuristics for preparing contextual information, and
+contrasts to prominent TKG methods and simple frequency and recency baselines.
+We also discover that using numerical indices instead of entity/relation names,
+i.e., hiding semantic information, does not significantly affect the
+performance ($\pm$0.4\% Hit@1). This shows that prior semantic knowledge is
+unnecessary; instead, LLMs can leverage the existing patterns in the context to
+achieve such performance. Our analysis also reveals that ICL enables LLMs to
+learn irregular patterns from the historical context, going beyond simple
+predictions based on common or recent information.
+"
+Learning In-context Learning for Named Entity Recognition,Jiawei Chen,http://arxiv.org/pdf/2305.11038v3.pdf,2023-05-18,['cs.cl'],2305.11038v3.pdf,"  Named entity recognition in real-world applications suffers from the
+diversity of entity types, the emergence of new entity types, and the lack of
+high-quality annotations. To address the above problems, this paper proposes an
+in-context learning-based NER approach, which can effectively inject in-context
+NER ability into PLMs and recognize entities of novel types on-the-fly using
+only a few demonstrative instances. Specifically, we model PLMs as a
+meta-function $\mathcal{ \lambda_ {\text{instruction, demonstrations, text}}.
+M}$, and a new entity extractor can be implicitly constructed by applying new
+instruction and demonstrations to PLMs, i.e., $\mathcal{ (\lambda . M)
+}$(instruction, demonstrations) $\to$ $\mathcal{F}$ where $\mathcal{F}$ will be
+a new entity extractor, i.e., $\mathcal{F}$: text $\to$ entities. To inject the
+above in-context NER ability into PLMs, we propose a meta-function pre-training
+algorithm, which pre-trains PLMs by comparing the (instruction,
+demonstration)-initialized extractor with a surrogate golden extractor.
+Experimental results on 4 few-shot NER datasets show that our method can
+effectively inject in-context NER ability into PLMs and significantly
+outperforms the PLMs+fine-tuning counterparts.
+"
+PlugMed: Improving Specificity in Patient-Centered Medical Dialogue  Generation using In-Context Learning,Chengfeng Dou,http://arxiv.org/pdf/2305.11508v2.pdf,2023-05-19,"['cs.cl', 'cs.ai', 'i.2.7']",2305.11508v2.pdf,"  The patient-centered medical dialogue systems strive to offer diagnostic
+interpretation services to users who are less knowledgeable about medical
+knowledge, through emphasizing the importance of providing responses specific
+to the patients. It is difficult for the large language models (LLMs) to
+guarantee the specificity of responses in spite of its promising performance
+even in some tasks in medical field. Inspired by in-context learning, we
+propose PlugMed, a Plug-and-Play Medical Dialogue System, for addressing this
+challenge. PlugMed is equipped with two modules, the prompt generation (PG)
+module and the response ranking (RR) module, to enhances LLMs' dialogue
+strategies for improving the specificity of the dialogue. The PG module is
+designed to stimulate the imitative ability of LLMs by providing them with real
+dialogues from similar patients as prompts. The RR module incorporates
+fine-tuned small model as response filter to enable the selection of
+appropriate responses generated by LLMs. Furthermore, we introduce a new
+evaluation method based on matching both user's intent and high-frequency
+medical term to effectively assess the specificity of the responses. We conduct
+experimental evaluations on three medical dialogue datasets, and the results,
+including both automatic and human evaluation, demonstrate the effectiveness of
+our approach.
+"
+ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via  Tool Embeddings,Shibo Hao,http://arxiv.org/pdf/2305.11554v3.pdf,2023-05-19,"['cs.cl', 'cs.lg']",2305.11554v3.pdf,"  Augmenting large language models (LLMs) with external tools has emerged as a
+promising approach to solving complex problems. However, traditional methods,
+which finetune LLMs with tool demonstration data, can be both costly and
+restricted to a predefined set of tools. Recent in-context learning paradigm
+alleviates these issues, but the limited context length only allows for a few
+shots of demonstrations, leading to suboptimal understandings of the tools.
+Moreover, when there are numerous tools to choose from, in-context learning
+could completely fail to work. In this paper, we propose an alternative
+approach, $\textbf{ToolkenGPT}$, which combines the benefits of both sides. Our
+approach represents each $\underline{tool}$ as a to$\underline{ken}$
+($\textit{toolken}$) and learns an embedding for it, enabling tool calls in the
+same way as generating a regular word token. Once a toolken is triggered, the
+LLM is prompted to complete arguments for the tool to execute. ToolkenGPT
+offers the flexibility to plug in an arbitrary number of tools by expanding the
+set of toolkens on the fly. In addition, it improves tool use by allowing
+extensive demonstration data for learning the toolken embeddings. In diverse
+domains, including numerical reasoning, knowledge-based question answering, and
+embodied plan generation, our approach effectively augments LLMs with tools and
+substantially outperforms various latest baselines. ToolkenGPT demonstrates the
+promising ability to use relevant tools from a large tool set in complex
+scenarios.
+"
+Iterative Forward Tuning Boosts In-context Learning in Language Models,Jiaxi Yang,http://arxiv.org/pdf/2305.13016v2.pdf,2023-05-22,['cs.cl'],2305.13016v2.pdf,"  Large language models (LLMs) have exhibited an emergent in-context learning
+(ICL) ability. However, the ICL models that can solve ordinary cases are hardly
+extended to solve more complex tasks by processing the demonstration examples
+once. This single-turn ICL is incoordinate with the decision making process of
+humans by learning from analogy. In this paper, we propose an effective and
+efficient two-stage framework to boost ICL in LLMs by exploiting a dual form
+between Transformer attention and gradient descent-based optimization.
+Concretely, we divide the ICL process into ""Deep-Thinking"" and inference
+stages. The ""Deep-Thinking"" stage performs iterative forward optimization of
+demonstrations, which is expected to boost the reasoning abilities of LLMs at
+test time by ""thinking"" demonstrations multiple times. It produces accumulated
+meta-gradients by manipulating the Key-Value matrices in the self-attention
+modules of the Transformer. Then, the inference stage only takes the test query
+as input without concatenating demonstrations and applies the learned
+meta-gradients through attention for output prediction. In this way,
+demonstrations are not required during the inference stage since they are
+already learned and stored in the definitive meta-gradients. LLMs can be
+effectively and efficiently adapted to downstream tasks. Extensive experiments
+on ten classification and multiple-choice datasets show that our method
+achieves substantially better performance than standard ICL in terms of both
+accuracy and efficiency.
+"
+Measuring Inductive Biases of In-Context Learning with Underspecified  Demonstrations,Chenglei Si,http://arxiv.org/pdf/2305.13299v1.pdf,2023-05-22,"['cs.cl', 'cs.ai', 'cs.lg']",2305.13299v1.pdf,"  In-context learning (ICL) is an important paradigm for adapting large
+language models (LLMs) to new tasks, but the generalization behavior of ICL
+remains poorly understood. We investigate the inductive biases of ICL from the
+perspective of feature bias: which feature ICL is more likely to use given a
+set of underspecified demonstrations in which two features are equally
+predictive of the labels. First, we characterize the feature biases of GPT-3
+models by constructing underspecified demonstrations from a range of NLP
+datasets and feature combinations. We find that LLMs exhibit clear feature
+biases - for example, demonstrating a strong bias to predict labels according
+to sentiment rather than shallow lexical features, like punctuation. Second, we
+evaluate the effect of different interventions that are designed to impose an
+inductive bias in favor of a particular feature, such as adding a natural
+language instruction or using semantically relevant label words. We find that,
+while many interventions can influence the learner to prefer a particular
+feature, it can be difficult to overcome strong prior biases. Overall, our
+results provide a broader picture of the types of features that ICL may be more
+likely to exploit and how to impose inductive biases that are better aligned
+with the intended task.
+"
+Exploring Diverse In-Context Configurations for Image Captioning,Xu Yang,http://arxiv.org/pdf/2305.14800v5.pdf,2023-05-24,['cs.cv'],2305.14800v5.pdf,"  After discovering that Language Models (LMs) can be good in-context few-shot
+learners, numerous strategies have been proposed to optimize in-context
+sequence configurations. Recently, researchers in Vision-Language (VL) domains
+also develop their few-shot learners, while they only use the simplest way,
+ie., randomly sampling, to configure in-context image-text pairs. In order to
+explore the effects of varying configurations on VL in-context learning, we
+devised four strategies for image selection and four for caption assignment to
+configure in-context image-text pairs for image captioning. Here Image
+Captioning is used as the case study since it can be seen as the
+visually-conditioned LM. Our comprehensive experiments yield two
+counter-intuitive but valuable insights, highlighting the distinct
+characteristics of VL in-context learning due to multi-modal synergy, as
+compared to the NLP case. Furthermore, in our exploration of optimal
+combination strategies, we observed an average performance enhancement of 20.9
+of CIDEr scores compared to the baseline. The code is given in
+https://github.com/yongliang-wu/ExploreCfg.
+"
+Estimating Large Language Model Capabilities without Labeled Test Data,Harvey Yiyun Fu,http://arxiv.org/pdf/2305.14802v2.pdf,2023-05-24,['cs.cl'],2305.14802v2.pdf,"  Large Language Models (LLMs) have the impressive ability to perform
+in-context learning (ICL) from only a few examples, but the success of ICL
+varies widely from task to task. Thus, it is important to quickly determine
+whether ICL is applicable to a new task, but directly evaluating ICL accuracy
+can be expensive in situations where test data is expensive to annotate -- the
+exact situations where ICL is most appealing. In this paper, we propose the
+task of ICL accuracy estimation, in which we predict the accuracy of an LLM
+when doing in-context learning on a new task given only unlabeled test data for
+that task. To perform ICL accuracy estimation, we propose a method that trains
+a meta-model using LLM confidence scores as features. We compare our method to
+several strong accuracy estimation baselines on a new benchmark that covers 4
+LLMs and 3 task collections. The meta-model improves over all baselines across
+8 out of 12 settings and achieves the same estimation performance as directly
+evaluating on 40 collected labeled test examples per task. At the same time, no
+existing approach provides an accurate and reliable ICL accuracy estimation in
+every setting, highlighting the need for better ways to measure the uncertainty
+of LLM predictions.
+"
+BUFFET: Benchmarking Large Language Models for Few-shot Cross-lingual  Transfer,Akari Asai,http://arxiv.org/pdf/2305.14857v1.pdf,2023-05-24,['cs.cl'],2305.14857v1.pdf,"  Despite remarkable advancements in few-shot generalization in natural
+language processing, most models are developed and evaluated primarily in
+English. To facilitate research on few-shot cross-lingual transfer, we
+introduce a new benchmark, called BUFFET, which unifies 15 diverse tasks across
+54 languages in a sequence-to-sequence format and provides a fixed set of
+few-shot examples and instructions. BUFFET is designed to establish a rigorous
+and equitable evaluation framework for few-shot cross-lingual transfer across a
+broad range of tasks and languages. Using BUFFET, we perform thorough
+evaluations of state-of-the-art multilingual large language models with
+different transfer methods, namely in-context learning and fine-tuning. Our
+findings reveal significant room for improvement in few-shot in-context
+cross-lingual transfer. In particular, ChatGPT with in-context learning often
+performs worse than much smaller mT5-base models fine-tuned on English task
+data and few-shot in-language examples. Our analysis suggests various avenues
+for future research in few-shot cross-lingual transfer, such as improved
+pretraining, understanding, and future evaluations.
+"
+Adversarial Demonstration Attacks on Large Language Models,Jiongxiao Wang,http://arxiv.org/pdf/2305.14950v2.pdf,2023-05-24,"['cs.cl', 'cs.ai', 'cs.cr', 'cs.lg']",2305.14950v2.pdf,"  With the emergence of more powerful large language models (LLMs), such as
+ChatGPT and GPT-4, in-context learning (ICL) has gained significant prominence
+in leveraging these models for specific tasks by utilizing data-label pairs as
+precondition prompts. While incorporating demonstrations can greatly enhance
+the performance of LLMs across various tasks, it may introduce a new security
+concern: attackers can manipulate only the demonstrations without changing the
+input to perform an attack. In this paper, we investigate the security concern
+of ICL from an adversarial perspective, focusing on the impact of
+demonstrations. We propose a novel attack method named advICL, which aims to
+manipulate only the demonstration without changing the input to mislead the
+models. Our results demonstrate that as the number of demonstrations increases,
+the robustness of in-context learning would decrease. Additionally, we also
+identify the intrinsic property of the demonstrations is that they can be used
+(prepended) with different inputs. As a result, it introduces a more practical
+threat model in which an attacker can attack the test input example even
+without knowing and manipulating it. To achieve it, we propose the transferable
+version of advICL, named Transferable-advICL. Our experiment shows that the
+adversarial demonstration generated by Transferable-advICL can successfully
+attack the unseen test input examples. We hope that our study reveals the
+critical security risks associated with ICL and underscores the need for
+extensive research on the robustness of ICL, particularly given its increasing
+significance in the advancement of LLMs.
+"
+Self-ICL: Zero-Shot In-Context Learning with Self-Generated  Demonstrations,Wei-Lin Chen,http://arxiv.org/pdf/2305.15035v2.pdf,2023-05-24,['cs.cl'],2305.15035v2.pdf,"  Large language models (LLMs) have exhibited striking in-context learning
+(ICL) ability to adapt to target tasks with a few input-output demonstrations.
+For better ICL, different methods are proposed to select representative
+demonstrations from existing training corpora. However, such settings are not
+aligned with real-world practices, as end-users usually query LMs without
+access to demonstration pools. In this work, we introduce Self-ICL -- a simple
+framework which bootstraps LMs' intrinsic capabilities to perform zero-shot
+ICL. Given a test input, Self-ICL first prompts the model to generate
+pseudo-inputs. Next, the model predicts pseudo-labels for the pseudo-inputs via
+zero-shot prompting. Finally, we perform ICL for the test input with the
+pseudo-input-label pairs as demonstrations. Evaluation on 23 BIG-Bench Hard
+tasks shows Self-ICL outperforms zero-shot baselines on both average accuracy
+and head-to-head comparison. Moreover, with zero-shot chain-of-thought,
+Self-ICL achieves results comparable to using real demonstrations.
+Additionally, we conduct a range of analyses to validate Self-ICL's
+effectiveness and provide insights for its behaviors under different settings.
+"
+Measuring and Mitigating Constraint Violations of In-Context Learning  for Utterance-to-API Semantic Parsing,Shufan Wang,http://arxiv.org/pdf/2305.15338v1.pdf,2023-05-24,"['cs.ai', 'cs.cl']",2305.15338v1.pdf,"  In executable task-oriented semantic parsing, the system aims to translate
+users' utterances in natural language to machine-interpretable programs (API
+calls) that can be executed according to pre-defined API specifications. With
+the popularity of Large Language Models (LLMs), in-context learning offers a
+strong baseline for such scenarios, especially in data-limited regimes.
+However, LLMs are known to hallucinate and therefore pose a formidable
+challenge in constraining generated content. Thus, it remains uncertain if LLMs
+can effectively perform task-oriented utterance-to-API generation where
+respecting API's structural and task-specific constraints is crucial.
+  In this work, we seek to measure, analyze and mitigate such constraints
+violations. First, we identify the categories of various constraints in
+obtaining API-semantics from task-oriented utterances, and define fine-grained
+metrics that complement traditional ones. Second, we leverage these metrics to
+conduct a detailed error analysis of constraints violations seen in
+state-of-the-art LLMs, which motivates us to investigate two mitigation
+strategies: Semantic-Retrieval of Demonstrations (SRD) and API-aware
+Constrained Decoding (API-CD). Our experiments show that these strategies are
+effective at reducing constraints violations and improving the quality of the
+generated API calls, but require careful consideration given their
+implementation complexity and latency.
+"
+What can Large Language Models do in chemistry? A comprehensive  benchmark on eight tasks,Taicheng Guo,http://arxiv.org/pdf/2305.18365v2.pdf,2023-05-27,"['cs.cl', 'cs.ai']",2305.18365v2.pdf,"  Large Language Models (LLMs) with strong abilities in natural language
+processing tasks have emerged and have been applied in various kinds of areas
+such as science, finance and software engineering. However, the capability of
+LLMs to advance the field of chemistry remains unclear. In this paper, rather
+than pursuing state-of-the-art performance, we aim to evaluate capabilities of
+LLMs in a wide range of tasks across the chemistry domain. We identify three
+key chemistry-related capabilities including understanding, reasoning and
+explaining to explore in LLMs and establish a benchmark containing eight
+chemistry tasks. Our analysis draws on widely recognized datasets facilitating
+a broad exploration of the capacities of LLMs within the context of practical
+chemistry. Five LLMs (GPT-4, GPT-3.5, Davinci-003, Llama and Galactica) are
+evaluated for each chemistry task in zero-shot and few-shot in-context learning
+settings with carefully selected demonstration examples and specially crafted
+prompts. Our investigation found that GPT-4 outperformed other models and LLMs
+exhibit different competitive levels in eight chemistry tasks. In addition to
+the key findings from the comprehensive benchmark analysis, our work provides
+insights into the limitation of current LLMs and the impact of in-context
+learning settings on LLMs' performance across various chemistry tasks. The code
+and datasets used in this study are available at
+https://github.com/ChemFoundationModels/ChemLLMBench.
+"
+Mitigating Label Biases for In-context Learning,Yu Fei,http://arxiv.org/pdf/2305.19148v3.pdf,2023-05-28,"['cs.cl', 'cs.ai', 'cs.lg']",2305.19148v3.pdf,"  Various design settings for in-context learning (ICL), such as the choice and
+order of the in-context examples, can bias a model toward a particular
+prediction without being reflective of an understanding of the task. While many
+studies discuss these design choices, there have been few systematic
+investigations into categorizing them and mitigating their impact. In this
+work, we define a typology for three types of label biases in ICL for text
+classification: vanilla-label bias, context-label bias, and domain-label bias
+(which we conceptualize and detect for the first time).
+  Our analysis demonstrates that prior label bias calibration methods fall
+short of addressing all three types of biases. Specifically, domain-label bias
+restricts LLMs to random-level performance on many tasks regardless of the
+choice of in-context examples. To mitigate the effect of these biases, we
+propose a simple bias calibration method that estimates a language model's
+label bias using random in-domain words from the task corpus. After controlling
+for this estimated bias when making predictions, our novel domain-context
+calibration significantly improves the ICL performance of GPT-J and GPT-3 on a
+wide range of tasks. The gain is substantial on tasks with large domain-label
+bias (up to 37% in Macro-F1). Furthermore, our results generalize to models
+with different scales, pretraining methods, and manually-designed task
+instructions, showing the prevalence of label biases in ICL.
+"
+"What and How does In-Context Learning Learn? Bayesian Model Averaging,  Parameterization, and Generalization",Yufeng Zhang,http://arxiv.org/pdf/2305.19420v2.pdf,2023-05-30,"['stat.ml', 'cs.lg']",2305.19420v2.pdf,"  In this paper, we conduct a comprehensive study of In-Context Learning (ICL)
+by addressing several open questions: (a) What type of ICL estimator is learned
+by large language models? (b) What is a proper performance metric for ICL and
+what is the error rate? (c) How does the transformer architecture enable ICL?
+To answer these questions, we adopt a Bayesian view and formulate ICL as a
+problem of predicting the response corresponding to the current covariate,
+given a number of examples drawn from a latent variable model. To answer (a),
+we show that, without updating the neural network parameters, ICL implicitly
+implements the Bayesian model averaging algorithm, which is proven to be
+approximately parameterized by the attention mechanism. For (b), we analyze the
+ICL performance from an online learning perspective and establish a
+$\mathcal{O}(1/T)$ regret bound for perfectly pretrained ICL, where $T$ is the
+number of examples in the prompt. To answer (c), we show that, in addition to
+encoding Bayesian model averaging via attention, the transformer architecture
+also enables a fine-grained statistical analysis of pretraining under realistic
+assumptions. In particular, we prove that the error of pretrained model is
+bounded by a sum of an approximation error and a generalization error, where
+the former decays to zero exponentially as the depth grows, and the latter
+decays to zero sublinearly with the number of tokens in the pretraining
+dataset. Our results provide a unified understanding of the transformer and its
+ICL ability with bounds on ICL regret, approximation, and generalization, which
+deepens our knowledge of these essential aspects of modern language models.
+"
+Augmenting Language Models with Long-Term Memory,Weizhi Wang,http://arxiv.org/pdf/2306.07174v1.pdf,2023-06-12,['cs.cl'],2306.07174v1.pdf,"  Existing large language models (LLMs) can only afford fix-sized inputs due to
+the input length limit, preventing them from utilizing rich long-context
+information from past inputs. To address this, we propose a framework, Language
+Models Augmented with Long-Term Memory (LongMem), which enables LLMs to
+memorize long history. We design a novel decoupled network architecture with
+the original backbone LLM frozen as a memory encoder and an adaptive residual
+side-network as a memory retriever and reader. Such a decoupled memory design
+can easily cache and update long-term past contexts for memory retrieval
+without suffering from memory staleness. Enhanced with memory-augmented
+adaptation training, LongMem can thus memorize long past context and use
+long-term memory for language modeling. The proposed memory retrieval module
+can handle unlimited-length context in its memory bank to benefit various
+downstream tasks. Typically, LongMem can enlarge the long-form memory to 65k
+tokens and thus cache many-shot extra demonstration examples as long-form
+memory for in-context learning. Experiments show that our method outperforms
+strong long-context models on ChapterBreak, a challenging long-context modeling
+benchmark, and achieves remarkable improvements on memory-augmented in-context
+learning over LLMs. The results demonstrate that the proposed method is
+effective in helping language models to memorize and utilize long-form
+contents. Our code is open-sourced at https://aka.ms/LongMem.
+"
+Pretraining task diversity and the emergence of non-Bayesian in-context  learning for regression,Allan Raventós,http://arxiv.org/pdf/2306.15063v2.pdf,2023-06-26,"['cs.lg', 'cs.ai', 'cs.cl']",2306.15063v2.pdf,"  Pretrained transformers exhibit the remarkable ability of in-context learning
+(ICL): they can learn tasks from just a few examples provided in the prompt
+without updating any weights. This raises a foundational question: can ICL
+solve fundamentally $\textit{new}$ tasks that are very different from those
+seen during pretraining? To probe this question, we examine ICL's performance
+on linear regression while varying the diversity of tasks in the pretraining
+dataset. We empirically demonstrate a $\textit{task diversity threshold}$ for
+the emergence of ICL. Below this threshold, the pretrained transformer cannot
+solve unseen regression tasks, instead behaving like a Bayesian estimator with
+the $\textit{non-diverse pretraining task distribution}$ as the prior. Beyond
+this threshold, the transformer significantly outperforms this estimator; its
+behavior aligns with that of ridge regression, corresponding to a Gaussian
+prior over $\textit{all tasks}$, including those not seen during pretraining.
+Thus, when pretrained on data with task diversity greater than the threshold,
+transformers $\textit{can}$ optimally solve fundamentally new tasks in-context.
+Importantly, this capability hinges on it deviating from the Bayes optimal
+estimator with the pretraining distribution as the prior. This study also
+explores the effect of regularization, model capacity and task structure and
+underscores, in a concrete example, the critical role of task diversity,
+alongside data and model scale, in the emergence of ICL. Code is available at
+https://github.com/mansheej/icl-task-diversity.
+"
+Understanding In-Context Learning via Supportive Pretraining Data,Xiaochuang Han,http://arxiv.org/pdf/2306.15091v1.pdf,2023-06-26,['cs.cl'],2306.15091v1.pdf,"  In-context learning (ICL) improves language models' performance on a variety
+of NLP tasks by simply demonstrating a handful of examples at inference time.
+It is not well understood why ICL ability emerges, as the model has never been
+specifically trained on such demonstrations. Unlike prior work that explores
+implicit mechanisms behind ICL, we study ICL via investigating the pretraining
+data. Specifically, we first adapt an iterative, gradient-based approach to
+find a small subset of pretraining data that supports ICL. We observe that a
+continued pretraining on this small subset significantly improves the model's
+ICL ability, by up to 18%. We then compare the supportive subset constrastively
+with random subsets of pretraining data and discover: (1) The supportive
+pretraining data to ICL do not have a higher domain relevance to downstream
+tasks. (2) The supportive pretraining data have a higher mass of rarely
+occurring, long-tail tokens. (3) The supportive pretraining data are
+challenging examples where the information gain from long-range context is
+below average, indicating learning to incorporate difficult long-range context
+encourages ICL. Our work takes a first step towards understanding ICL via
+analyzing instance-level pretraining data. Our insights have a potential to
+enhance the ICL ability of language models by actively guiding the construction
+of pretraining data in the future.
+"
+Schema-learning and rebinding as mechanisms of in-context learning and  emergence,Sivaramakrishnan Swaminathan,http://arxiv.org/pdf/2307.01201v1.pdf,2023-06-16,"['cs.cl', 'cs.ai']",2307.01201v1.pdf,"  In-context learning (ICL) is one of the most powerful and most unexpected
+capabilities to emerge in recent transformer-based large language models
+(LLMs). Yet the mechanisms that underlie it are poorly understood. In this
+paper, we demonstrate that comparable ICL capabilities can be acquired by an
+alternative sequence prediction learning method using clone-structured causal
+graphs (CSCGs). Moreover, a key property of CSCGs is that, unlike
+transformer-based LLMs, they are {\em interpretable}, which considerably
+simplifies the task of explaining how ICL works. Specifically, we show that it
+uses a combination of (a) learning template (schema) circuits for pattern
+completion, (b) retrieving relevant templates in a context-sensitive manner,
+and (c) rebinding of novel tokens to appropriate slots in the templates. We go
+on to marshall evidence for the hypothesis that similar mechanisms underlie ICL
+in LLMs. For example, we find that, with CSCGs as with LLMs, different
+capabilities emerge at different levels of overparameterization, suggesting
+that overparameterization helps in learning more complex template (schema)
+circuits. By showing how ICL can be achieved with small models and datasets, we
+open up a path to novel architectures, and take a vital step towards a more
+general understanding of the mechanics behind this important capability.
+"
+Towards Understanding In-Context Learning with Contrastive  Demonstrations and Saliency Maps,Zongxia Li,http://arxiv.org/pdf/2307.05052v1.pdf,2023-07-11,"['cs.cl', 'cs.ai']",2307.05052v1.pdf,"  We investigate the role of various demonstration components in the in-context
+learning (ICL) performance of large language models (LLMs). Specifically, we
+explore the impacts of ground-truth labels, input distribution, and
+complementary explanations, particularly when these are altered or perturbed.
+We build on previous work, which offers mixed findings on how these elements
+influence ICL. To probe these questions, we employ explainable NLP (XNLP)
+methods and utilize saliency maps of contrastive demonstrations for both
+qualitative and quantitative analysis. Our findings reveal that flipping
+ground-truth labels significantly affects the saliency, though it's more
+noticeable in larger LLMs. Our analysis of the input distribution at a granular
+level reveals that changing sentiment-indicative terms in a sentiment analysis
+task to neutral ones does not have as substantial an impact as altering
+ground-truth labels. Finally, we find that the effectiveness of complementary
+explanations in boosting ICL performance is task-dependent, with limited
+benefits seen in sentiment analysis tasks compared to symbolic reasoning tasks.
+These insights are critical for understanding the functionality of LLMs and
+guiding the development of effective demonstrations, which is increasingly
+relevant in light of the growing use of LLMs in applications such as ChatGPT.
+Our research code is publicly available at https://github.com/paihengxu/XICL.
+"
+In-context learning for model-free system identification,Marco Forgione,http://arxiv.org/pdf/2308.13380v1.pdf,2023-08-25,"['eess.sy', 'cs.lg', 'cs.sy']",2308.13380v1.pdf,"  In traditional system identification, we estimate a model of an unknown
+dynamical system based on given input/output sequences and available physical
+knowledge. Yet, is it also possible to understand the intricacies of dynamical
+systems not solely from their input/output patterns, but by observing the
+behavior of other systems within the same class? This central question drives
+the study presented in this paper.
+  In response to this query, we introduce a novel paradigm for system
+identification, addressing two primary tasks: one-step-ahead prediction and
+multi-step simulation. Unlike conventional methods, we do not directly estimate
+a model for the specific system. Instead, we pretrain a meta model that
+represents a class of dynamical systems. This meta model is trained from a
+potentially infinite stream of synthetic data, generated by systems randomly
+extracted from a certain distribution. At its core, the meta model serves as an
+implicit representation of the main characteristics of a class of dynamical
+systems. When provided with a brief context from a new system - specifically, a
+short input/output sequence - the meta model implicitly discerns its dynamics,
+enabling predictions of its behavior.
+  The proposed approach harnesses the power of Transformer architectures,
+renowned for their in-context learning capabilities in Natural Language
+Processing tasks. For one-step prediction, a GPT-like decoder-only architecture
+is utilized, whereas the simulation problem employs an encoder-decoder
+structure.
+  Initial experimental results affirmatively answer our foundational question,
+opening doors to fresh research avenues in system identification.
+"
+Ambiguity-Aware In-Context Learning with Large Language Models,Lingyu Gao,http://arxiv.org/pdf/2309.07900v1.pdf,2023-09-14,"['cs.cl', 'cs.ir']",2309.07900v1.pdf,"  In-context learning (ICL) i.e. showing LLMs only a few task-specific
+demonstrations has led to downstream gains with no task-specific fine-tuning
+required. However, LLMs are sensitive to the choice of prompts, and therefore a
+crucial research question is how to select good demonstrations for ICL. One
+effective strategy is leveraging semantic similarity between the ICL
+demonstrations and test inputs by using a text retriever, which however is
+sub-optimal as that does not consider the LLM's existing knowledge about that
+task. From prior work (Min et al., 2022), we already know that labels paired
+with the demonstrations bias the model predictions. This leads us to our
+hypothesis whether considering LLM's existing knowledge about the task,
+especially with respect to the output label space can help in a better
+demonstration selection strategy. Through extensive experimentation on three
+text classification tasks, we find that it is beneficial to not only choose
+semantically similar ICL demonstrations but also to choose those demonstrations
+that help resolve the inherent label ambiguity surrounding the test example.
+Interestingly, we find that including demonstrations that the LLM previously
+mis-classified and also fall on the test example's decision boundary, brings
+the most performance gain.
+"
+Are Human-generated Demonstrations Necessary for In-context Learning?,Rui Li,http://arxiv.org/pdf/2309.14681v2.pdf,2023-09-26,"['cs.lg', 'cs.ai']",2309.14681v2.pdf,"  Despite the promising few-shot ability of large language models (LLMs), the
+standard paradigm of In-context Learning (ICL) suffers the disadvantages of
+susceptibility to selected demonstrations and the intricacy to generate these
+demonstrations. In this paper, we raise the fundamental question that whether
+human-generated demonstrations are necessary for ICL. To answer this question,
+we propose self-contemplation prompting strategy (SEC), a paradigm free from
+human-crafted demonstrations. The key point of SEC is that, instead of using
+hand-crafted examples as demonstrations in ICL, SEC asks LLMs to first create
+demonstrations on their own, based on which the final output is generated. SEC
+is a flexible framework and can be adapted to both the vanilla ICL and the
+chain-of-thought (CoT), but with greater ease: as the manual-generation process
+of both examples and rationale can be saved. Extensive experiments in
+arithmetic reasoning, commonsense reasoning, multi-task language understanding,
+and code generation benchmarks, show that SEC, which does not require
+hand-crafted demonstrations, significantly outperforms the zero-shot learning
+strategy, and achieves comparable results to ICL with hand-crafted
+demonstrations. This demonstrates that, for many tasks, contemporary LLMs
+possess a sufficient level of competence to exclusively depend on their own
+capacity for decision making, removing the need for external training data.
+Code is available at https://github.com/ruili33/SEC.
+"
+Beyond Task Performance: Evaluating and Reducing the Flaws of Large  Multimodal Models with In-Context Learning,Mustafa Shukor,http://arxiv.org/pdf/2310.00647v1.pdf,2023-10-01,"['cs.cv', 'cs.mm']",2310.00647v1.pdf,"  Following the success of Large Language Models (LLMs), Large Multimodal
+Models (LMMs), such as the Flamingo model and its subsequent competitors, have
+started to emerge as natural steps towards generalist agents. However,
+interacting with recent LMMs reveals major limitations that are hardly captured
+by the current evaluation benchmarks. Indeed, task performances (e.g., VQA
+accuracy) alone do not provide enough clues to understand their real
+capabilities, limitations, and to which extent such models are aligned to human
+expectations. To refine our understanding of those flaws, we deviate from the
+current evaluation paradigm and propose the EvALign-ICL framework, in which we
+(1) evaluate 8 recent open-source LMMs (based on the Flamingo architecture such
+as OpenFlamingo and IDEFICS) on 5 different axes; hallucinations, abstention,
+compositionality, explainability and instruction following. Our evaluation on
+these axes reveals major flaws in LMMs. To efficiently address these problems,
+and inspired by the success of in-context learning (ICL) in LLMs, (2) we
+explore ICL as a solution and study how it affects these limitations. Based on
+our ICL study, (3) we push ICL further and propose new multimodal ICL
+approaches such as; Multitask-ICL, Chain-of-Hindsight-ICL, and
+Self-Correcting-ICL. Our findings are as follows; (1) Despite their success,
+LMMs have flaws that remain unsolved with scaling alone. (2) The effect of ICL
+on LMMs flaws is nuanced; despite its effectiveness for improved
+explainability, abstention, and instruction following, ICL does not improve
+compositional abilities, and actually even amplifies hallucinations. (3) The
+proposed ICL variants are promising as post-hoc approaches to efficiently
+tackle some of those flaws. The code is available here:
+https://evalign-icl.github.io/
+"
+Understanding In-Context Learning in Transformers and LLMs by Learning  to Learn Discrete Functions,Satwik Bhattamishra,http://arxiv.org/pdf/2310.03016v1.pdf,2023-10-04,"['cs.lg', 'cs.cl']",2310.03016v1.pdf,"  In order to understand the in-context learning phenomenon, recent works have
+adopted a stylized experimental framework and demonstrated that Transformers
+can learn gradient-based learning algorithms for various classes of real-valued
+functions. However, the limitations of Transformers in implementing learning
+algorithms, and their ability to learn other forms of algorithms are not well
+understood. Additionally, the degree to which these capabilities are confined
+to attention-based models is unclear. Furthermore, it remains to be seen
+whether the insights derived from these stylized settings can be extrapolated
+to pretrained Large Language Models (LLMs). In this work, we take a step
+towards answering these questions by demonstrating the following: (a) On a
+test-bed with a variety of Boolean function classes, we find that Transformers
+can nearly match the optimal learning algorithm for 'simpler' tasks, while
+their performance deteriorates on more 'complex' tasks. Additionally, we find
+that certain attention-free models perform (almost) identically to Transformers
+on a range of tasks. (b) When provided a teaching sequence, i.e. a set of
+examples that uniquely identifies a function in a class, we show that
+Transformers learn more sample-efficiently. Interestingly, our results show
+that Transformers can learn to implement two distinct algorithms to solve a
+single task, and can adaptively select the more sample-efficient algorithm
+depending on the sequence of in-context examples. (c) Lastly, we show that
+extant LLMs, e.g. LLaMA-2, GPT-4, can compete with nearest-neighbor baselines
+on prediction tasks that are guaranteed to not be in their training set.
+"
+SEER : A Knapsack approach to Exemplar Selection for In-Context HybridQA,Jonathan Tonglet,http://arxiv.org/pdf/2310.06675v2.pdf,2023-10-10,['cs.cl'],2310.06675v2.pdf,"  Question answering over hybrid contexts is a complex task, which requires the
+combination of information extracted from unstructured texts and structured
+tables in various ways. Recently, In-Context Learning demonstrated significant
+performance advances for reasoning tasks. In this paradigm, a large language
+model performs predictions based on a small set of supporting exemplars. The
+performance of In-Context Learning depends heavily on the selection procedure
+of the supporting exemplars, particularly in the case of HybridQA, where
+considering the diversity of reasoning chains and the large size of the hybrid
+contexts becomes crucial. In this work, we present Selection of ExEmplars for
+hybrid Reasoning (SEER), a novel method for selecting a set of exemplars that
+is both representative and diverse. The key novelty of SEER is that it
+formulates exemplar selection as a Knapsack Integer Linear Program. The
+Knapsack framework provides the flexibility to incorporate diversity
+constraints that prioritize exemplars with desirable attributes, and capacity
+constraints that ensure that the prompt size respects the provided capacity
+budgets. The effectiveness of SEER is demonstrated on FinQA and TAT-QA, two
+real-world benchmarks for HybridQA, where it outperforms previous exemplar
+selection methods.
+"
+How Do Transformers Learn In-Context Beyond Simple Functions? A Case  Study on Learning with Representations,Tianyu Guo,http://arxiv.org/pdf/2310.10616v1.pdf,2023-10-16,['cs.lg'],2310.10616v1.pdf,"  While large language models based on the transformer architecture have
+demonstrated remarkable in-context learning (ICL) capabilities, understandings
+of such capabilities are still in an early stage, where existing theory and
+mechanistic understanding focus mostly on simple scenarios such as learning
+simple function classes. This paper takes initial steps on understanding ICL in
+more complex scenarios, by studying learning with representations. Concretely,
+we construct synthetic in-context learning problems with a compositional
+structure, where the label depends on the input through a possibly complex but
+fixed representation function, composed with a linear function that differs in
+each instance. By construction, the optimal ICL algorithm first transforms the
+inputs by the representation function, and then performs linear ICL on top of
+the transformed dataset. We show theoretically the existence of transformers
+that approximately implement such algorithms with mild depth and size.
+Empirically, we find trained transformers consistently achieve near-optimal ICL
+performance in this setting, and exhibit the desired dissection where lower
+layers transforms the dataset and upper layers perform linear ICL. Through
+extensive probing and a new pasting experiment, we further reveal several
+mechanisms within the trained transformers, such as concrete copying behaviors
+on both the inputs and the representations, linear ICL capability of the upper
+layers alone, and a post-ICL representation selection mechanism in a harder
+mixture setting. These observed mechanisms align well with our theory and may
+shed light on how transformers perform ICL in more realistic scenarios.
+"
+Demonstrations Are All You Need: Advancing Offensive Content  Paraphrasing using In-Context Learning,Anirudh Som,http://arxiv.org/pdf/2310.10707v1.pdf,2023-10-16,"['cs.cl', 'cs.ai']",2310.10707v1.pdf,"  Paraphrasing of offensive content is a better alternative to content removal
+and helps improve civility in a communication environment. Supervised
+paraphrasers; however, rely heavily on large quantities of labelled data to
+help preserve meaning and intent. They also retain a large portion of the
+offensiveness of the original content, which raises questions on their overall
+usability. In this paper we aim to assist practitioners in developing usable
+paraphrasers by exploring In-Context Learning (ICL) with large language models
+(LLMs), i.e., using a limited number of input-label demonstration pairs to
+guide the model in generating desired outputs for specific queries. Our study
+focuses on key factors such as -- number and order of demonstrations, exclusion
+of prompt instruction, and reduction in measured toxicity. We perform
+principled evaluation on three datasets, including our proposed Context-Aware
+Polite Paraphrase dataset, comprising of dialogue-style rude utterances, polite
+paraphrases, and additional dialogue context. We evaluate our approach using
+two closed source and one open source LLM. Our results reveal that ICL is
+comparable to supervised methods in generation quality, while being
+qualitatively better by 25% on human evaluation and attaining lower toxicity by
+76%. Also, ICL-based paraphrasers only show a slight reduction in performance
+even with just 10% training data.
+"
+O3D: Offline Data-driven Discovery and Distillation for Sequential  Decision-Making with Large Language Models,Yuchen Xiao,http://arxiv.org/pdf/2310.14403v1.pdf,2023-10-22,"['cs.ai', 'cs.cl']",2310.14403v1.pdf,"  Recent advancements in large language models (LLMs) have exhibited promising
+performance in solving sequential decision-making problems. By imitating
+few-shot examples provided in the prompts (i.e., in-context learning), an LLM
+agent can interact with an external environment and complete given tasks
+without additional training. However, such few-shot examples are often
+insufficient to generate high-quality solutions for complex and long-horizon
+tasks, while the limited context length cannot consume larger-scale
+demonstrations. To this end, we propose an offline learning framework that
+utilizes offline data at scale (e.g, logs of human interactions) to facilitate
+the in-context learning performance of LLM agents. We formally define
+LLM-powered policies with both text-based approaches and code-based approaches.
+We then introduce an Offline Data-driven Discovery and Distillation (O3D)
+framework to improve LLM-powered policies without finetuning. O3D automatically
+discovers reusable skills and distills generalizable knowledge across multiple
+tasks based on offline interaction data, advancing the capability of solving
+downstream tasks. Empirical results under two interactive decision-making
+benchmarks (ALFWorld and WebShop) demonstrate that O3D can notably enhance the
+decision-making capabilities of LLMs through the offline discovery and
+distillation process, and consistently outperform baselines across various LLMs
+with both text-based-policy and code-based-policy.
+"
+Transformers Learn Higher-Order Optimization Methods for In-Context  Learning: A Study with Linear Models,Deqing Fu,http://arxiv.org/pdf/2310.17086v1.pdf,2023-10-26,"['cs.lg', 'cs.ai', 'cs.cl']",2310.17086v1.pdf,"  Transformers are remarkably good at in-context learning (ICL) -- learning
+from demonstrations without parameter updates -- but how they perform ICL
+remains a mystery. Recent work suggests that Transformers may learn in-context
+by internally running Gradient Descent, a first-order optimization method. In
+this paper, we instead demonstrate that Transformers learn to implement
+higher-order optimization methods to perform ICL. Focusing on in-context linear
+regression, we show that Transformers learn to implement an algorithm very
+similar to Iterative Newton's Method, a higher-order optimization method,
+rather than Gradient Descent. Empirically, we show that predictions from
+successive Transformer layers closely match different iterations of Newton's
+Method linearly, with each middle layer roughly computing 3 iterations. In
+contrast, exponentially more Gradient Descent steps are needed to match an
+additional Transformers layer; this suggests that Transformers have an
+comparable rate of convergence with high-order methods such as Iterative
+Newton, which are exponentially faster than Gradient Descent. We also show that
+Transformers can learn in-context on ill-conditioned data, a setting where
+Gradient Descent struggles but Iterative Newton succeeds. Finally, we show
+theoretical results which support our empirical findings and have a close
+correspondence with them: we prove that Transformers can implement $k$
+iterations of Newton's method with $\mathcal{O}(k)$ layers.
+"
+Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time,Zichang Liu,http://arxiv.org/pdf/2310.17157v1.pdf,2023-10-26,['cs.lg'],2310.17157v1.pdf,"  Large language models (LLMs) with hundreds of billions of parameters have
+sparked a new wave of exciting AI applications. However, they are
+computationally expensive at inference time. Sparsity is a natural approach to
+reduce this cost, but existing methods either require costly retraining, have
+to forgo LLM's in-context learning ability, or do not yield wall-clock time
+speedup on modern hardware. We hypothesize that contextual sparsity, which are
+small, input-dependent sets of attention heads and MLP parameters that yield
+approximately the same output as the dense model for a given input, can address
+these issues. We show that contextual sparsity exists, that it can be
+accurately predicted, and that we can exploit it to speed up LLM inference in
+wall-clock time without compromising LLM's quality or in-context learning
+ability. Based on these insights, we propose DejaVu, a system that uses a
+low-cost algorithm to predict contextual sparsity on the fly given inputs to
+each layer, along with an asynchronous and hardware-aware implementation that
+speeds up LLM inference. We validate that DejaVu can reduce the inference
+latency of OPT-175B by over 2X compared to the state-of-the-art
+FasterTransformer, and over 6X compared to the widely used Hugging Face
+implementation, without compromising model quality. The code is available at
+https://github.com/FMInference/DejaVu.
+"
+Improving Input-label Mapping with Demonstration Replay for In-context  Learning,Zhuocheng Gong,http://arxiv.org/pdf/2310.19572v1.pdf,2023-10-30,['cs.cl'],2310.19572v1.pdf,"  In-context learning (ICL) is an emerging capability of large autoregressive
+language models where a few input-label demonstrations are appended to the
+input to enhance the model's understanding of downstream NLP tasks, without
+directly adjusting the model parameters. The effectiveness of ICL can be
+attributed to the strong language modeling capabilities of large language
+models (LLMs), which enable them to learn the mapping between input and labels
+based on in-context demonstrations. Despite achieving promising results, the
+causal nature of language modeling in ICL restricts the attention to be
+backward only, i.e., a token only attends to its previous tokens, failing to
+capture the full input-label information and limiting the model's performance.
+In this paper, we propose a novel ICL method called Repeated Demonstration with
+Sliding Causal Attention, (RdSca). Specifically, we duplicate later
+demonstrations and concatenate them to the front, allowing the model to
+`observe' the later information even under the causal restriction. Besides, we
+introduce sliding causal attention, which customizes causal attention to avoid
+information leakage. Experimental results show that our method significantly
+improves the input-label mapping in ICL demonstrations. We also conduct an
+in-depth analysis of how to customize the causal attention without training,
+which has been an unexplored area in previous research.
+"
+Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in  Transformer Models,Steve Yadlowsky,http://arxiv.org/pdf/2311.00871v1.pdf,2023-11-01,"['cs.lg', 'cs.cl', 'stat.ml']",2311.00871v1.pdf,"  Transformer models, notably large language models (LLMs), have the remarkable
+ability to perform in-context learning (ICL) -- to perform new tasks when
+prompted with unseen input-output examples without any explicit model training.
+In this work, we study how effectively transformers can bridge between their
+pretraining data mixture, comprised of multiple distinct task families, to
+identify and learn new tasks in-context which are both inside and outside the
+pretraining distribution. Building on previous work, we investigate this
+question in a controlled setting, where we study transformer models trained on
+sequences of $(x, f(x))$ pairs rather than natural language. Our empirical
+results show transformers demonstrate near-optimal unsupervised model selection
+capabilities, in their ability to first in-context identify different task
+families and in-context learn within them when the task families are
+well-represented in their pretraining data. However when presented with tasks
+or functions which are out-of-domain of their pretraining data, we demonstrate
+various failure modes of transformers and degradation of their generalization
+for even simple extrapolation tasks. Together our results highlight that the
+impressive ICL abilities of high-capacity sequence models may be more closely
+tied to the coverage of their pretraining data mixtures than inductive biases
+that create fundamental generalization capabilities.
+"
+Large Language Models are Few-Shot Summarizers: Multi-Intent Comment  Generation via In-Context Learning,Mingyang Geng,http://arxiv.org/pdf/2304.11384v3.pdf,2023-04-22,['cs.se'],2304.11384v3.pdf,"  Code comment generation aims at generating natural language descriptions for
+a code snippet to facilitate developers' program comprehension activities.
+Despite being studied for a long time, a bottleneck for existing approaches is
+that given a code snippet, they can only generate one comment while developers
+usually need to know information from diverse perspectives such as what is the
+functionality of this code snippet and how to use it. To tackle this
+limitation, this study empirically investigates the feasibility of utilizing
+large language models (LLMs) to generate comments that can fulfill developers'
+diverse intents. Our intuition is based on the facts that (1) the code and its
+pairwise comment are used during the pre-training process of LLMs to build the
+semantic connection between the natural language and programming language, and
+(2) comments in the real-world projects, which are collected for the
+pre-training, usually contain different developers' intents. We thus postulate
+that the LLMs can already understand the code from different perspectives after
+the pre-training. Indeed, experiments on two large-scale datasets demonstrate
+the rationale of our insights: by adopting the in-context learning paradigm and
+giving adequate prompts to the LLM (e.g., providing it with ten or more
+examples), the LLM can significantly outperform a state-of-the-art supervised
+learning approach on generating comments with multiple intents. Results also
+show that customized strategies for constructing the prompts and
+post-processing strategies for reranking the results can both boost the LLM's
+performances, which shed light on future research directions for using LLMs to
+achieve comment generation.
+"
+Principle-Driven Self-Alignment of Language Models from Scratch with  Minimal Human Supervision,Zhiqing Sun,http://arxiv.org/pdf/2305.03047v1.pdf,2023-05-04,"['cs.lg', 'cs.ai', 'cs.cl', 'cs.cy']",2305.03047v1.pdf,"  Recent AI-assistant agents, such as ChatGPT, predominantly rely on supervised
+fine-tuning (SFT) with human annotations and reinforcement learning from human
+feedback (RLHF) to align the output of large language models (LLMs) with human
+intentions, ensuring they are helpful, ethical, and reliable. However, this
+dependence can significantly constrain the true potential of AI-assistant
+agents due to the high cost of obtaining human supervision and the related
+issues on quality, reliability, diversity, self-consistency, and undesirable
+biases. To address these challenges, we propose a novel approach called
+SELF-ALIGN, which combines principle-driven reasoning and the generative power
+of LLMs for the self-alignment of AI agents with minimal human supervision. Our
+approach encompasses four stages: first, we use an LLM to generate synthetic
+prompts, and a topic-guided method to augment the prompt diversity; second, we
+use a small set of human-written principles for AI models to follow, and guide
+the LLM through in-context learning from demonstrations (of principles
+application) to produce helpful, ethical, and reliable responses to user's
+queries; third, we fine-tune the original LLM with the high-quality
+self-aligned responses so that the resulting model can generate desirable
+responses for each query directly without the principle set and the
+demonstrations anymore; and finally, we offer a refinement step to address the
+issues of overly-brief or indirect responses. Applying SELF-ALIGN to the
+LLaMA-65b base language model, we develop an AI assistant named Dromedary. With
+fewer than 300 lines of human annotations (including < 200 seed prompts, 16
+generic principles, and 5 exemplars for in-context learning). Dromedary
+significantly surpasses the performance of several state-of-the-art AI systems,
+including Text-Davinci-003 and Alpaca, on benchmark datasets with various
+settings.
+"
+One for All: Towards Training One Graph Model for All Classification  Tasks,Hao Liu,http://arxiv.org/pdf/2310.00149v1.pdf,2023-09-29,['cs.lg'],2310.00149v1.pdf,"  Designing a single model that addresses multiple tasks has been a
+long-standing objective in artificial intelligence. Recently, large language
+models have demonstrated exceptional capability in integrating and solving
+different tasks within the language domain. However, a unified model for
+various tasks on graphs remains underexplored, primarily due to the challenges
+unique to the graph learning domain. First, graph data from different areas
+carry distinct attributes and follow different distributions. Such discrepancy
+makes it hard to represent graphs in a single representation space. Second,
+tasks on graphs diversify into node, link, and graph tasks, requiring distinct
+embedding strategies. Finally, an appropriate graph prompting paradigm for
+in-context learning is unclear. Striving to handle all the aforementioned
+challenges, we propose One for All (OFA), the first general framework that can
+use a single graph model to address the above challenges. Specifically, OFA
+proposes text-attributed graphs to unify different graph data by describing
+nodes and edges with natural language and uses language models to encode the
+diverse and possibly cross-domain text attributes to feature vectors in the
+same embedding space. Furthermore, OFA introduces the concept of
+nodes-of-interest to standardize different tasks with a single task
+representation. For in-context learning on graphs, OFA introduces a novel graph
+prompting paradigm that appends prompting substructures to the input graph,
+which enables it to address varied tasks without fine-tuning. We train the OFA
+model using graph data from multiple domains (including citation networks,
+molecular graphs, knowledge graphs, etc.) simultaneously and evaluate its
+ability in supervised, few-shot, and zero-shot learning scenarios. OFA performs
+well across different tasks, making it the first general-purpose graph
+classification model across domains.
+"
+The Inductive Bias of In-Context Learning: Rethinking Pretraining  Example Design,Yoav Levine,http://arxiv.org/pdf/2110.04541v3.pdf,2021-10-09,"['cs.cl', 'cs.lg']",2110.04541v3.pdf,"  Pretraining Neural Language Models (NLMs) over a large corpus involves
+chunking the text into training examples, which are contiguous text segments of
+sizes processable by the neural architecture. We highlight a bias introduced by
+this common practice: we prove that the pretrained NLM can model much stronger
+dependencies between text segments that appeared in the same training example,
+than it can between text segments that appeared in different training examples.
+This intuitive result has a twofold role. First, it formalizes the motivation
+behind a broad line of recent successful NLM training heuristics, proposed for
+the pretraining and fine-tuning stages, which do not necessarily appear related
+at first glance. Second, our result clearly indicates further improvements to
+be made in NLM pretraining for the benefit of Natural Language Understanding
+tasks. As an example, we propose ""kNN-Pretraining"": we show that including
+semantically related non-neighboring sentences in the same pretraining example
+yields improved sentence representations and open domain question answering
+abilities. This theoretically motivated degree of freedom for pretraining
+example design indicates new training schemes for self-improving
+representations.
+"
+MAGMA -- Multimodal Augmentation of Generative Models through  Adapter-based Finetuning,Constantin Eichenberg,http://arxiv.org/pdf/2112.05253v2.pdf,2021-12-09,"['cs.cv', 'cs.cl', 'i.2.7; i.4.8; i.5.1']",2112.05253v2.pdf,"  Large-scale pretraining is fast becoming the norm in Vision-Language (VL)
+modeling. However, prevailing VL approaches are limited by the requirement for
+labeled data and the use of complex multi-step pretraining objectives. We
+present MAGMA - a simple method for augmenting generative language models with
+additional modalities using adapter-based finetuning. Building on Frozen, we
+train a series of VL models that autoregressively generate text from arbitrary
+combinations of visual and textual input. The pretraining is entirely
+end-to-end using a single language modeling objective, simplifying optimization
+compared to previous approaches. Importantly, the language model weights remain
+unchanged during training, allowing for transfer of encyclopedic knowledge and
+in-context learning abilities from language pretraining. MAGMA outperforms
+Frozen on open-ended generative tasks, achieving state of the art results on
+the OKVQA benchmark and competitive results on a range of other popular VL
+benchmarks, while pretraining on 0.2% of the number of samples used to train
+SimVLM.
+"
+Black-Box Tuning for Language-Model-as-a-Service,Tianxiang Sun,http://arxiv.org/pdf/2201.03514v4.pdf,2022-01-10,"['cs.cl', 'cs.ai']",2201.03514v4.pdf,"  Extremely large pre-trained language models (PTMs) such as GPT-3 are usually
+released as a service. It allows users to design task-specific prompts to query
+the PTMs through some black-box APIs. In such a scenario, which we call
+Language-Model-as-a-Service (LMaaS), the gradients of PTMs are usually
+unavailable. Can we optimize the task prompts by only accessing the model
+inference APIs? This paper proposes the black-box tuning framework to optimize
+the continuous prompt prepended to the input text via derivative-free
+optimization. Instead of optimizing in the original high-dimensional prompt
+space, which is intractable for traditional derivative-free optimization, we
+perform optimization in a randomly generated subspace due to the low intrinsic
+dimensionality of large PTMs. The experimental results show that the black-box
+tuning with RoBERTa on a few labeled samples not only significantly outperforms
+manual prompt and GPT-3's in-context learning, but also surpasses the
+gradient-based counterparts, i.e., prompt tuning and full model tuning.
+"
+Contrastive Learning for Prompt-Based Few-Shot Language Learners,Yiren Jian,http://arxiv.org/pdf/2205.01308v1.pdf,2022-05-03,"['cs.cl', 'cs.ai']",2205.01308v1.pdf,"  The impressive performance of GPT-3 using natural language prompts and
+in-context learning has inspired work on better fine-tuning of moderately-sized
+models under this paradigm. Following this line of work, we present a
+contrastive learning framework that clusters inputs from the same class for
+better generality of models trained with only limited examples. Specifically,
+we propose a supervised contrastive framework that clusters inputs from the
+same class under different augmented ""views"" and repel the ones from different
+classes. We create different ""views"" of an example by appending it with
+different language prompts and contextual demonstrations. Combining a
+contrastive loss with the standard masked language modeling (MLM) loss in
+prompt-based few-shot learners, the experimental results show that our method
+can improve over the state-of-the-art methods in a diverse set of 15 language
+tasks. Our framework makes minimal assumptions on the task or the base model,
+and can be applied to many recent methods with little modification. The code
+will be made available at: https://github.com/yiren-jian/LM-SupCon.
+"
+Instruction Induction: From Few Examples to Natural Language Task  Descriptions,Or Honovich,http://arxiv.org/pdf/2205.10782v1.pdf,2022-05-22,['cs.cl'],2205.10782v1.pdf,"  Large language models are able to perform a task by conditioning on a few
+input-output demonstrations - a paradigm known as in-context learning. We show
+that language models can explicitly infer an underlying task from a few
+demonstrations by prompting them to generate a natural language instruction
+that fits the examples. To explore this ability, we introduce the instruction
+induction challenge, compile a dataset consisting of 24 tasks, and define a
+novel evaluation metric based on executing the generated instruction. We
+discover that, to a large extent, the ability to generate instructions does
+indeed emerge when using a model that is both large enough and aligned to
+follow instructions; InstructGPT achieves 65.7% of human performance in our
+execution-based metric, while the original GPT-3 model reaches only 9.8% of
+human performance. This surprising result suggests that instruction induction
+might be a viable learning paradigm in and of itself, where instead of fitting
+a set of latent continuous parameters to the data, one searches for the best
+description in the natural language hypothesis space.
+"
+Exploring Length Generalization in Large Language Models,Cem Anil,http://arxiv.org/pdf/2207.04901v2.pdf,2022-07-11,"['cs.cl', 'cs.lg']",2207.04901v2.pdf,"  The ability to extrapolate from short problem instances to longer ones is an
+important form of out-of-distribution generalization in reasoning tasks, and is
+crucial when learning from datasets where longer problem instances are rare.
+These include theorem proving, solving quantitative mathematics problems, and
+reading/summarizing novels. In this paper, we run careful empirical studies
+exploring the length generalization capabilities of transformer-based language
+models. We first establish that naively finetuning transformers on length
+generalization tasks shows significant generalization deficiencies independent
+of model scale. We then show that combining pretrained large language models'
+in-context learning abilities with scratchpad prompting (asking the model to
+output solution steps before producing an answer) results in a dramatic
+improvement in length generalization. We run careful failure analyses on each
+of the learning modalities and identify common sources of mistakes that
+highlight opportunities in equipping language models with the ability to
+generalize to longer problems.
+"
+Large Language Models are few(1)-shot Table Reasoners,Wenhu Chen,http://arxiv.org/pdf/2210.06710v2.pdf,2022-10-13,['cs.cl'],2210.06710v2.pdf,"  Recent literature has shown that large language models (LLMs) are generally
+excellent few-shot reasoners to solve text reasoning tasks. However, the
+capability of LLMs on table reasoning tasks is yet to be explored. In this
+paper, we aim at understanding how well LLMs can perform table-related tasks
+with few-shot in-context learning. Specifically, we evaluated LLMs on popular
+table QA and fact verification datasets like WikiTableQuestion, FetaQA,
+TabFact, and FEVEROUS and found that LLMs are competent at complex reasoning
+over table structures, though these models are not pre-trained on any table
+corpus. When combined with `chain of thoughts' prompting, LLMs can achieve very
+strong performance with only a 1-shot demonstration, even on par with some SoTA
+models. We show that LLMs are even more competent at generating comprehensive
+long-form answers on FetaQA than tuned T5-large. We further manually studied
+the reasoning chains elicited from LLMs and found that these reasoning chains
+are highly consistent with the underlying semantic form. We believe that LLMs
+can serve as a simple yet generic baseline for future research. The code and
+data are released in https://github.com/wenhuchen/TableCoT.
+"
+Explanations from Large Language Models Make Small Reasoners Better,Shiyang Li,http://arxiv.org/pdf/2210.06726v1.pdf,2022-10-13,['cs.cl'],2210.06726v1.pdf,"  Integrating free-text explanations to in-context learning of large language
+models (LLM) is shown to elicit strong reasoning capabilities along with
+reasonable explanations. In this paper, we consider the problem of leveraging
+the explanations generated by LLM to improve the training of small reasoners,
+which are more favorable in real-production deployment due to their low cost.
+We systematically explore three explanation generation approaches from LLM and
+utilize a multi-task learning framework to facilitate small models to acquire
+strong reasoning power together with explanation generation capabilities.
+Experiments on multiple reasoning tasks show that our method can consistently
+and significantly outperform finetuning baselines across different settings,
+and even perform better than finetuning/prompting a 60x larger GPT-3 (175B)
+model by up to 9.5% in accuracy. As a side benefit, human evaluation further
+shows that our method can generate high-quality explanations to justify its
+predictions, moving towards the goal of explainable AI.
+"
+Prompting Language Models for Linguistic Structure,Terra Blevins,http://arxiv.org/pdf/2211.07830v2.pdf,2022-11-15,['cs.cl'],2211.07830v2.pdf,"  Although pretrained language models (PLMs) can be prompted to perform a wide
+range of language tasks, it remains an open question how much this ability
+comes from generalizable linguistic understanding versus surface-level lexical
+patterns. To test this, we present a structured prompting approach for
+linguistic structured prediction tasks, allowing us to perform zero- and
+few-shot sequence tagging with autoregressive PLMs. We evaluate this approach
+on part-of-speech tagging, named entity recognition, and sentence chunking,
+demonstrating strong few-shot performance in all cases. We also find that while
+PLMs contain significant prior knowledge of task labels due to task leakage
+into the pretraining corpus, structured prompting can also retrieve linguistic
+structure with arbitrary labels. These findings indicate that the in-context
+learning ability and linguistic knowledge of PLMs generalizes beyond
+memorization of their training data.
+"
+Visual Programming: Compositional visual reasoning without training,Tanmay Gupta,http://arxiv.org/pdf/2211.11559v1.pdf,2022-11-18,"['cs.cv', 'cs.ai', 'cs.cl']",2211.11559v1.pdf,"  We present VISPROG, a neuro-symbolic approach to solving complex and
+compositional visual tasks given natural language instructions. VISPROG avoids
+the need for any task-specific training. Instead, it uses the in-context
+learning ability of large language models to generate python-like modular
+programs, which are then executed to get both the solution and a comprehensive
+and interpretable rationale. Each line of the generated program may invoke one
+of several off-the-shelf computer vision models, image processing routines, or
+python functions to produce intermediate outputs that may be consumed by
+subsequent parts of the program. We demonstrate the flexibility of VISPROG on 4
+diverse tasks - compositional visual question answering, zero-shot reasoning on
+image pairs, factual knowledge object tagging, and language-guided image
+editing. We believe neuro-symbolic approaches like VISPROG are an exciting
+avenue to easily and effectively expand the scope of AI systems to serve the
+long tail of complex tasks that people may wish to perform.
+"
+Self-Prompting Large Language Models for Zero-Shot Open-Domain QA,Junlong Li,http://arxiv.org/pdf/2212.08635v2.pdf,2022-12-16,"['cs.cl', 'cs.ai']",2212.08635v2.pdf,"  Open-Domain Question Answering (ODQA) aims at answering factoid questions
+without explicitly providing specific background documents. In a zero-shot
+setting, this task is more challenging since no data is available to train
+customized models like Retriever-Readers. Recently, Large Language Models
+(LLMs) like GPT-3 have shown their power in zero-shot ODQA with direct
+prompting methods, but these methods are still far from releasing the full
+powerfulness of LLMs only in an implicitly invoking way. In this paper, we
+propose a Self-Prompting framework to explicitly utilize the massive knowledge
+stored in the parameters of LLMs and their strong instruction understanding
+abilities. Concretely, we prompt LLMs step by step to generate multiple pseudo
+QA pairs with background passages and explanations from scratch and then use
+those generated elements for in-context learning. Experimental results show our
+method surpasses previous SOTA methods significantly on three widely-used ODQA
+datasets, and even achieves comparable performance with some Retriever-Reader
+models fine-tuned on full training data.
+"
+"Don't Generate, Discriminate: A Proposal for Grounding Language Models  to Real-World Environments",Yu Gu,http://arxiv.org/pdf/2212.09736v2.pdf,2022-12-19,"['cs.cl', 'cs.ai', 'i.2.7']",2212.09736v2.pdf,"  A key missing capacity of current language models (LMs) is grounding to
+real-world environments. Most existing work for grounded language understanding
+uses LMs to directly generate plans that can be executed in the environment to
+achieve the desired effects. It thereby casts the burden of ensuring
+grammaticality, faithfulness, and controllability all on the LMs. We propose
+Pangu, a generic framework for grounded language understanding that capitalizes
+on the discriminative ability of LMs instead of their generative ability. Pangu
+consists of a symbolic agent and a neural LM working in a concerted fashion:
+The agent explores the environment to incrementally construct valid plans, and
+the LM evaluates the plausibility of the candidate plans to guide the search
+process. A case study on the challenging problem of knowledge base question
+answering (KBQA), which features a massive environment, demonstrates the
+remarkable effectiveness and flexibility of Pangu: A BERT-base LM is sufficient
+for setting a new record on standard KBQA datasets, and larger LMs further
+bring substantial gains. Pangu also enables, for the first time, effective
+few-shot in-context learning for KBQA with large LMs such as Codex.
+"
+Ontologically Faithful Generation of Non-Player Character Dialogues,Nathaniel Weir,http://arxiv.org/pdf/2212.10618v2.pdf,2022-12-20,['cs.cl'],2212.10618v2.pdf,"  We introduce a language generation task grounded in a popular video game
+environment. KNUDGE (KNowledge Constrained User-NPC Dialogue GEneration)
+requires models to produce trees of dialogue between video game characters that
+accurately reflect quest and entity specifications stated in natural language.
+KNUDGE is constructed from side quest dialogues drawn directly from game data
+of Obsidian Entertainment's The Outer Worlds, leading to real-world
+complexities in generation: (1) dialogues are branching trees as opposed to
+linear chains of utterances; (2) utterances must remain faithful to the game
+lore -- character personas, backstories, and entity relationships; and (3) a
+dialogue must accurately reveal new quest details to the human player. We
+report results for a set of neural generation models using supervised and
+in-context learning techniques; we find competent performance but room for
+future work addressing the challenges of creating realistic, game-quality
+dialogues.
+"
+Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers,Chengyi Wang,http://arxiv.org/pdf/2301.02111v1.pdf,2023-01-05,"['cs.cl', 'cs.sd', 'eess.as']",2301.02111v1.pdf,"  We introduce a language modeling approach for text to speech synthesis (TTS).
+Specifically, we train a neural codec language model (called Vall-E) using
+discrete codes derived from an off-the-shelf neural audio codec model, and
+regard TTS as a conditional language modeling task rather than continuous
+signal regression as in previous work. During the pre-training stage, we scale
+up the TTS training data to 60K hours of English speech which is hundreds of
+times larger than existing systems. Vall-E emerges in-context learning
+capabilities and can be used to synthesize high-quality personalized speech
+with only a 3-second enrolled recording of an unseen speaker as an acoustic
+prompt. Experiment results show that Vall-E significantly outperforms the
+state-of-the-art zero-shot TTS system in terms of speech naturalness and
+speaker similarity. In addition, we find Vall-E could preserve the speaker's
+emotion and acoustic environment of the acoustic prompt in synthesis. See
+https://aka.ms/valle for demos of our work.
+"
+Batch Prompting: Efficient Inference with Large Language Model APIs,Zhoujun Cheng,http://arxiv.org/pdf/2301.08721v2.pdf,2023-01-19,"['cs.cl', 'cs.ai']",2301.08721v2.pdf,"  Performing inference on large volumes of samples with large language models
+(LLMs) can be computationally and financially costly in industry and real-world
+use. We propose batch prompting, a simple yet effective prompting approach that
+enables the LLM to run inference in batches, instead of one sample at a time.
+Our method reduces both token and time costs while retaining downstream
+performance. We theoretically demonstrate that under a few-shot in-context
+learning setting, the inference costs decrease almost inverse linearly with the
+number of samples in each batch. We extensively validate the effectiveness of
+batch prompting on ten datasets across commonsense QA, arithmetic reasoning,
+and NLI/NLU: batch prompting significantly~(up to 5x with six samples in batch)
+reduces the LLM (Codex) inference token and time costs while achieving better
+or comparable performance. For state-of-the-art Chat-based LLMs, e.g., GPT-3.5
+and GPT-4, we show the benefits of batch prompting also hold. Further analysis
+shows that the number of samples in each batch and the complexity of tasks
+affect its performance. Moreover, batch prompting can be applied across
+different reasoning methods using LLMs. Our code can be found at the site
+https://github.com/xlang-ai/batch-prompting.
+"
+Looped Transformers as Programmable Computers,Angeliki Giannou,http://arxiv.org/pdf/2301.13196v1.pdf,2023-01-30,"['cs.lg', 'cs.ai']",2301.13196v1.pdf,"  We present a framework for using transformer networks as universal computers
+by programming them with specific weights and placing them in a loop. Our input
+sequence acts as a punchcard, consisting of instructions and memory for data
+read/writes. We demonstrate that a constant number of encoder layers can
+emulate basic computing blocks, including embedding edit operations, non-linear
+functions, function calls, program counters, and conditional branches. Using
+these building blocks, we emulate a small instruction-set computer. This allows
+us to map iterative algorithms to programs that can be executed by a looped,
+13-layer transformer. We show how this transformer, instructed by its input,
+can emulate a basic calculator, a basic linear algebra library, and in-context
+learning algorithms that employ backpropagation. Our work highlights the
+versatility of the attention mechanism, and demonstrates that even shallow
+transformers can execute full-fledged, general-purpose programs.
+"
+Grounding Language Models to Images for Multimodal Inputs and Outputs,Jing Yu Koh,http://arxiv.org/pdf/2301.13823v4.pdf,2023-01-31,"['cs.cl', 'cs.ai', 'cs.cv', 'cs.lg']",2301.13823v4.pdf,"  We propose an efficient method to ground pretrained text-only language models
+to the visual domain, enabling them to process arbitrarily interleaved
+image-and-text data, and generate text interleaved with retrieved images. Our
+method leverages the abilities of language models learnt from large scale
+text-only pretraining, such as in-context learning and free-form text
+generation. We keep the language model frozen, and finetune input and output
+linear layers to enable cross-modality interactions. This allows our model to
+process arbitrarily interleaved image-and-text inputs, and generate free-form
+text interleaved with retrieved images. We achieve strong zero-shot performance
+on grounded tasks such as contextual image retrieval and multimodal dialogue,
+and showcase compelling interactive abilities. Our approach works with any
+off-the-shelf language model and paves the way towards an effective, general
+solution for leveraging pretrained language models in visually grounded
+settings.
+"
+ProofNet: Autoformalizing and Formally Proving Undergraduate-Level  Mathematics,Zhangir Azerbayev,http://arxiv.org/pdf/2302.12433v1.pdf,2023-02-24,"['cs.cl', 'cs.ai', 'cs.lo']",2302.12433v1.pdf,"  We introduce ProofNet, a benchmark for autoformalization and formal proving
+of undergraduate-level mathematics. The ProofNet benchmarks consists of 371
+examples, each consisting of a formal theorem statement in Lean 3, a natural
+language theorem statement, and a natural language proof. The problems are
+primarily drawn from popular undergraduate pure mathematics textbooks and cover
+topics such as real and complex analysis, linear algebra, abstract algebra, and
+topology. We intend for ProofNet to be a challenging benchmark that will drive
+progress in autoformalization and automatic theorem proving. We report baseline
+results on statement autoformalization via in-context learning. Moreover, we
+introduce two novel statement autoformalization methods: prompt retrieval and
+distilled backtranslation.
+"
+Finding Support Examples for In-Context Learning,Xiaonan Li,http://arxiv.org/pdf/2302.13539v3.pdf,2023-02-27,['cs.cl'],2302.13539v3.pdf,"  Additionally, the strong dependency among in-context examples makes it an
+NP-hard combinatorial optimization problem and enumerating all permutations is
+infeasible. Hence we propose LENS, a fiLter-thEN-Search method to tackle this
+challenge in two stages: First we filter the dataset to obtain informative
+in-context examples individually. Specifically, we propose a novel metric,
+InfoScore, to evaluate the example's in-context informativeness based on the
+language model's feedback, and further propose a progressive filtering process
+to filter out uninformative examples. Then we propose diversity-guided example
+search which iteratively refines and evaluates the selected example
+permutations, to find examples that fully depict the task. The experimental
+results show that LENS significantly outperforms a wide range of baselines.
+"
+In-Context Instruction Learning,Seonghyeon Ye,http://arxiv.org/pdf/2302.14691v1.pdf,2023-02-28,"['cs.cl', 'cs.ai']",2302.14691v1.pdf,"  Instruction learning of Large Language Models (LLMs) has enabled zero-shot
+task generalization. However, instruction learning has been predominantly
+approached as a fine-tuning problem, including instruction tuning and
+reinforcement learning from human feedback, where LLMs are multi-task
+fine-tuned on various tasks with instructions. In this paper, we present a
+surprising finding that applying in-context learning to instruction learning,
+referred to as In-Context Instruction Learning (ICIL), significantly improves
+the zero-shot task generalization performance for both pretrained and
+instruction-fine-tuned models. One of the core advantages of ICIL is that it
+uses a single fixed prompt to evaluate all tasks, which is a concatenation of
+cross-task demonstrations. In particular, we demonstrate that the most powerful
+instruction-fine-tuned baseline (text-davinci-003) also benefits from ICIL by
+9.3%, indicating that the effect of ICIL is complementary to instruction-based
+fine-tuning.
+"
+Speak Foreign Languages with Your Own Voice: Cross-Lingual Neural Codec  Language Modeling,Ziqiang Zhang,http://arxiv.org/pdf/2303.03926v1.pdf,2023-03-07,"['cs.cl', 'cs.ai', 'cs.sd', 'eess.as']",2303.03926v1.pdf,"  We propose a cross-lingual neural codec language model, VALL-E X, for
+cross-lingual speech synthesis. Specifically, we extend VALL-E and train a
+multi-lingual conditional codec language model to predict the acoustic token
+sequences of the target language speech by using both the source language
+speech and the target language text as prompts. VALL-E X inherits strong
+in-context learning capabilities and can be applied for zero-shot cross-lingual
+text-to-speech synthesis and zero-shot speech-to-speech translation tasks.
+Experimental results show that it can generate high-quality speech in the
+target language via just one speech utterance in the source language as a
+prompt while preserving the unseen speaker's voice, emotion, and acoustic
+environment. Moreover, VALL-E X effectively alleviates the foreign accent
+problems, which can be controlled by a language ID. Audio samples are available
+at \url{https://aka.ms/vallex}.
+"
+Self-planning Code Generation with Large Language Models,Xue Jiang,http://arxiv.org/pdf/2303.06689v2.pdf,2023-03-12,['cs.se'],2303.06689v2.pdf,"  Although large language models have demonstrated impressive ability in code
+generation, they are still struggling to address the complicated intent
+provided by humans. It is widely acknowledged that humans typically employ
+planning to decompose complex problems and schedule the solution steps prior to
+implementation. Thus we introduce planning into code generation to help the
+model understand complex intent and reduce the difficulty of problem solving.
+This paper proposes a self-planning code generation method with large language
+model, which consists of two phases, namely planning phase and implementation
+phase. Specifically, in the planning phase, the language model plans out the
+solution steps from the intent combined with in-context learning. Then it
+enters the implementation phase, where the model generates code step by step,
+guided by the solution steps. The effectiveness of self-planning code
+generation has been rigorously evaluated on multiple code generation datasets
+and the results have demonstrated a marked superiority over naive direct
+generation approaches with language model. The improvement in performance is
+substantial, highlighting the significance of self-planning in code generation
+tasks.
+"
+GPT is becoming a Turing machine: Here are some ways to program it,Ana Jojic,http://arxiv.org/pdf/2303.14310v1.pdf,2023-03-25,['cs.cl'],2303.14310v1.pdf,"  We demonstrate that, through appropriate prompting, GPT-3 family of models
+can be triggered to perform iterative behaviours necessary to execute (rather
+than just write or recall) programs that involve loops, including several
+popular algorithms found in computer science curricula or software developer
+interviews. We trigger execution and description of Iterations by Regimenting
+Self-Attention (IRSA) in one (or a combination) of three ways: 1) Using strong
+repetitive structure in an example of an execution path of a target program for
+one particular input, 2) Prompting with fragments of execution paths, and 3)
+Explicitly forbidding (skipping) self-attention to parts of the generated text.
+On a dynamic program execution, IRSA leads to larger accuracy gains than
+replacing the model with the much more powerful GPT-4. IRSA has promising
+applications in education, as the prompts and responses resemble student
+assignments in data structures and algorithms classes. Our findings hold
+implications for evaluating LLMs, which typically target the in-context
+learning: We show that prompts that may not even cover one full task example
+can trigger algorithmic behaviour, allowing solving problems previously thought
+of as hard for LLMs, such as logical puzzles. Consequently, prompt design plays
+an even more critical role in LLM performance than previously recognized.
+"
+When Brain-inspired AI Meets AGI,Lin Zhao,http://arxiv.org/pdf/2303.15935v1.pdf,2023-03-28,['cs.ai'],2303.15935v1.pdf,"  Artificial General Intelligence (AGI) has been a long-standing goal of
+humanity, with the aim of creating machines capable of performing any
+intellectual task that humans can do. To achieve this, AGI researchers draw
+inspiration from the human brain and seek to replicate its principles in
+intelligent machines. Brain-inspired artificial intelligence is a field that
+has emerged from this endeavor, combining insights from neuroscience,
+psychology, and computer science to develop more efficient and powerful AI
+systems. In this article, we provide a comprehensive overview of brain-inspired
+AI from the perspective of AGI. We begin with the current progress in
+brain-inspired AI and its extensive connection with AGI. We then cover the
+important characteristics for both human intelligence and AGI (e.g., scaling,
+multimodality, and reasoning). We discuss important technologies toward
+achieving AGI in current AI systems, such as in-context learning and prompt
+tuning. We also investigate the evolution of AGI systems from both algorithmic
+and infrastructural perspectives. Finally, we explore the limitations and
+future of AGI.
+"
+Larger Probes Tell a Different Story: Extending Psycholinguistic  Datasets Via In-Context Learning,Namrata Shivagunde,http://arxiv.org/pdf/2303.16445v1.pdf,2023-03-29,['cs.cl'],2303.16445v1.pdf,"  Language model probing is often used to test specific capabilities of these
+models. However, conclusions from such studies may be limited when the probing
+benchmarks are small and lack statistical power. In this work, we introduce
+new, larger datasets for negation (NEG-1500-SIMP) and role reversal (ROLE-1500)
+inspired by psycholinguistic studies. We dramatically extend existing NEG-136
+and ROLE-88 benchmarks using GPT3, increasing their size from 18 and 44
+sentence pairs to 750 each. We also create another version of extended negation
+dataset (NEG-1500-SIMP-TEMP), created using template-based generation. It
+consists of 770 sentence pairs. We evaluate 22 models on the extended datasets,
+seeing model performance dip 20-57% compared to the original smaller
+benchmarks. We observe high levels of negation sensitivity in models like BERT
+and ALBERT demonstrating that previous findings might have been skewed due to
+smaller test sets. Finally, we observe that while GPT3 has generated all the
+examples in ROLE-1500 is only able to solve 24.6% of them during probing.
+"
+Is ChatGPT a Highly Fluent Grammatical Error Correction System? A  Comprehensive Evaluation,Tao Fang,http://arxiv.org/pdf/2304.01746v1.pdf,2023-04-04,['cs.cl'],2304.01746v1.pdf,"  ChatGPT, a large-scale language model based on the advanced GPT-3.5
+architecture, has shown remarkable potential in various Natural Language
+Processing (NLP) tasks. However, there is currently a dearth of comprehensive
+study exploring its potential in the area of Grammatical Error Correction
+(GEC). To showcase its capabilities in GEC, we design zero-shot
+chain-of-thought (CoT) and few-shot CoT settings using in-context learning for
+ChatGPT. Our evaluation involves assessing ChatGPT's performance on five
+official test sets in three different languages, along with three
+document-level GEC test sets in English. Our experimental results and human
+evaluations demonstrate that ChatGPT has excellent error detection capabilities
+and can freely correct errors to make the corrected sentences very fluent,
+possibly due to its over-correction tendencies and not adhering to the
+principle of minimal edits. Additionally, its performance in non-English and
+low-resource settings highlights its potential in multilingual GEC tasks.
+However, further analysis of various types of errors at the document-level has
+shown that ChatGPT cannot effectively correct agreement, coreference, tense
+errors across sentences, and cross-sentence boundary errors.
+"
+SegGPT: Segmenting Everything In Context,Xinlong Wang,http://arxiv.org/pdf/2304.03284v1.pdf,2023-04-06,['cs.cv'],2304.03284v1.pdf,"  We present SegGPT, a generalist model for segmenting everything in context.
+We unify various segmentation tasks into a generalist in-context learning
+framework that accommodates different kinds of segmentation data by
+transforming them into the same format of images. The training of SegGPT is
+formulated as an in-context coloring problem with random color mapping for each
+data sample. The objective is to accomplish diverse tasks according to the
+context, rather than relying on specific colors. After training, SegGPT can
+perform arbitrary segmentation tasks in images or videos via in-context
+inference, such as object instance, stuff, part, contour, and text. SegGPT is
+evaluated on a broad range of tasks, including few-shot semantic segmentation,
+video object segmentation, semantic segmentation, and panoptic segmentation.
+Our results show strong capabilities in segmenting in-domain and out-of-domain
+targets, either qualitatively or quantitatively.
+"
+Extractive Summarization via ChatGPT for Faithful Summary Generation,Haopeng Zhang,http://arxiv.org/pdf/2304.04193v2.pdf,2023-04-09,['cs.cl'],2304.04193v2.pdf,"  Extractive summarization is a crucial task in natural language processing
+that aims to condense long documents into shorter versions by directly
+extracting sentences. The recent introduction of large language models has
+attracted significant interest in the NLP community due to its remarkable
+performance on a wide range of downstream tasks. This paper first presents a
+thorough evaluation of ChatGPT's performance on extractive summarization and
+compares it with traditional fine-tuning methods on various benchmark datasets.
+Our experimental analysis reveals that ChatGPT exhibits inferior extractive
+summarization performance in terms of ROUGE scores compared to existing
+supervised systems, while achieving higher performance based on LLM-based
+evaluation metrics. In addition, we explore the effectiveness of in-context
+learning and chain-of-thought reasoning for enhancing its performance.
+Furthermore, we find that applying an extract-then-generate pipeline with
+ChatGPT yields significant performance improvements over abstractive baselines
+in terms of summary faithfulness. These observations highlight potential
+directions for enhancing ChatGPT's capabilities in faithful summarization using
+two-stage approaches.
+"
+Towards Robust Prompts on Vision-Language Models,Jindong Gu,http://arxiv.org/pdf/2304.08479v1.pdf,2023-04-17,['cs.cv'],2304.08479v1.pdf,"  With the advent of vision-language models (VLMs) that can perform in-context
+and prompt-based learning, how can we design prompting approaches that robustly
+generalize to distribution shift and can be used on novel classes outside the
+support set of the prompts? In this work, we first define two types of
+robustness to distribution shift on VLMs, namely, robustness on base classes
+(the classes included in the support set of prompts) and robustness on novel
+classes. Then, we study the robustness of existing in-context learning and
+prompt learning approaches, where we find that prompt learning performs
+robustly on test images from base classes, while it does not generalize well on
+images from novel classes. We propose robust prompt learning by integrating
+multiple-scale image features into the prompt, which improves both types of
+robustness. Comprehensive experiments are conducted to study the defined
+robustness on six benchmarks and show the effectiveness of our proposal.
+"
+A Latent Space Theory for Emergent Abilities in Large Language Models,Hui Jiang,http://arxiv.org/pdf/2304.09960v3.pdf,2023-04-19,"['cs.cl', 'cs.ai', 'cs.lg']",2304.09960v3.pdf,"  Languages are not created randomly but rather to communicate information.
+There is a strong association between languages and their underlying meanings,
+resulting in a sparse joint distribution that is heavily peaked according to
+their correlations. Moreover, these peak values happen to match with the
+marginal distribution of languages due to the sparsity. With the advent of LLMs
+trained on big data and large models, we can now precisely assess the marginal
+distribution of languages, providing a convenient means of exploring the sparse
+structures in the joint distribution for effective inferences. In this paper,
+we categorize languages as either unambiguous or {\epsilon}-ambiguous and
+present quantitative results to demonstrate that the emergent abilities of
+LLMs, such as language understanding, in-context learning, chain-of-thought
+prompting, and effective instruction fine-tuning, can all be attributed to
+Bayesian inference on the sparse joint distribution of languages.
+"
+Understanding and Predicting Human Label Variation in Natural Language  Inference through Explanation,Nan-Jiang Jiang,http://arxiv.org/pdf/2304.12443v1.pdf,2023-04-24,['cs.cl'],2304.12443v1.pdf,"  Human label variation (Plank 2022), or annotation disagreement, exists in
+many natural language processing (NLP) tasks. To be robust and trusted, NLP
+models need to identify such variation and be able to explain it. To this end,
+we created the first ecologically valid explanation dataset with diverse
+reasoning, LiveNLI. LiveNLI contains annotators' highlights and free-text
+explanations for the label(s) of their choice for 122 English Natural Language
+Inference items, each with at least 10 annotations. We used its explanations
+for chain-of-thought prompting, and found there is still room for improvement
+in GPT-3's ability to predict label distribution with in-context learning.
+"
+"Stance Detection With Supervised, Zero-Shot, and Few-Shot Applications",Michael Burnham,http://arxiv.org/pdf/2305.01723v1.pdf,2023-05-02,['cs.cl'],2305.01723v1.pdf,"  Stance detection is the identification of an author's beliefs about a subject
+from a document. Researchers widely rely on sentiment analysis to accomplish
+this. However, recent research has show that sentiment analysis is only loosely
+correlated with stance, if at all. This paper advances methods in text analysis
+by precisely defining the task of stance detection, providing a generalized
+framework for the task, and then presenting three distinct approaches for
+performing stance detection: supervised classification, zero-shot
+classification with NLI classifiers, and in-context learning. In doing so, I
+demonstrate how zero-shot and few-shot language classifiers can replace human
+labelers for a variety of tasks and discuss how their application and
+limitations differ from supervised classifiers. Finally, I demonstrate an
+application of zero-shot stance detection by replicating Block Jr et al.
+(2022).
+"
+WangLab at MEDIQA-Chat 2023: Clinical Note Generation from  Doctor-Patient Conversations using Large Language Models,John Giorgi,http://arxiv.org/pdf/2305.02220v2.pdf,2023-05-03,"['cs.cl', 'cs.ai', 'cs.lg']",2305.02220v2.pdf,"  This paper describes our submission to the MEDIQA-Chat 2023 shared task for
+automatic clinical note generation from doctor-patient conversations. We report
+results for two approaches: the first fine-tunes a pre-trained language model
+(PLM) on the shared task data, and the second uses few-shot in-context learning
+(ICL) with a large language model (LLM). Both achieve high performance as
+measured by automatic metrics (e.g. ROUGE, BERTScore) and ranked second and
+first, respectively, of all submissions to the shared task. Expert human
+scrutiny indicates that notes generated via the ICL-based approach with GPT-4
+are preferred about as often as human-written notes, making it a promising path
+toward automated note generation from doctor-patient conversations.
+"
+Otter: A Multi-Modal Model with In-Context Instruction Tuning,Bo Li,http://arxiv.org/pdf/2305.03726v1.pdf,2023-05-05,"['cs.cv', 'cs.cl']",2305.03726v1.pdf,"  Large language models (LLMs) have demonstrated significant universal
+capabilities as few/zero-shot learners in various tasks due to their
+pre-training on vast amounts of text data, as exemplified by GPT-3, which
+boosted to InstrctGPT and ChatGPT, effectively following natural language
+instructions to accomplish real-world tasks. In this paper, we propose to
+introduce instruction tuning into multi-modal models, motivated by the Flamingo
+model's upstream interleaved format pretraining dataset. We adopt a similar
+approach to construct our MultI-Modal In-Context Instruction Tuning (MIMIC-IT)
+dataset. We then introduce Otter, a multi-modal model based on OpenFlamingo
+(open-sourced version of DeepMind's Flamingo), trained on MIMIC-IT and
+showcasing improved instruction-following ability and in-context learning. We
+also optimize OpenFlamingo's implementation for researchers, democratizing the
+required training resources from 1$\times$ A100 GPU to 4$\times$ RTX-3090 GPUs,
+and integrate both OpenFlamingo and Otter into Huggingface Transformers for
+more researchers to incorporate the models into their customized training and
+inference pipelines.
+"
+How Good are Commercial Large Language Models on African Languages?,Jessica Ojo,http://arxiv.org/pdf/2305.06530v1.pdf,2023-05-11,"['cs.cl', 'cs.ai', 'cs.lg']",2305.06530v1.pdf,"  Recent advancements in Natural Language Processing (NLP) has led to the
+proliferation of large pretrained language models. These models have been shown
+to yield good performance, using in-context learning, even on unseen tasks and
+languages. They have also been exposed as commercial APIs as a form of
+language-model-as-a-service, with great adoption. However, their performance on
+African languages is largely unknown. We present a preliminary analysis of
+commercial large language models on two tasks (machine translation and text
+classification) across eight African languages, spanning different language
+families and geographical areas. Our results suggest that commercial language
+models produce below-par performance on African languages. We also find that
+they perform better on text classification than machine translation. In
+general, our findings present a call-to-action to ensure African languages are
+well represented in commercial large language models, given their growing
+popularity.
+"
+Chain-of-Dictionary Prompting Elicits Translation in Large Language  Models,Hongyuan Lu,http://arxiv.org/pdf/2305.06575v3.pdf,2023-05-11,['cs.cl'],2305.06575v3.pdf,"  Large language models (LLMs) have shown surprisingly good performance in
+multilingual neural machine translation (MNMT) even when trained without
+parallel data. Yet, despite the fact that the amount of training data is
+gigantic, they still struggle with translating rare words, particularly for
+low-resource languages. Even worse, it is usually unrealistic to retrieve
+relevant demonstrations for in-context learning with low-resource languages on
+LLMs, which restricts the practical use of LLMs for translation -- how should
+we mitigate this problem? To this end, we present a novel method, CoD, which
+augments LLMs with prior knowledge with the chains of multilingual dictionaries
+for a subset of input words to elicit translation abilities for LLMs. Extensive
+experiments indicate that augmenting ChatGPT with CoD elicits large gains by up
+to 13x chrF++ points for MNMT (3.08 to 42.63 for English to Serbian written in
+Cyrillic script) on FLORES-200 full devtest set. We further demonstrate the
+importance of chaining the multilingual dictionaries, as well as the
+superiority of CoD to few-shot demonstration for low-resource languages.
+"
+Is ChatGPT a Good Causal Reasoner? A Comprehensive Evaluation,Jinglong Gao,http://arxiv.org/pdf/2305.07375v4.pdf,2023-05-12,"['cs.cl', 'cs.ai']",2305.07375v4.pdf,"  Causal reasoning ability is crucial for numerous NLP applications. Despite
+the impressive emerging ability of ChatGPT in various NLP tasks, it is unclear
+how well ChatGPT performs in causal reasoning. In this paper, we conduct the
+first comprehensive evaluation of the ChatGPT's causal reasoning capabilities.
+Experiments show that ChatGPT is not a good causal reasoner, but a good causal
+explainer. Besides, ChatGPT has a serious hallucination on causal reasoning,
+possibly due to the reporting biases between causal and non-causal
+relationships in natural language, as well as ChatGPT's upgrading processes,
+such as RLHF. The In-Context Learning (ICL) and Chain-of-Thought (CoT)
+techniques can further exacerbate such causal hallucination. Additionally, the
+causal reasoning ability of ChatGPT is sensitive to the words used to express
+the causal concept in prompts, and close-ended prompts perform better than
+open-ended prompts. For events in sentences, ChatGPT excels at capturing
+explicit causality rather than implicit causality, and performs better in
+sentences with lower event density and smaller lexical distance between events.
+The code is available on https://github.com/ArrogantL/ChatGPT4CausalReasoning .
+"
+AutoTrial: Prompting Language Models for Clinical Trial Design,Zifeng Wang,http://arxiv.org/pdf/2305.11366v2.pdf,2023-05-19,['cs.cl'],2305.11366v2.pdf,"  Clinical trials are critical for drug development. Constructing the
+appropriate eligibility criteria (i.e., the inclusion/exclusion criteria for
+patient recruitment) is essential for the trial's success. Proper design of
+clinical trial protocols should consider similar precedent trials and their
+eligibility criteria to ensure sufficient patient coverage. In this paper, we
+present a method named AutoTrial to aid the design of clinical eligibility
+criteria using language models. It allows (1) controllable generation under
+instructions via a hybrid of discrete and neural prompting, (2) scalable
+knowledge incorporation via in-context learning, and (3) explicit reasoning
+chains to provide rationales for understanding the outputs. Experiments on over
+70K clinical trials verify that AutoTrial generates high-quality criteria texts
+that are fluent and coherent and with high accuracy in capturing the relevant
+clinical concepts to the target trial. It is noteworthy that our method, with a
+much smaller parameter size, gains around 60% winning rate against the GPT-3.5
+baselines via human evaluations.
+"
+Cross-Lingual Supervision improves Large Language Models Pre-training,Andrea Schioppa,http://arxiv.org/pdf/2305.11778v1.pdf,2023-05-19,"['cs.cl', 'cs.lg']",2305.11778v1.pdf,"  The recent rapid progress in pre-training Large Language Models has relied on
+using self-supervised language modeling objectives like next token prediction
+or span corruption. On the other hand, Machine Translation Systems are mostly
+trained using cross-lingual supervision that requires aligned data between
+source and target languages. We demonstrate that pre-training Large Language
+Models on a mixture of a self-supervised Language Modeling objective and the
+supervised Machine Translation objective, therefore including cross-lingual
+parallel data during pre-training, yields models with better in-context
+learning abilities. As pre-training is a very resource-intensive process and a
+grid search on the best mixing ratio between the two objectives is
+prohibitively expensive, we propose a simple yet effective strategy to learn it
+during pre-training.
+"
+"How to Prompt LLMs for Text-to-SQL: A Study in Zero-shot, Single-domain,  and Cross-domain Settings",Shuaichen Chang,http://arxiv.org/pdf/2305.11853v2.pdf,2023-05-19,['cs.cl'],2305.11853v2.pdf,"  Large language models (LLMs) with in-context learning have demonstrated
+remarkable capability in the text-to-SQL task. Previous research has prompted
+LLMs with various demonstration-retrieval strategies and intermediate reasoning
+steps to enhance the performance of LLMs. However, those works often employ
+varied strategies when constructing the prompt text for text-to-SQL inputs,
+such as databases and demonstration examples. This leads to a lack of
+comparability in both the prompt constructions and their primary contributions.
+Furthermore, selecting an effective prompt construction has emerged as a
+persistent problem for future research. To address this limitation, we
+comprehensively investigate the impact of prompt constructions across various
+settings and provide insights for future work.
+"
+Fact-Checking Complex Claims with Program-Guided Reasoning,Liangming Pan,http://arxiv.org/pdf/2305.12744v1.pdf,2023-05-22,"['cs.cl', 'cs.ai']",2305.12744v1.pdf,"  Fact-checking real-world claims often requires collecting multiple pieces of
+evidence and applying complex multi-step reasoning. In this paper, we present
+Program-Guided Fact-Checking (ProgramFC), a novel fact-checking model that
+decomposes complex claims into simpler sub-tasks that can be solved using a
+shared library of specialized functions. We first leverage the in-context
+learning ability of large language models to generate reasoning programs to
+guide the verification process. Afterward, we execute the program by delegating
+each sub-task to the corresponding sub-task handler. This process makes our
+model both explanatory and data-efficient, providing clear explanations of its
+reasoning process and requiring minimal training data. We evaluate ProgramFC on
+two challenging fact-checking datasets and show that it outperforms seven
+fact-checking baselines across different settings of evidence availability,
+with explicit output programs that benefit human debugging. Our codes and data
+are publicly available at https://github.com/mbzuai-nlp/ProgramFC.
+"
+ExplainCPE: A Free-text Explanation Benchmark of Chinese Pharmacist  Examination,Dongfang Li,http://arxiv.org/pdf/2305.12945v2.pdf,2023-05-22,['cs.cl'],2305.12945v2.pdf,"  As ChatGPT and GPT-4 spearhead the development of Large Language Models
+(LLMs), more researchers are investigating their performance across various
+tasks. But more research needs to be done on the interpretability capabilities
+of LLMs, that is, the ability to generate reasons after an answer has been
+given. Existing explanation datasets are mostly English-language general
+knowledge questions, which leads to insufficient thematic and linguistic
+diversity. To address the language bias and lack of medical resources in
+generating rationales QA datasets, we present ExplainCPE (over 7k instances), a
+challenging medical benchmark in Simplified Chinese. We analyzed the errors of
+ChatGPT and GPT-4, pointing out the limitations of current LLMs in
+understanding text and computational reasoning. During the experiment, we also
+found that different LLMs have different preferences for in-context learning.
+ExplainCPE presents a significant challenge, but its potential for further
+investigation is promising, and it can be used to evaluate the ability of a
+model to generate explanations. AI safety and trustworthiness need more
+attention, and this work makes the first step to explore the medical
+interpretability of LLMs.The dataset is available at
+https://github.com/HITsz-TMG/ExplainCPE.
+"
+MAILEX: Email Event and Argument Extraction,Saurabh Srivastava,http://arxiv.org/pdf/2305.13469v2.pdf,2023-05-22,"['cs.cl', 'cs.ai']",2305.13469v2.pdf,"  In this work, we present the first dataset, MailEx, for performing event
+extraction from conversational email threads. To this end, we first proposed a
+new taxonomy covering 10 event types and 76 arguments in the email domain. Our
+final dataset includes 1.5K email threads and ~4K emails, which are annotated
+with totally ~8K event instances. To understand the task challenges, we
+conducted a series of experiments comparing three types of approaches, i.e.,
+fine-tuned sequence labeling, fine-tuned generative extraction, and few-shot
+in-context learning. Our results showed that the task of email event extraction
+is far from being addressed, due to challenges lying in, e.g., extracting
+non-continuous, shared trigger spans, extracting non-named entity arguments,
+and modeling the email conversational history. Our work thus suggests more
+future investigations in this domain-specific event extraction task.
+"
+Can ChatGPT Detect Intent? Evaluating Large Language Models for Spoken  Language Understanding,Mutian He,http://arxiv.org/pdf/2305.13512v2.pdf,2023-05-22,"['cs.cl', 'cs.ai', 'cs.sd', 'eess.as']",2305.13512v2.pdf,"  Recently, large pretrained language models have demonstrated strong language
+understanding capabilities. This is particularly reflected in their zero-shot
+and in-context learning abilities on downstream tasks through prompting. To
+assess their impact on spoken language understanding (SLU), we evaluate several
+such models like ChatGPT and OPT of different sizes on multiple benchmarks. We
+verify the emergent ability unique to the largest models as they can reach
+intent classification accuracy close to that of supervised models with zero or
+few shots on various languages given oracle transcripts. By contrast, the
+results for smaller models fitting a single GPU fall far behind. We note that
+the error cases often arise from the annotation scheme of the dataset;
+responses from ChatGPT are still reasonable. We show, however, that the model
+is worse at slot filling, and its performance is sensitive to ASR errors,
+suggesting serious challenges for the application of those textual models on
+SLU.
+"
+LogicLLM: Exploring Self-supervised Logic-enhanced Training for Large  Language Models,Fangkai Jiao,http://arxiv.org/pdf/2305.13718v2.pdf,2023-05-23,['cs.cl'],2305.13718v2.pdf,"  Existing efforts to improve logical reasoning ability of language models have
+predominantly relied on supervised fine-tuning, hindering generalization to new
+domains and/or tasks. The development of Large Langauge Models (LLMs) has
+demonstrated the capacity of compressing abundant knowledge into a single
+proxy, enabling them to tackle multiple tasks effectively. Our preliminary
+experiments, nevertheless, show that LLMs do not show capability on logical
+reasoning. The performance of LLMs on logical reasoning benchmarks is far
+behind the existing state-of-the-art baselines. In this paper, we make the
+first attempt to investigate the feasibility of incorporating logical knowledge
+through self-supervised post-training, and activating it via in-context
+learning, which we termed as LogicLLM. Specifically, we devise an
+auto-regressive objective variant of MERIt and integrate it with two LLM
+series, i.e., FLAN-T5 and LLaMA, with parameter size ranging from 3 billion to
+13 billion. The results on two challenging logical reasoning benchmarks
+demonstrate the effectiveness of LogicLLM. Besides, we conduct extensive
+ablation studies to analyze the key factors in designing logic-oriented proxy
+tasks.
+"
+Make a Choice! Knowledge Base Question Answering with In-Context  Learning,Chuanyuan Tan,http://arxiv.org/pdf/2305.13972v1.pdf,2023-05-23,['cs.cl'],2305.13972v1.pdf,"  Question answering over knowledge bases (KBQA) aims to answer factoid
+questions with a given knowledge base (KB). Due to the large scale of KB,
+annotated data is impossible to cover all fact schemas in KB, which poses a
+challenge to the generalization ability of methods that require a sufficient
+amount of annotated data. Recently, LLMs have shown strong few-shot performance
+in many NLP tasks. We expect LLM can help existing methods improve their
+generalization ability, especially in low-resource situations. In this paper,
+we present McL-KBQA, a framework that incorporates the few-shot ability of LLM
+into the KBQA method via ICL-based multiple choice and then improves the
+effectiveness of the QA tasks. Experimental results on two KBQA datasets
+demonstrate the competitive performance of McL-KBQA with strong improvements in
+generalization. We expect to explore a new way to QA tasks from KBQA in
+conjunction with LLM, how to generate answers normatively and correctly with
+strong generalization.
+"
+CTQScorer: Combining Multiple Features for In-context Example Selection  for Machine Translation,Aswanth Kumar,http://arxiv.org/pdf/2305.14105v2.pdf,2023-05-23,"['cs.cl', 'cs.ai']",2305.14105v2.pdf,"  Large language models have demonstrated the capability to perform on machine
+translation when the input is prompted with a few examples (in-context
+learning). Translation quality depends on various features of the selected
+examples, such as their quality and relevance, but previous work has
+predominantly focused on individual features in isolation. In this paper, we
+propose a general framework for combining different features influencing
+example selection. We learn a regression model, CTQ Scorer (Contextual
+Translation Quality), that selects examples based on multiple features in order
+to maximize the translation quality. On multiple language pairs and language
+models, we show that CTQ Scorer helps significantly outperform random selection
+as well as strong single-factor baselines reported in the literature. We also
+see an improvement of over 2.5 COMET points on average with respect to a strong
+BM25 retrieval-based baseline.
+"
+Empowering LLM-based Machine Translation with Cultural Awareness,Binwei Yao,http://arxiv.org/pdf/2305.14328v1.pdf,2023-05-23,['cs.cl'],2305.14328v1.pdf,"  Traditional neural machine translation (NMT) systems often fail to translate
+sentences that contain culturally specific information. Most previous NMT
+methods have incorporated external cultural knowledge during training, which
+requires fine-tuning on low-frequency items specific to the culture. Recent
+in-context learning utilizes lightweight prompts to guide large language models
+(LLMs) to perform machine translation, however, whether such an approach works
+in terms of injecting culture awareness into machine translation remains
+unclear. To this end, we introduce a new data curation pipeline to construct a
+culturally relevant parallel corpus, enriched with annotations of
+cultural-specific entities. Additionally, we design simple but effective
+prompting strategies to assist this LLM-based translation. Extensive
+experiments show that our approaches can largely help incorporate cultural
+knowledge into LLM-based machine translation, outperforming traditional NMT
+systems in translating cultural-specific sentences.
+"
+Self-Checker: Plug-and-Play Modules for Fact-Checking with Large  Language Models,Miaoran Li,http://arxiv.org/pdf/2305.14623v1.pdf,2023-05-24,['cs.cl'],2305.14623v1.pdf,"  Fact-checking is an essential task in NLP that is commonly utilized for
+validating the factual accuracy of claims. Prior work has mainly focused on
+fine-tuning pre-trained languages models on specific datasets, which can be
+computationally intensive and time-consuming. With the rapid development of
+large language models (LLMs), such as ChatGPT and GPT-3, researchers are now
+exploring their in-context learning capabilities for a wide range of tasks. In
+this paper, we aim to assess the capacity of LLMs for fact-checking by
+introducing Self-Checker, a framework comprising a set of plug-and-play modules
+that facilitate fact-checking by purely prompting LLMs in an almost zero-shot
+setting. This framework provides a fast and efficient way to construct
+fact-checking systems in low-resource environments. Empirical results
+demonstrate the potential of Self-Checker in utilizing LLMs for fact-checking.
+However, there is still significant room for improvement compared to SOTA
+fine-tuned models, which suggests that LLM adoption could be a promising
+approach for future fact-checking research.
+"
+ExpertPrompting: Instructing Large Language Models to be Distinguished  Experts,Benfeng Xu,http://arxiv.org/pdf/2305.14688v1.pdf,2023-05-24,"['cs.cl', 'cs.ai']",2305.14688v1.pdf,"  The answering quality of an aligned large language model (LLM) can be
+drastically improved if treated with proper crafting of prompts. In this paper,
+we propose ExpertPrompting to elicit the potential of LLMs to answer as
+distinguished experts. We first utilize In-Context Learning to automatically
+synthesize detailed and customized descriptions of the expert identity for each
+specific instruction, and then ask LLMs to provide answer conditioned on such
+agent background. Based on this augmented prompting strategy, we produce a new
+set of instruction-following data using GPT-3.5, and train a competitive
+open-source chat assistant called ExpertLLaMA. We employ GPT4-based evaluation
+to show that 1) the expert data is of significantly higher quality than vanilla
+answers, and 2) ExpertLLaMA outperforms existing open-source opponents and
+achieves 96\% of the original ChatGPT's capability. All data and the
+ExpertLLaMA model will be made publicly available at
+\url{https://github.com/OFA-Sys/ExpertLLaMA}.
+"
+Adapting Language Models to Compress Contexts,Alexis Chevalier,http://arxiv.org/pdf/2305.14788v2.pdf,2023-05-24,['cs.cl'],2305.14788v2.pdf,"  Transformer-based language models (LMs) are powerful and widely-applicable
+tools, but their usefulness is constrained by a finite context window and the
+expensive computational cost of processing long text documents. We propose to
+adapt pre-trained LMs into AutoCompressors. These language models are capable
+of compressing long contexts into compact summary vectors, which are then
+accessible to the model as soft prompts. Summary vectors are trained with an
+unsupervised objective, whereby long documents are processed in segments, and
+summary vectors from all previous segments are used in language modeling. We
+fine-tune OPT and Llama-2 models on sequences of up to 30,720 tokens and show
+that AutoCompressors can utilize long contexts to improve perplexity. We
+evaluate AutoCompressors on in-context learning by compressing task
+demonstrations and find that summary vectors are good substitutes for
+plain-text demonstrations, increasing accuracy while reducing inference costs.
+Finally, we explore the benefits of pre-computing summary vectors for large
+corpora by applying summary vectors to retrievalaugmented language modeling and
+a passage re-ranking task. Overall, AutoCompressors emerge as a simple and
+inexpensive solution to extend the context window of LMs while speeding up
+inference over long contexts.
+"
+ByteSized32: A Corpus and Challenge Task for Generating Task-Specific  World Models Expressed as Text Games,Ruoyao Wang,http://arxiv.org/pdf/2305.14879v2.pdf,2023-05-24,"['cs.cl', 'cs.ai']",2305.14879v2.pdf,"  In this work, we investigate the capacity of language models to generate
+explicit, interpretable, and interactive world models of scientific and
+common-sense reasoning tasks. We operationalize this as a task of generating
+text games, expressed as hundreds of lines of Python code. To facilitate this
+task, we introduce ByteSized32 (Code: github.com/cognitiveailab/BYTESIZED32), a
+corpus of 32 reasoning-focused text games totaling 20k lines of Python code. We
+empirically demonstrate that GPT-4 can use these games as templates for
+single-shot in-context learning, successfully producing runnable games on
+unseen topics in 28% of cases. When allowed to self-reflect on program errors,
+game runnability substantially increases to 57%. While evaluating simulation
+fidelity is labor-intensive, we introduce a suite of automated metrics to
+assess game fidelity, technical validity, adherence to task specifications, and
+winnability, showing a high degree of agreement with expert human ratings. We
+pose this as a challenge task to spur further development at the juncture of
+world modeling and code generation.
+"
+Getting Sick After Seeing a Doctor? Diagnosing and Mitigating Knowledge  Conflicts in Event Temporal Reasoning,Tianqing Fang,http://arxiv.org/pdf/2305.14970v1.pdf,2023-05-24,"['cs.cl', 'cs.ai']",2305.14970v1.pdf,"  Event temporal reasoning aims at identifying the temporal relations between
+two or more events. However, knowledge conflicts arise when there is a mismatch
+between the actual temporal relations of events in the context and the prior
+knowledge or biases learned by the model. We first systematically define
+distinct kinds of bias in event temporal reasoning, which include event
+relation prior bias, tense bias, narrative bias, and dependency bias, as
+indicators to study knowledge conflicts. To mitigate such event-related
+knowledge conflict, we introduce a Counterfactual Data Augmentation based
+method that can be applied to both Pre-trained Language Models (PLMs) and Large
+Language Models (LLMs) either as additional training data or demonstrations for
+In-Context Learning. Experiments suggest the importance of mitigating knowledge
+conflicts in event temporal reasoning tasks for reducing hallucination and
+highlight the potential of counterfactual data augmentation for improving model
+performance.
+"
+Boosting Cross-lingual Transferability in Multilingual Models via  In-Context Learning,Sunkyoung Kim,http://arxiv.org/pdf/2305.15233v1.pdf,2023-05-24,"['cs.cl', 'cs.ai']",2305.15233v1.pdf,"  Existing cross-lingual transfer (CLT) prompting methods are only concerned
+with monolingual demonstration examples in the source language. In this paper,
+we propose In-CLT, a novel cross-lingual transfer prompting method that
+leverages both source and target languages to construct the demonstration
+examples. We conduct comprehensive evaluations on multilingual benchmarks,
+focusing on question answering tasks. Experiment results show that In-CLT
+prompt not only improves multilingual models' cross-lingual transferability,
+but also demonstrates remarkable unseen language generalization ability. In-CLT
+prompting, in particular, improves model performance by 10 to 20\% points on
+average when compared to prior cross-lingual transfer approaches. We also
+observe the surprising performance gain on the other multilingual benchmarks,
+especially in reasoning tasks. Furthermore, we investigate the relationship
+between lexical similarity and pre-training corpora in terms of the
+cross-lingual transfer gap.
+"
+A Mechanism for Solving Relational Tasks in Transformer Language Models,Jack Merullo,http://arxiv.org/pdf/2305.16130v2.pdf,2023-05-25,"['cs.cl', 'cs.lg']",2305.16130v2.pdf,"  A primary criticism towards language models (LMs) is their inscrutability.
+This paper presents evidence that, despite their size and complexity, LMs
+sometimes exploit a simple computational mechanism to solve one-to-one
+relational tasks (e.g., capital_of(Poland)=Warsaw). We investigate a range of
+language model sizes (from 124M parameters to 176B parameters) in an in-context
+learning setting, and find that for a variety of tasks (involving capital
+cities, upper-casing, and past-tensing) a key part of the mechanism reduces to
+a simple linear update typically applied by the feedforward (FFN) networks.
+These updates also tend to promote the output of the relation in a
+content-independent way (e.g., encoding Poland:Warsaw::China:Beijing),
+revealing a predictable pattern that these models take in solving these tasks.
+We further show that this mechanism is specific to tasks that require retrieval
+from pretraining memory, rather than retrieval from local context. Our results
+contribute to a growing body of work on the mechanistic interpretability of
+LLMs, and offer reason to be optimistic that, despite the massive and
+non-linear nature of the models, the strategies they ultimately use to solve
+tasks can sometimes reduce to familiar and even intuitive algorithms.
+"
+Large Language Models Are Partially Primed in Pronoun Interpretation,Suet-Ying Lam,http://arxiv.org/pdf/2305.16917v1.pdf,2023-05-26,['cs.cl'],2305.16917v1.pdf,"  While a large body of literature suggests that large language models (LLMs)
+acquire rich linguistic representations, little is known about whether they
+adapt to linguistic biases in a human-like way. The present study probes this
+question by asking whether LLMs display human-like referential biases using
+stimuli and procedures from real psycholinguistic experiments. Recent
+psycholinguistic studies suggest that humans adapt their referential biases
+with recent exposure to referential patterns; closely replicating three
+relevant psycholinguistic experiments from Johnson & Arnold (2022) in an
+in-context learning (ICL) framework, we found that InstructGPT adapts its
+pronominal interpretations in response to the frequency of referential patterns
+in the local discourse, though in a limited fashion: adaptation was only
+observed relative to syntactic but not semantic biases. By contrast, FLAN-UL2
+fails to generate meaningful patterns. Our results provide further evidence
+that contemporary LLMs discourse representations are sensitive to syntactic
+patterns in the local context but less so to semantic patterns. Our data and
+code are available at \url{https://github.com/zkx06111/llm_priming}.
+"
+A Mechanism for Sample-Efficient In-Context Learning for Sparse  Retrieval Tasks,Jacob Abernethy,http://arxiv.org/pdf/2305.17040v1.pdf,2023-05-26,"['cs.lg', 'cs.cl']",2305.17040v1.pdf,"  We study the phenomenon of \textit{in-context learning} (ICL) exhibited by
+large language models, where they can adapt to a new learning task, given a
+handful of labeled examples, without any explicit parameter optimization. Our
+goal is to explain how a pre-trained transformer model is able to perform ICL
+under reasonable assumptions on the pre-training process and the downstream
+tasks. We posit a mechanism whereby a transformer can achieve the following:
+(a) receive an i.i.d. sequence of examples which have been converted into a
+prompt using potentially-ambiguous delimiters, (b) correctly segment the prompt
+into examples and labels, (c) infer from the data a \textit{sparse linear
+regressor} hypothesis, and finally (d) apply this hypothesis on the given test
+example and return a predicted label. We establish that this entire procedure
+is implementable using the transformer mechanism, and we give sample complexity
+guarantees for this learning framework. Our empirical findings validate the
+challenge of segmentation, and we show a correspondence between our posited
+mechanisms and observed attention maps for step (c).
+"
+Augmenting Large Language Model Translators via Translation Memories,Yongyu Mu,http://arxiv.org/pdf/2305.17367v1.pdf,2023-05-27,['cs.cl'],2305.17367v1.pdf,"  Using translation memories (TMs) as prompts is a promising approach to
+in-context learning of machine translation models. In this work, we take a step
+towards prompting large language models (LLMs) with TMs and making them better
+translators. We find that the ability of LLMs to ``understand'' prompts is
+indeed helpful for making better use of TMs. Experiments show that the results
+of a pre-trained LLM translator can be greatly improved by using high-quality
+TM-based prompts. These results are even comparable to those of the
+state-of-the-art NMT systems which have access to large-scale in-domain
+bilingual data and are well tuned on the downstream tasks.
+"
+In-Context Analogical Reasoning with Pre-Trained Language Models,Xiaoyang Hu,http://arxiv.org/pdf/2305.17626v2.pdf,2023-05-28,"['cs.ai', 'cs.cl', 'cs.lg']",2305.17626v2.pdf,"  Analogical reasoning is a fundamental capacity of human cognition that allows
+us to reason abstractly about novel situations by relating them to past
+experiences. While it is thought to be essential for robust reasoning in AI
+systems, conventional approaches require significant training and/or
+hard-coding of domain knowledge to be applied to benchmark tasks. Inspired by
+cognitive science research that has found connections between human language
+and analogy-making, we explore the use of intuitive language-based abstractions
+to support analogy in AI systems. Specifically, we apply large pre-trained
+language models (PLMs) to visual Raven's Progressive Matrices (RPM), a common
+relational reasoning test. By simply encoding the perceptual features of the
+problem into language form, we find that PLMs exhibit a striking capacity for
+zero-shot relational reasoning, exceeding human performance and nearing
+supervised vision-based methods. We explore different encodings that vary the
+level of abstraction over task features, finding that higher-level abstractions
+further strengthen PLMs' analogical reasoning. Our detailed analysis reveals
+insights on the role of model complexity, in-context learning, and prior
+knowledge in solving RPM tasks.
+"
+Towards Explainable Conversational Recommender Systems,Shuyu Guo,http://arxiv.org/pdf/2305.18363v1.pdf,2023-05-27,"['cs.ir', 'cs.ai']",2305.18363v1.pdf,"  Explanations in conventional recommender systems have demonstrated benefits
+in helping the user understand the rationality of the recommendations and
+improving the system's efficiency, transparency, and trustworthiness. In the
+conversational environment, multiple contextualized explanations need to be
+generated, which poses further challenges for explanations. To better measure
+explainability in conversational recommender systems (CRS), we propose ten
+evaluation perspectives based on concepts from conventional recommender systems
+together with the characteristics of CRS. We assess five existing CRS benchmark
+datasets using these metrics and observe the necessity of improving the
+explanation quality of CRS. To achieve this, we conduct manual and automatic
+approaches to extend these dialogues and construct a new CRS dataset, namely
+Explainable Recommendation Dialogues (E-ReDial). It includes 756 dialogues with
+over 2,000 high-quality rewritten explanations. We compare two baseline
+approaches to perform explanation generation based on E-ReDial. Experimental
+results suggest that models trained on E-ReDial can significantly improve
+explainability while introducing knowledge into the models can further improve
+the performance. GPT-3 in the in-context learning setting can generate more
+realistic and diverse movie descriptions. In contrast, T5 training on E-ReDial
+can better generate clear reasons for recommendations based on user
+preferences. E-ReDial is available at https://github.com/Superbooming/E-ReDial.
+"
+Grammar Prompting for Domain-Specific Language Generation with Large  Language Models,Bailin Wang,http://arxiv.org/pdf/2305.19234v3.pdf,2023-05-30,"['cs.cl', 'cs.ai']",2305.19234v3.pdf,"  Large language models (LLMs) can learn to perform a wide range of natural
+language tasks from just a handful of in-context examples. However, for
+generating strings from highly structured languages (e.g., semantic parsing to
+complex domain-specific languages), it is challenging for the LLM to generalize
+from just a few exemplars. We propose \emph{grammar prompting}, a simple
+approach to enable LLMs to use external knowledge and domain-specific
+constraints, expressed through a grammar in Backus--Naur Form (BNF), during
+in-context learning. Grammar prompting augments each demonstration example with
+a specialized grammar that is minimally sufficient for generating the
+particular output example, where the specialized grammar is a subset of the
+full DSL grammar. For inference, the LLM first predicts a BNF grammar given a
+test input, and then generates the output according to the rules of the
+grammar. Experiments demonstrate that grammar prompting can enable LLMs to
+perform competitively on a diverse set of DSL generation tasks, including
+semantic parsing (SMCalFlow, Overnight, GeoQuery), PDDL planning, and
+SMILES-based molecule generation.
+"
+Contextual Vision Transformers for Robust Representation Learning,Yujia Bao,http://arxiv.org/pdf/2305.19402v2.pdf,2023-05-30,"['cs.cv', 'cs.ai', 'cs.cl']",2305.19402v2.pdf,"  We introduce Contextual Vision Transformers (ContextViT), a method designed
+to generate robust image representations for datasets experiencing shifts in
+latent factors across various groups. Derived from the concept of in-context
+learning, ContextViT incorporates an additional context token to encapsulate
+group-specific information. This integration allows the model to adjust the
+image representation in accordance with the group-specific context.
+Specifically, for a given input image, ContextViT maps images with identical
+group membership into this context token, which is appended to the input image
+tokens. Additionally, we introduce a context inference network to predict such
+tokens on-the-fly, given a batch of samples from the group. This enables
+ContextViT to adapt to new testing distributions during inference time. We
+demonstrate the efficacy of ContextViT across a wide range of applications. In
+supervised fine-tuning, we show that augmenting pre-trained ViTs with our
+proposed context conditioning mechanism results in consistent improvements in
+out-of-distribution generalization on iWildCam and FMoW. We also investigate
+self-supervised representation learning with ContextViT. Our experiments on the
+Camelyon17 pathology imaging benchmark and the JUMP-CP microscopy imaging
+benchmark demonstrate that ContextViT excels in learning stable image
+featurizations amidst distribution shift, consistently outperforming its ViT
+counterpart.
+"
+Self-Verification Improves Few-Shot Clinical Information Extraction,Zelalem Gero,http://arxiv.org/pdf/2306.00024v1.pdf,2023-05-30,"['cs.cl', 'cs.lg']",2306.00024v1.pdf,"  Extracting patient information from unstructured text is a critical task in
+health decision-support and clinical research. Large language models (LLMs)
+have shown the potential to accelerate clinical curation via few-shot
+in-context learning, in contrast to supervised learning which requires much
+more costly human annotations. However, despite drastic advances in modern LLMs
+such as GPT-4, they still struggle with issues regarding accuracy and
+interpretability, especially in mission-critical domains such as health. Here,
+we explore a general mitigation framework using self-verification, which
+leverages the LLM to provide provenance for its own extraction and check its
+own outputs. This is made possible by the asymmetry between verification and
+generation, where the latter is often much easier than the former. Experimental
+results show that our method consistently improves accuracy for various LLMs in
+standard clinical information extraction tasks. Additionally, self-verification
+yields interpretations in the form of a short text span corresponding to each
+output, which makes it very efficient for human experts to audit the results,
+paving the way towards trustworthy extraction of clinical information in
+resource-constrained scenarios. To facilitate future research in this
+direction, we release our code and prompts.
+"
+ChatGPT for Zero-shot Dialogue State Tracking: A Solution or an  Opportunity?,Michael Heck,http://arxiv.org/pdf/2306.01386v1.pdf,2023-06-02,"['cs.cl', 'cs.ai']",2306.01386v1.pdf,"  Recent research on dialogue state tracking (DST) focuses on methods that
+allow few- and zero-shot transfer to new domains or schemas. However,
+performance gains heavily depend on aggressive data augmentation and
+fine-tuning of ever larger language model based architectures. In contrast,
+general purpose language models, trained on large amounts of diverse data, hold
+the promise of solving any kind of task without task-specific training. We
+present preliminary experimental results on the ChatGPT research preview,
+showing that ChatGPT achieves state-of-the-art performance in zero-shot DST.
+Despite our findings, we argue that properties inherent to general purpose
+models limit their ability to replace specialized systems. We further theorize
+that the in-context learning capabilities of such models will likely become
+powerful tools to support the development of dedicated and dynamic dialogue
+state trackers.
+"
+Prompt to be Consistent is Better than Self-Consistent? Few-Shot and  Zero-Shot Fact Verification with Pre-trained Language Models,Fengzhu Zeng,http://arxiv.org/pdf/2306.02569v1.pdf,2023-06-05,['cs.cl'],2306.02569v1.pdf,"  Few-shot or zero-shot fact verification only relies on a few or no labeled
+training examples. In this paper, we propose a novel method called ProToCo, to
+\underline{Pro}mpt pre-trained language models (PLMs) \underline{To} be
+\underline{Co}nsistent, for improving the factuality assessment capability of
+PLMs in the few-shot and zero-shot settings. Given a claim-evidence pair,
+ProToCo generates multiple variants of the claim with different relations and
+frames a simple consistency mechanism as constraints for making compatible
+predictions across these variants. We update PLMs by using parameter-efficient
+fine-tuning (PEFT), leading to more accurate predictions in few-shot and
+zero-shot fact verification tasks. Our experiments on three public verification
+datasets show that ProToCo significantly outperforms state-of-the-art few-shot
+fact verification baselines. With a small number of unlabeled instances,
+ProToCo also outperforms the strong zero-shot learner T0 on zero-shot
+verification. Compared to large PLMs using in-context learning (ICL) method,
+ProToCo outperforms OPT-30B and the Self-Consistency-enabled OPT-6.7B model in
+both few- and zero-shot settings.
+"
+STEPS: A Benchmark for Order Reasoning in Sequential Tasks,Weizhi Wang,http://arxiv.org/pdf/2306.04441v1.pdf,2023-06-07,['cs.cl'],2306.04441v1.pdf,"  Various human activities can be abstracted into a sequence of actions in
+natural text, i.e. cooking, repairing, manufacturing, etc. Such action
+sequences heavily depend on the executing order, while disorder in action
+sequences leads to failure of further task execution by robots or AI agents.
+Therefore, to verify the order reasoning capability of current neural models in
+sequential tasks, we propose a challenging benchmark , named STEPS. STEPS
+involves two subtask settings, focusing on determining the rationality of given
+next step in recipes and selecting the reasonable step from the multi-choice
+question, respectively. We describe the data construction and task
+formulations, and benchmark most of significant Large Language Models (LLMs).
+The experimental results demonstrate 1) The commonsense reasoning of action
+orders in sequential tasks are challenging to resolve via zero-shot prompting
+or few-shot in-context learning for LLMs; 2) Prompting method still
+significantly lags behind tuning-based method on STEPS.
+"
+Modular Visual Question Answering via Code Generation,Sanjay Subramanian,http://arxiv.org/pdf/2306.05392v1.pdf,2023-06-08,['cs.cl'],2306.05392v1.pdf,"  We present a framework that formulates visual question answering as modular
+code generation. In contrast to prior work on modular approaches to VQA, our
+approach requires no additional training and relies on pre-trained language
+models (LMs), visual models pre-trained on image-caption pairs, and fifty VQA
+examples used for in-context learning. The generated Python programs invoke and
+compose the outputs of the visual models using arithmetic and conditional
+logic. Our approach improves accuracy on the COVR dataset by at least 3% and on
+the GQA dataset by roughly 2% compared to the few-shot baseline that does not
+employ code generation.
+"
+Measuring and Modifying Factual Knowledge in Large Language Models,Pouya Pezeshkpour,http://arxiv.org/pdf/2306.06264v1.pdf,2023-06-09,"['cs.cl', 'cs.lg']",2306.06264v1.pdf,"  Large Language Models (LLMs) store an extensive amount of factual knowledge
+obtained from vast collections of text. To effectively utilize these models for
+downstream tasks, it is crucial to have reliable methods for measuring their
+knowledge. However, existing approaches for knowledge measurement have certain
+limitations, and despite recent efforts, they fail to provide accurate
+measurements and the necessary insights for modifying the knowledge within
+LLMs. In this work, we employ information theory-based measurements to provide
+a framework estimating the factual knowledge contained within large language
+models. More specifically, we measure knowledge by analyzing the LLM's
+prediction probability distribution before and after instilling the target
+knowledge, employing metrics such as entropy and KL-divergence. Introducing our
+metrics, we first assess their accuracy in comparison to previous ranking-based
+methods, surpassing them by over $35\%$ in a synthetic experiment. Then, we
+explore two prominent methods of knowledge instillation, discovering that LLMs
+exhibit limitations in capturing new knowledge under specific circumstances for
+one of these methods. Lastly, we demonstrate the applicability of our methods
+in extracting unlearned and mislearned facts in LLMs through their application
+to in-context learning. We make code and data for all methods and experiments
+in this paper publicly available.
+"
+A Survey on Multimodal Large Language Models,Shukang Yin,http://arxiv.org/pdf/2306.13549v1.pdf,2023-06-23,"['cs.cv', 'cs.ai', 'cs.cl', 'cs.lg']",2306.13549v1.pdf,"  Multimodal Large Language Model (MLLM) recently has been a new rising
+research hotspot, which uses powerful Large Language Models (LLMs) as a brain
+to perform multimodal tasks. The surprising emergent capabilities of MLLM, such
+as writing stories based on images and OCR-free math reasoning, are rare in
+traditional methods, suggesting a potential path to artificial general
+intelligence. In this paper, we aim to trace and summarize the recent progress
+of MLLM. First of all, we present the formulation of MLLM and delineate its
+related concepts. Then, we discuss the key techniques and applications,
+including Multimodal Instruction Tuning (M-IT), Multimodal In-Context Learning
+(M-ICL), Multimodal Chain of Thought (M-CoT), and LLM-Aided Visual Reasoning
+(LAVR). Finally, we discuss existing challenges and point out promising
+research directions. In light of the fact that the era of MLLM has only just
+begun, we will keep updating this survey and hope it can inspire more research.
+An associated GitHub link collecting the latest papers is available at
+https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models.
+"
+Potential Benefits of Employing Large Language Models in Research in  Moral Education and Development,Hyemin Han,http://arxiv.org/pdf/2306.13805v2.pdf,2023-06-23,"['cs.cy', 'cs.ai']",2306.13805v2.pdf,"  Recently, computer scientists have developed large language models (LLMs) by
+training prediction models with large-scale language corpora and human
+reinforcements. The LLMs have become one promising way to implement artificial
+intelligence with accuracy in various fields. Interestingly, recent LLMs
+possess emergent functional features that emulate sophisticated human
+cognition, especially in-context learning and the chain of thought, which were
+unavailable in previous prediction models. In this paper, I will examine how
+LLMs might contribute to moral education and development research. To achieve
+this goal, I will review the most recently published conference papers and
+ArXiv preprints to overview the novel functional features implemented in LLMs.
+I also intend to conduct brief experiments with ChatGPT to investigate how LLMs
+behave while addressing ethical dilemmas and external feedback. The results
+suggest that LLMs might be capable of solving dilemmas based on reasoning and
+revising their reasoning process with external input. Furthermore, a
+preliminary experimental result from the moral exemplar test may demonstrate
+that exemplary stories can elicit moral elevation in LLMs as do they among
+human participants. I will discuss the potential implications of LLMs on
+research on moral education and development with the results.
+"
+DisasterResponseGPT: Large Language Models for Accelerated Plan of  Action Development in Disaster Response Scenarios,Vinicius G. Goecks,http://arxiv.org/pdf/2306.17271v1.pdf,2023-06-29,"['cs.lg', 'i.2.7; j.7; k.4.0']",2306.17271v1.pdf,"  The development of plans of action in disaster response scenarios is a
+time-consuming process. Large Language Models (LLMs) offer a powerful solution
+to expedite this process through in-context learning. This study presents
+DisasterResponseGPT, an algorithm that leverages LLMs to generate valid plans
+of action quickly by incorporating disaster response and planning guidelines in
+the initial prompt. In DisasterResponseGPT, users input the scenario
+description and receive a plan of action as output. The proposed method
+generates multiple plans within seconds, which can be further refined following
+the user's feedback. Preliminary results indicate that the plans of action
+developed by DisasterResponseGPT are comparable to human-generated ones while
+offering greater ease of modification in real-time. This approach has the
+potential to revolutionize disaster response operations by enabling rapid
+updates and adjustments during the plan's execution.
+"
+Meta-Reasoning: Semantics-Symbol Deconstruction For Large Language  Models,Yiming Wang,http://arxiv.org/pdf/2306.17820v2.pdf,2023-06-30,['cs.cl'],2306.17820v2.pdf,"  Neural-symbolic methods have shown their effectiveness in enhancing the
+reasoning abilities of large language models (LLMs). However, existing methods
+primarily rely on mapping natural languages to more syntactically complete
+formal languages (e.g., Python and SQL). Those approaches necessitate that
+reasoning tasks be convertible into programs, which cater more to the computer
+execution mindset and deviate from human reasoning habits. To expand the
+real-world applicability and flexibility of symbolic methods, we propose
+Meta-Reasoning from the scope of linguistics itself. This method empowers LLMs
+to deconstruct questions and effectively capture more generalized knowledge
+autonomously. We find that Meta-Reasoning achieves improved in-context learning
+efficiency, reasoning accuracy, and output stability in six arithmetic and
+symbolic reasoning tasks. In particular, when applied to symbolic reasoning
+tasks such as Tracking Shuffled Objects, GPT-3 (text-davinci-002) surpasses the
+few-shot Chain-of-Thought prompting approach (+37.7%), with 99% accuracy after
+a single demonstration of Meta-Reasoning.
+"
+Assessing the efficacy of large language models in generating accurate  teacher responses,Yann Hicke,http://arxiv.org/pdf/2307.04274v1.pdf,2023-07-09,"['cs.cl', 'cs.lg']",2307.04274v1.pdf,"  (Tack et al., 2023) organized the shared task hosted by the 18th Workshop on
+Innovative Use of NLP for Building Educational Applications on generation of
+teacher language in educational dialogues. Following the structure of the
+shared task, in this study, we attempt to assess the generative abilities of
+large language models in providing informative and helpful insights to
+students, thereby simulating the role of a knowledgeable teacher. To this end,
+we present an extensive evaluation of several benchmarking generative models,
+including GPT-4 (few-shot, in-context learning), fine-tuned GPT-2, and
+fine-tuned DialoGPT. Additionally, to optimize for pedagogical quality, we
+fine-tuned the Flan-T5 model using reinforcement learning. Our experimental
+findings on the Teacher-Student Chatroom Corpus subset indicate the efficacy of
+GPT-4 over other fine-tuned models, measured using BERTScore and DialogRPT.
+  We hypothesize that several dataset characteristics, including sampling,
+representativeness, and dialog completeness, pose significant challenges to
+fine-tuning, thus contributing to the poor generalizability of the fine-tuned
+models. Finally, we note the need for these generative models to be evaluated
+with a metric that relies not only on dialog coherence and matched language
+modeling distribution but also on the model's ability to showcase pedagogical
+skills.
+"
+Unsupervised Calibration through Prior Adaptation for Text  Classification using Large Language Models,Lautaro Estienne,http://arxiv.org/pdf/2307.06713v3.pdf,2023-07-13,"['cs.cl', 'cs.lg']",2307.06713v3.pdf,"  A wide variety of natural language tasks are currently being addressed with
+large-scale language models (LLMs). These models are usually trained with a
+very large amount of unsupervised text data and adapted to perform a downstream
+natural language task using methods like fine-tuning, calibration or in-context
+learning. In this work, we propose an approach to adapt the prior class
+distribution to perform text classification tasks without the need for labelled
+samples and only few in-domain sample queries. The proposed approach treats the
+LLM as a black box, adding a stage where the model posteriors are calibrated to
+the task. Results show that these methods outperform the un-adapted model for
+different number of training shots in the prompt and a previous approach were
+calibration is performed without using any adaptation data.
+"
+Reasoning before Responding: Integrating Commonsense-based Causality  Explanation for Empathetic Response Generation,Yahui Fu,http://arxiv.org/pdf/2308.00085v2.pdf,2023-07-28,"['cs.cl', 'cs.ai']",2308.00085v2.pdf,"  Recent approaches to empathetic response generation try to incorporate
+commonsense knowledge or reasoning about the causes of emotions to better
+understand the user's experiences and feelings. However, these approaches
+mainly focus on understanding the causalities of context from the user's
+perspective, ignoring the system's perspective. In this paper, we propose a
+commonsense-based causality explanation approach for diverse empathetic
+response generation that considers both the user's perspective (user's desires
+and reactions) and the system's perspective (system's intentions and
+reactions). We enhance ChatGPT's ability to reason for the system's perspective
+by integrating in-context learning with commonsense knowledge. Then, we
+integrate the commonsense-based causality explanation with both ChatGPT and a
+T5-based model. Experimental evaluations demonstrate that our method
+outperforms other comparable methods on both automatic and human evaluations.
+"
+Baby's CoThought: Leveraging Large Language Models for Enhanced  Reasoning in Compact Models,Zheyu Zhang,http://arxiv.org/pdf/2308.01684v2.pdf,2023-08-03,['cs.cl'],2308.01684v2.pdf,"  Large Language Models (LLMs) demonstrate remarkable performance on a variety
+of natural language understanding (NLU) tasks, primarily due to their
+in-context learning ability. This ability could be applied to building babylike
+models, i.e. models at small scales, improving training efficiency. In this
+paper, we propose a ""CoThought"" pipeline, which efficiently trains smaller
+""baby"" language models (BabyLMs) by leveraging the Chain of Thought prompting
+of LLMs. Our pipeline restructures a dataset of less than 100M in size using
+GPT-3.5-turbo, transforming it into task-oriented, human-readable texts that
+are comparable to the school texts for language learners. The BabyLM is then
+pretrained on this restructured dataset in a RoBERTa fashion. In evaluations
+across 4 benchmarks, our BabyLM outperforms the vanilla RoBERTa in 10
+linguistic, NLU, and question-answering tasks by more than 3 points, showing a
+superior ability to extract contextual information. These results suggest that
+compact LMs pretrained on small, LLM-restructured data can better understand
+tasks and achieve improved performance.
+"
+FLIRT: Feedback Loop In-context Red Teaming,Ninareh Mehrabi,http://arxiv.org/pdf/2308.04265v1.pdf,2023-08-08,['cs.ai'],2308.04265v1.pdf,"  Warning: this paper contains content that may be inappropriate or offensive.
+  As generative models become available for public use in various applications,
+testing and analyzing vulnerabilities of these models has become a priority.
+Here we propose an automatic red teaming framework that evaluates a given model
+and exposes its vulnerabilities against unsafe and inappropriate content
+generation. Our framework uses in-context learning in a feedback loop to red
+team models and trigger them into unsafe content generation. We propose
+different in-context attack strategies to automatically learn effective and
+diverse adversarial prompts for text-to-image models. Our experiments
+demonstrate that compared to baseline approaches, our proposed strategy is
+significantly more effective in exposing vulnerabilities in Stable Diffusion
+(SD) model, even when the latter is enhanced with safety features. Furthermore,
+we demonstrate that the proposed framework is effective for red teaming
+text-to-text models, resulting in significantly higher toxic response
+generation rate compared to previously reported numbers.
+"
+JEN-1: Text-Guided Universal Music Generation with Omnidirectional  Diffusion Models,Peike Li,http://arxiv.org/pdf/2308.04729v1.pdf,2023-08-09,"['cs.sd', 'cs.ai', 'cs.lg', 'cs.mm', 'eess.as']",2308.04729v1.pdf,"  Music generation has attracted growing interest with the advancement of deep
+generative models. However, generating music conditioned on textual
+descriptions, known as text-to-music, remains challenging due to the complexity
+of musical structures and high sampling rate requirements. Despite the task's
+significance, prevailing generative models exhibit limitations in music
+quality, computational efficiency, and generalization. This paper introduces
+JEN-1, a universal high-fidelity model for text-to-music generation. JEN-1 is a
+diffusion model incorporating both autoregressive and non-autoregressive
+training. Through in-context learning, JEN-1 performs various generation tasks
+including text-guided music generation, music inpainting, and continuation.
+Evaluations demonstrate JEN-1's superior performance over state-of-the-art
+methods in text-music alignment and music quality while maintaining
+computational efficiency. Our demos are available at
+http://futureverse.com/research/jen/demos/jen1
+"
+Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language  Models,Bilgehan Sel,http://arxiv.org/pdf/2308.10379v2.pdf,2023-08-20,"['cs.cl', 'cs.ai']",2308.10379v2.pdf,"  Current literature, aiming to surpass the ""Chain-of-Thought"" approach, often
+resorts to an external modus operandi involving halting, modifying, and then
+resuming the generation process to boost Large Language Models' (LLMs)
+reasoning capacities. This mode escalates the number of query requests, leading
+to increased costs, memory, and computational overheads. Addressing this, we
+propose the Algorithm of Thoughts -- a novel strategy that propels LLMs through
+algorithmic reasoning pathways, pioneering a new mode of in-context learning.
+By employing algorithmic examples, we exploit the innate recurrence dynamics of
+LLMs, expanding their idea exploration with merely one or a few queries. Our
+technique outperforms earlier single-query methods and stands on par with a
+recent multi-query strategy that employs an extensive tree search algorithm.
+Intriguingly, our results suggest that instructing an LLM using an algorithm
+can lead to performance surpassing that of the algorithm itself, hinting at
+LLM's inherent ability to weave its intuition into optimized searches. We probe
+into the underpinnings of our method's efficacy and its nuances in application.
+"
+Building Emotional Support Chatbots in the Era of LLMs,Zhonghua Zheng,http://arxiv.org/pdf/2308.11584v1.pdf,2023-08-17,"['cs.cl', 'cs.ai']",2308.11584v1.pdf,"  The integration of emotional support into various conversational scenarios
+presents profound societal benefits, such as social interactions, mental health
+counseling, and customer service. However, there are unsolved challenges that
+hinder real-world applications in this field, including limited data
+availability and the absence of well-accepted model training paradigms. This
+work endeavors to navigate these challenges by harnessing the capabilities of
+Large Language Models (LLMs). We introduce an innovative methodology that
+synthesizes human insights with the computational prowess of LLMs to curate an
+extensive emotional support dialogue dataset. Our approach is initiated with a
+meticulously designed set of dialogues spanning diverse scenarios as generative
+seeds. By utilizing the in-context learning potential of ChatGPT, we
+recursively generate an ExTensible Emotional Support dialogue dataset, named
+ExTES. Following this, we deploy advanced tuning techniques on the LLaMA model,
+examining the impact of diverse training strategies, ultimately yielding an LLM
+meticulously optimized for emotional support interactions. An exhaustive
+assessment of the resultant model showcases its proficiency in offering
+emotional support, marking a pivotal step in the realm of emotional support
+bots and paving the way for subsequent research and implementations.
+"
+Diffusion Language Models Can Perform Many Tasks with Scaling and  Instruction-Finetuning,Jiasheng Ye,http://arxiv.org/pdf/2308.12219v2.pdf,2023-08-23,"['cs.cl', 'cs.ai', 'cs.lg']",2308.12219v2.pdf,"  The recent surge of generative AI has been fueled by the generative power of
+diffusion probabilistic models and the scalable capabilities of large language
+models. Despite their potential, it remains elusive whether diffusion language
+models can solve general language tasks comparable to their autoregressive
+counterparts. This paper demonstrates that scaling diffusion models w.r.t.
+data, sizes, and tasks can effectively make them strong language learners. We
+build competent diffusion language models at scale by first acquiring knowledge
+from massive data via masked language modeling pretraining thanks to their
+intrinsic connections. We then reprogram pretrained masked language models into
+diffusion language models via diffusive adaptation, wherein task-specific
+finetuning and instruction finetuning are explored to unlock their versatility
+in solving general language tasks. Experiments show that scaling diffusion
+language models consistently improves performance across downstream language
+tasks. We further discover that instruction finetuning can elicit zero-shot and
+few-shot in-context learning abilities that help tackle many unseen tasks by
+following natural language instructions, and show promise in advanced and
+challenging abilities such as reasoning.
+"
+Large Language Model as Autonomous Decision Maker,Yining Ye,http://arxiv.org/pdf/2308.12519v1.pdf,2023-08-24,['cs.cl'],2308.12519v1.pdf,"  While large language models (LLMs) exhibit impressive language understanding
+and in-context learning abilities, their decision-making ability still heavily
+relies on the guidance of task-specific expert knowledge when solving
+real-world tasks. To unleash the potential of LLMs as autonomous decision
+makers, this paper presents an approach JuDec to endow LLMs with the
+self-judgment ability, enabling LLMs to achieve autonomous judgment and
+exploration for decision making. Specifically, in JuDec, Elo-based
+Self-Judgment Mechanism is designed to assign Elo scores to decision steps to
+judge their values and utilities via pairwise comparisons between two solutions
+and then guide the decision-searching process toward the optimal solution
+accordingly. Experimental results on the ToolBench dataset demonstrate JuDec's
+superiority over baselines, achieving over 10% improvement in Pass Rate on
+diverse tasks. It offers higher-quality solutions and reduces costs (ChatGPT
+API calls), highlighting its effectiveness and efficiency.
+"
+Breaking the Bank with ChatGPT: Few-Shot Text Classification for Finance,Lefteris Loukas,http://arxiv.org/pdf/2308.14634v1.pdf,2023-08-28,"['cs.cl', 'cs.ai', 'cs.lg', 'q-fin.cp']",2308.14634v1.pdf,"  We propose the use of conversational GPT models for easy and quick few-shot
+text classification in the financial domain using the Banking77 dataset. Our
+approach involves in-context learning with GPT-3.5 and GPT-4, which minimizes
+the technical expertise required and eliminates the need for expensive GPU
+computing while yielding quick and accurate results. Additionally, we fine-tune
+other pre-trained, masked language models with SetFit, a recent contrastive
+learning technique, to achieve state-of-the-art results both in full-data and
+few-shot settings. Our findings show that querying GPT-3.5 and GPT-4 can
+outperform fine-tuned, non-generative models even with fewer examples. However,
+subscription fees associated with these solutions may be considered costly for
+small organizations. Lastly, we find that generative models perform better on
+the given task when shown representative samples selected by a human expert
+rather than when shown random ones. We conclude that a) our proposed methods
+offer a practical solution for few-shot tasks in datasets with limited label
+availability, and b) our state-of-the-art results can inspire future work in
+the area.
+"
+Gender-specific Machine Translation with Large Language Models,Eduardo Sánchez,http://arxiv.org/pdf/2309.03175v1.pdf,2023-09-06,['cs.cl'],2309.03175v1.pdf,"  Decoder-only Large Language Models (LLMs) have demonstrated potential in
+machine translation (MT), albeit with performance slightly lagging behind
+traditional encoder-decoder Neural Machine Translation (NMT) systems. However,
+LLMs offer a unique advantage: the ability to control the properties of the
+output through prompts. In this study, we harness this flexibility to explore
+LLaMa's capability to produce gender-specific translations for languages with
+grammatical gender. Our results indicate that LLaMa can generate
+gender-specific translations with competitive accuracy and gender bias
+mitigation when compared to NLLB, a state-of-the-art multilingual NMT system.
+Furthermore, our experiments reveal that LLaMa's translations are robust,
+showing significant performance drops when evaluated against opposite-gender
+references in gender-ambiguous datasets but maintaining consistency in less
+ambiguous contexts. This research provides insights into the potential and
+challenges of using LLMs for gender-specific translations and highlights the
+importance of in-context learning to elicit new tasks in LLMs.
+"
+Improving Open Information Extraction with Large Language Models: A  Study on Demonstration Uncertainty,Chen Ling,http://arxiv.org/pdf/2309.03433v1.pdf,2023-09-07,['cs.cl'],2309.03433v1.pdf,"  Open Information Extraction (OIE) task aims at extracting structured facts
+from unstructured text, typically in the form of (subject, relation, object)
+triples. Despite the potential of large language models (LLMs) like ChatGPT as
+a general task solver, they lag behind state-of-the-art (supervised) methods in
+OIE tasks due to two key issues. First, LLMs struggle to distinguish irrelevant
+context from relevant relations and generate structured output due to the
+restrictions on fine-tuning the model. Second, LLMs generates responses
+autoregressively based on probability, which makes the predicted relations lack
+confidence. In this paper, we assess the capabilities of LLMs in improving the
+OIE task. Particularly, we propose various in-context learning strategies to
+enhance LLM's instruction-following ability and a demonstration uncertainty
+quantification module to enhance the confidence of the generated relations. Our
+experiments on three OIE benchmark datasets show that our approach holds its
+own against established supervised methods, both quantitatively and
+qualitatively.
+"
+EPA: Easy Prompt Augmentation on Large Language Models via Multiple  Sources and Multiple Targets,Hongyuan Lu,http://arxiv.org/pdf/2309.04725v1.pdf,2023-09-09,['cs.cl'],2309.04725v1.pdf,"  Large language models (LLMs) have shown promising performance on various NLP
+tasks via task prompting. And their performance can be further improved by
+appending task demonstrations to the head of the prompt. And usually, a better
+performance can be achieved with more demonstrations. However, asking the users
+to write the demonstrations can be cumbersome. As a simple yet cost-effective
+workaround, this paper proposes a novel method called EPA (\textbf{E}asy
+\textbf{P}rompt \textbf{A}ugmentation)\footnote{While this paper considers
+augmenting prompts via demonstrations, we name it EPA as the name EDA is
+already taken by a well-known NLP method \citep{wei-zou-2019-eda}.} that
+effectively minimizes user efforts in writing demonstrations while improving
+the model performance at the same time. EPA achieves these goals by
+automatically augmenting the demonstrations with multiple sources/targets,
+where each of them paraphrases each other. This is well motivated as augmenting
+data via paraphrasing effectively improves neural language models. EPA thus
+employs paraphrasing as an augmentation method for in-context learning.
+Extensive experiments indicate that EPA effectively improves both NLU and NLG
+tasks, covering from natural language inference to machine translation in
+translating tens of languages.\footnote{Code and data will be released upon
+publication.}
+"
+CONVERSER: Few-Shot Conversational Dense Retrieval with Synthetic Data  Generation,Chao-Wei Huang,http://arxiv.org/pdf/2309.06748v1.pdf,2023-09-13,"['cs.cl', 'cs.ir']",2309.06748v1.pdf,"  Conversational search provides a natural interface for information retrieval
+(IR). Recent approaches have demonstrated promising results in applying dense
+retrieval to conversational IR. However, training dense retrievers requires
+large amounts of in-domain paired data. This hinders the development of
+conversational dense retrievers, as abundant in-domain conversations are
+expensive to collect. In this paper, we propose CONVERSER, a framework for
+training conversational dense retrievers with at most 6 examples of in-domain
+dialogues. Specifically, we utilize the in-context learning capability of large
+language models to generate conversational queries given a passage in the
+retrieval corpus. Experimental results on conversational retrieval benchmarks
+OR-QuAC and TREC CAsT 19 show that the proposed CONVERSER achieves comparable
+performance to fully-supervised models, demonstrating the effectiveness of our
+proposed framework in few-shot conversational dense retrieval. All source code
+and generated datasets are available at https://github.com/MiuLab/CONVERSER
+"
+Speech-to-Speech Translation with Discrete-Unit-Based Style Transfer,Yongqi Wang,http://arxiv.org/pdf/2309.07566v1.pdf,2023-09-14,"['cs.sd', 'cs.ai', 'eess.as']",2309.07566v1.pdf,"  Direct speech-to-speech translation (S2ST) with discrete self-supervised
+representations has achieved remarkable accuracy, but is unable to preserve the
+speaker timbre of the source speech during translation. Meanwhile, the scarcity
+of high-quality speaker-parallel data poses a challenge for learning style
+transfer between source and target speech. We propose an S2ST framework with an
+acoustic language model based on discrete units from a self-supervised model
+and a neural codec for style transfer. The acoustic language model leverages
+self-supervised in-context learning, acquiring the ability for style transfer
+without relying on any speaker-parallel data, thereby overcoming the issue of
+data scarcity. By using extensive training data, our model achieves zero-shot
+cross-lingual style transfer on previously unseen source languages. Experiments
+show that our model generates translated speeches with high fidelity and style
+similarity. Audio samples are available at http://stylelm.github.io/ .
+"
+"Bridging Topic, Domain, and Language Shifts: An Evaluation of  Comprehensive Out-of-Distribution Scenarios",Andreas Waldis,http://arxiv.org/pdf/2309.08316v1.pdf,2023-09-15,['cs.cl'],2309.08316v1.pdf,"  Language models (LMs) excel in in-distribution (ID) scenarios where train and
+test data are independent and identically distributed. However, their
+performance often degrades in real-world applications like argument mining.
+Such degradation happens when new topics emerge, or other text domains and
+languages become relevant. To assess LMs' generalization abilities in such
+out-of-distribution (OOD) scenarios, we simulate such distribution shifts by
+deliberately withholding specific instances for testing, as from the social
+media domain or the topic Solar Energy.
+  Unlike prior studies focusing on specific shifts and metrics in isolation, we
+comprehensively analyze OOD generalization. We define three metrics to pinpoint
+generalization flaws and propose eleven classification tasks covering topic,
+domain, and language shifts. Overall, we find superior performance of
+prompt-based fine-tuning, notably when train and test splits primarily differ
+semantically. Simultaneously, in-context learning is more effective than
+prompt-based or vanilla fine-tuning for tasks when training data embodies heavy
+discrepancies in label distribution compared to testing data. This reveals a
+crucial drawback of gradient-based learning: it biases LMs regarding such
+structural obstacles.
+"
+Neural Machine Translation Models Can Learn to be Few-shot Learners,Raphael Reinauer,http://arxiv.org/pdf/2309.08590v1.pdf,2023-09-15,['cs.cl'],2309.08590v1.pdf,"  The emergent ability of Large Language Models to use a small number of
+examples to learn to perform in novel domains and tasks, also called in-context
+learning (ICL). In this work, we show that a much smaller model can be trained
+to perform ICL by fine-tuning towards a specialized training objective,
+exemplified on the task of domain adaptation for neural machine translation.
+With this capacity for ICL, the model can take advantage of relevant few-shot
+examples to adapt its output towards the domain. We compare the quality of this
+domain adaptation to traditional supervised techniques and ICL with a
+40B-parameter Large Language Model. Our approach allows efficient batch
+inference on a mix of domains and outperforms state-of-the-art baselines in
+terms of both translation quality and immediate adaptation rate, i.e. the
+ability to reproduce a specific term after being shown a single example.
+"
+Few-Shot Adaptation for Parsing Contextual Utterances with LLMs,Kevin Lin,http://arxiv.org/pdf/2309.10168v1.pdf,2023-09-18,['cs.cl'],2309.10168v1.pdf,"  We evaluate the ability of semantic parsers based on large language models
+(LLMs) to handle contextual utterances. In real-world settings, there typically
+exists only a limited number of annotated contextual utterances due to
+annotation cost, resulting in an imbalance compared to non-contextual
+utterances. Therefore, parsers must adapt to contextual utterances with a few
+training examples. We examine four major paradigms for doing so in
+conversational semantic parsing i.e., Parse-with-Utterance-History,
+Parse-with-Reference-Program, Parse-then-Resolve, and Rewrite-then-Parse. To
+facilitate such cross-paradigm comparisons, we construct
+SMCalFlow-EventQueries, a subset of contextual examples from SMCalFlow with
+additional annotations. Experiments with in-context learning and fine-tuning
+suggest that Rewrite-then-Parse is the most promising paradigm when
+holistically considering parsing accuracy, annotation cost, and error types.
+"
+Toward Unified Controllable Text Generation via Regular Expression  Instruction,Xin Zheng,http://arxiv.org/pdf/2309.10447v2.pdf,2023-09-19,"['cs.cl', 'cs.ai']",2309.10447v2.pdf,"  Controllable text generation is a fundamental aspect of natural language
+generation, with numerous methods proposed for different constraint types.
+However, these approaches often require significant architectural or decoding
+modifications, making them challenging to apply to additional constraints or
+resolve different constraint combinations. To address this, our paper
+introduces Regular Expression Instruction (REI), which utilizes an
+instruction-based mechanism to fully exploit regular expressions' advantages to
+uniformly model diverse constraints. Specifically, our REI supports all popular
+fine-grained controllable generation constraints, i.e., lexical, positional,
+and length, as well as their complex combinations, via regular expression-style
+instructions. Our method only requires fine-tuning on medium-scale language
+models or few-shot, in-context learning on large language models, and requires
+no further adjustment when applied to various constraint combinations.
+Experiments demonstrate that our straightforward approach yields high success
+rates and adaptability to various constraints while maintaining competitiveness
+in automatic metrics and outperforming most previous baselines.
+"
+Language Modeling Is Compression,Grégoire Delétang,http://arxiv.org/pdf/2309.10668v1.pdf,2023-09-19,"['cs.lg', 'cs.ai', 'cs.cl', 'cs.it', 'math.it']",2309.10668v1.pdf,"  It has long been established that predictive models can be transformed into
+lossless compressors and vice versa. Incidentally, in recent years, the machine
+learning community has focused on training increasingly large and powerful
+self-supervised (language) models. Since these large language models exhibit
+impressive predictive capabilities, they are well-positioned to be strong
+compressors. In this work, we advocate for viewing the prediction problem
+through the lens of compression and evaluate the compression capabilities of
+large (foundation) models. We show that large language models are powerful
+general-purpose predictors and that the compression viewpoint provides novel
+insights into scaling laws, tokenization, and in-context learning. For example,
+Chinchilla 70B, while trained primarily on text, compresses ImageNet patches to
+43.4% and LibriSpeech samples to 16.4% of their raw size, beating
+domain-specific compressors like PNG (58.5%) or FLAC (30.3%), respectively.
+Finally, we show that the prediction-compression equivalence allows us to use
+any compressor (like gzip) to build a conditional generative model.
+"
+Language-Oriented Communication with Semantic Coding and Knowledge  Distillation for Text-to-Image Generation,Hyelin Nam,http://arxiv.org/pdf/2309.11127v1.pdf,2023-09-20,"['eess.sp', 'cs.ai', 'cs.cl']",2309.11127v1.pdf,"  By integrating recent advances in large language models (LLMs) and generative
+models into the emerging semantic communication (SC) paradigm, in this article
+we put forward to a novel framework of language-oriented semantic communication
+(LSC). In LSC, machines communicate using human language messages that can be
+interpreted and manipulated via natural language processing (NLP) techniques
+for SC efficiency. To demonstrate LSC's potential, we introduce three
+innovative algorithms: 1) semantic source coding (SSC) which compresses a text
+prompt into its key head words capturing the prompt's syntactic essence while
+maintaining their appearance order to keep the prompt's context; 2) semantic
+channel coding (SCC) that improves robustness against errors by substituting
+head words with their lenghthier synonyms; and 3) semantic knowledge
+distillation (SKD) that produces listener-customized prompts via in-context
+learning the listener's language style. In a communication task for progressive
+text-to-image generation, the proposed methods achieve higher perceptual
+similarities with fewer transmissions while enhancing robustness in noisy
+communication channels.
+"
+Towards Effective Disambiguation for Machine Translation with Large  Language Models,Vivek Iyer,http://arxiv.org/pdf/2309.11668v2.pdf,2023-09-20,['cs.cl'],2309.11668v2.pdf,"  Resolving semantic ambiguity has long been recognised as a central challenge
+in the field of Machine Translation. Recent work on benchmarking translation
+performance on ambiguous sentences has exposed the limitations of conventional
+Neural Machine Translation (NMT) systems, which fail to handle many such cases.
+Large language models (LLMs) have emerged as a promising alternative,
+demonstrating comparable performance to traditional NMT models while
+introducing new paradigms for controlling the target outputs. In this paper, we
+study the capabilities of LLMs to translate ""ambiguous sentences"" - i.e. those
+containing highly polysemous words and/or rare word senses. We also propose two
+ways to improve their disambiguation capabilities, through a) in-context
+learning and b) fine-tuning on carefully curated ambiguous datasets.
+Experiments show that our methods can match or outperform state-of-the-art
+systems such as DeepL and NLLB in four out of five language directions. Our
+research provides valuable insights into effectively adapting LLMs to become
+better disambiguators during Machine Translation. We release our curated
+disambiguation corpora and resources at
+https://data.statmt.org/ambiguous-europarl.
+"
+In-context Interference in Chat-based Large Language Models,Eric Nuertey Coleman,http://arxiv.org/pdf/2309.12727v1.pdf,2023-09-22,"['cs.ai', 'cs.cl']",2309.12727v1.pdf,"  Large language models (LLMs) have had a huge impact on society due to their
+impressive capabilities and vast knowledge of the world. Various applications
+and tools have been created that allow users to interact with these models in a
+black-box scenario. However, one limitation of this scenario is that users
+cannot modify the internal knowledge of the model, and the only way to add or
+modify internal knowledge is by explicitly mentioning it to the model during
+the current interaction. This learning process is called in-context training,
+and it refers to training that is confined to the user's current session or
+context. In-context learning has significant applications, but also has
+limitations that are seldom studied. In this paper, we present a study that
+shows how the model can suffer from interference between information that
+continually flows in the context, causing it to forget previously learned
+knowledge, which can reduce the model's performance. Along with showing the
+problem, we propose an evaluation benchmark based on the bAbI dataset.
+"
+Affect Recognition in Conversations Using Large Language Models,Shutong Feng,http://arxiv.org/pdf/2309.12881v1.pdf,2023-09-22,['cs.cl'],2309.12881v1.pdf,"  Affect recognition, encompassing emotions, moods, and feelings, plays a
+pivotal role in human communication. In the realm of conversational artificial
+intelligence (AI), the ability to discern and respond to human affective cues
+is a critical factor for creating engaging and empathetic interactions. This
+study delves into the capacity of large language models (LLMs) to recognise
+human affect in conversations, with a focus on both open-domain chit-chat
+dialogues and task-oriented dialogues. Leveraging three diverse datasets,
+namely IEMOCAP, EmoWOZ, and DAIC-WOZ, covering a spectrum of dialogues from
+casual conversations to clinical interviews, we evaluated and compared LLMs'
+performance in affect recognition. Our investigation explores the zero-shot and
+few-shot capabilities of LLMs through in-context learning (ICL) as well as
+their model capacities through task-specific fine-tuning. Additionally, this
+study takes into account the potential impact of automatic speech recognition
+(ASR) errors on LLM predictions. With this work, we aim to shed light on the
+extent to which LLMs can replicate human-like affect recognition capabilities
+in conversations.
+"
+Calibrating LLM-Based Evaluator,Yuxuan Liu,http://arxiv.org/pdf/2309.13308v1.pdf,2023-09-23,['cs.cl'],2309.13308v1.pdf,"  Recent advancements in large language models (LLMs) on language modeling and
+emergent capabilities make them a promising reference-free evaluator of natural
+language generation quality, and a competent alternative to human evaluation.
+However, hindered by the closed-source or high computational demand to host and
+tune, there is a lack of practice to further calibrate an off-the-shelf
+LLM-based evaluator towards better human alignment. In this work, we propose
+AutoCalibrate, a multi-stage, gradient-free approach to automatically calibrate
+and align an LLM-based evaluator toward human preference. Instead of explicitly
+modeling human preferences, we first implicitly encompass them within a set of
+human labels. Then, an initial set of scoring criteria is drafted by the
+language model itself, leveraging in-context learning on different few-shot
+examples. To further calibrate this set of criteria, we select the best
+performers and re-draft them with self-refinement. Our experiments on multiple
+text quality evaluation datasets illustrate a significant improvement in
+correlation with expert evaluation through calibration. Our comprehensive
+qualitative analysis conveys insightful intuitions and observations on the
+essence of effective scoring criteria.
+"
+MedEdit: Model Editing for Medical Question Answering with External  Knowledge Bases,Yucheng Shi,http://arxiv.org/pdf/2309.16035v1.pdf,2023-09-27,"['cs.cl', 'cs.ai']",2309.16035v1.pdf,"  Large Language Models (LLMs), although powerful in general domains, often
+perform poorly on domain-specific tasks like medical question answering (QA).
+Moreover, they tend to function as ""black-boxes,"" making it challenging to
+modify their behavior. Addressing this, our study delves into model editing
+utilizing in-context learning, aiming to improve LLM responses without the need
+for fine-tuning or retraining. Specifically, we propose a comprehensive
+retrieval strategy to extract medical facts from an external knowledge base,
+and then we incorporate them into the query prompt for the LLM. Focusing on
+medical QA using the MedQA-SMILE dataset, we evaluate the impact of different
+retrieval models and the number of facts provided to the LLM. Notably, our
+edited Vicuna model exhibited an accuracy improvement from 44.46% to 48.54%.
+This work underscores the potential of model editing to enhance LLM
+performance, offering a practical approach to mitigate the challenges of
+black-box LLMs.
+"
+A Prefrontal Cortex-inspired Architecture for Planning in Large Language  Models,Taylor Webb,http://arxiv.org/pdf/2310.00194v1.pdf,2023-09-30,"['cs.ai', 'cs.ne']",2310.00194v1.pdf,"  Large language models (LLMs) demonstrate impressive performance on a wide
+variety of tasks, but they often struggle with tasks that require multi-step
+reasoning or goal-directed planning. To address this, we take inspiration from
+the human brain, in which planning is accomplished via the recurrent
+interaction of specialized modules in the prefrontal cortex (PFC). These
+modules perform functions such as conflict monitoring, state prediction, state
+evaluation, task decomposition, and task coordination. We find that LLMs are
+sometimes capable of carrying out these functions in isolation, but struggle to
+autonomously coordinate them in the service of a goal. Therefore, we propose a
+black box architecture with multiple LLM-based (GPT-4) modules. The
+architecture improves planning through the interaction of specialized
+PFC-inspired modules that break down a larger problem into multiple brief
+automated calls to the LLM. We evaluate the combined architecture on two
+challenging planning tasks -- graph traversal and Tower of Hanoi -- finding
+that it yields significant improvements over standard LLM methods (e.g.,
+zero-shot prompting or in-context learning). These results demonstrate the
+benefit of utilizing knowledge from cognitive neuroscience to improve planning
+in LLMs.
+"
+Towards LLM-based Fact Verification on News Claims with a Hierarchical  Step-by-Step Prompting Method,Xuan Zhang,http://arxiv.org/pdf/2310.00305v1.pdf,2023-09-30,['cs.cl'],2310.00305v1.pdf,"  While large pre-trained language models (LLMs) have shown their impressive
+capabilities in various NLP tasks, they are still under-explored in the
+misinformation domain. In this paper, we examine LLMs with in-context learning
+(ICL) for news claim verification, and find that only with 4-shot demonstration
+examples, the performance of several prompting methods can be comparable with
+previous supervised models. To further boost performance, we introduce a
+Hierarchical Step-by-Step (HiSS) prompting method which directs LLMs to
+separate a claim into several subclaims and then verify each of them via
+multiple questions-answering steps progressively. Experiment results on two
+public misinformation datasets show that HiSS prompting outperforms
+state-of-the-art fully-supervised approach and strong few-shot ICL-enabled
+baselines.
+"
+Text Data Augmentation in Low-Resource Settings via Fine-Tuning of Large  Language Models,Jean Kaddour,http://arxiv.org/pdf/2310.01119v1.pdf,2023-10-02,"['cs.cl', 'cs.lg']",2310.01119v1.pdf,"  The in-context learning ability of large language models (LLMs) enables them
+to generalize to novel downstream tasks with relatively few labeled examples.
+However, they require enormous computational resources to be deployed.
+Alternatively, smaller models can solve specific tasks if fine-tuned with
+enough labeled examples. These examples, however, are expensive to obtain. In
+pursuit of the best of both worlds, we study the annotation and generation of
+fine-tuning training data via fine-tuned teacher LLMs to improve the downstream
+performance of much smaller models. In four text classification and two text
+generation tasks, we find that both data generation and annotation dramatically
+improve the respective downstream model's performance, occasionally
+necessitating only a minor fraction of the original training dataset.
+"
+Fool Your (Vision and) Language Model With Embarrassingly Simple  Permutations,Yongshuo Zong,http://arxiv.org/pdf/2310.01651v1.pdf,2023-10-02,['cs.lg'],2310.01651v1.pdf,"  Large language and vision-language models are rapidly being deployed in
+practice thanks to their impressive capabilities in instruction following,
+in-context learning, and so on. This raises an urgent need to carefully analyse
+their robustness so that stakeholders can understand if and when such models
+are trustworthy enough to be relied upon in any given application. In this
+paper, we highlight a specific vulnerability in popular models, namely
+permutation sensitivity in multiple-choice question answering (MCQA).
+Specifically, we show empirically that popular models are vulnerable to
+adversarial permutation in answer sets for multiple-choice prompting, which is
+surprising as models should ideally be as invariant to prompt permutation as
+humans are. These vulnerabilities persist across various model sizes, and exist
+in very recent language and vision-language models. Code is available at
+\url{https://github.com/ys-zong/FoolyourVLLMs}.
+"
+Improving Automatic VQA Evaluation Using Large Language Models,Oscar Mañas,http://arxiv.org/pdf/2310.02567v1.pdf,2023-10-04,"['cs.cv', 'cs.ai', 'cs.cl', 'cs.lg']",2310.02567v1.pdf,"  8 years after the visual question answering (VQA) task was proposed, accuracy
+remains the primary metric for automatic evaluation. VQA Accuracy has been
+effective so far in the IID evaluation setting. However, our community is
+undergoing a shift towards open-ended generative models and OOD evaluation. In
+this new paradigm, the existing VQA Accuracy metric is overly stringent and
+underestimates the performance of VQA systems. Thus, there is a need to develop
+more robust automatic VQA metrics that serve as a proxy for human judgment. In
+this work, we propose to leverage the in-context learning capabilities of
+instruction-tuned large language models (LLMs) to build a better VQA metric. We
+formulate VQA evaluation as an answer-rating task where the LLM is instructed
+to score the accuracy of a candidate answer given a set of reference answers.
+We demonstrate the proposed metric better correlates with human judgment
+compared to existing metrics across several VQA models and benchmarks. We hope
+wide adoption of our metric will contribute to better estimating the research
+progress on the VQA task.
+"
+A Language-Agent Approach to Formal Theorem-Proving,Amitayush Thakur,http://arxiv.org/pdf/2310.04353v1.pdf,2023-10-06,"['cs.lg', 'cs.ai', 'cs.lo', 'cs.pl']",2310.04353v1.pdf,"  Language agents, which use a large language model (LLM) capable of in-context
+learning to interact with an external environment, have recently emerged as a
+promising approach to control tasks. We present the first language-agent
+approach to formal theorem-proving. Our method, COPRA, uses a high-capacity,
+black-box LLM (GPT-4) as part of a policy for a stateful backtracking search.
+During the search, the policy can select proof tactics and retrieve lemmas and
+definitions from an external database. Each selected tactic is executed in the
+underlying proof framework, and the execution feedback is used to build the
+prompt for the next policy invocation. The search also tracks selected
+information from its history and uses it to reduce hallucinations and
+unnecessary LLM queries.
+  We evaluate COPRA on the miniF2F benchmark for Lean and a set of Coq tasks
+from the Compcert project. On these benchmarks, COPRA is significantly better
+than one-shot invocations of GPT-4, as well as state-of-the-art models
+fine-tuned on proof data, at finding correct proofs quickly.
+"
+Guideline Learning for In-context Information Extraction,Chaoxu Pang,http://arxiv.org/pdf/2310.05066v2.pdf,2023-10-08,"['cs.cl', 'cs.lg']",2310.05066v2.pdf,"  Large language models (LLMs) can perform a new task by merely conditioning on
+task instructions and a few input-output examples, without optimizing any
+parameters. This is called In-Context Learning (ICL). In-context Information
+Extraction (IE) has recently garnered attention in the research community.
+However, the performance of In-context IE generally lags behind the
+state-of-the-art supervised expert models. We highlight a key reason for this
+shortfall: underspecified task description. The limited-length context
+struggles to thoroughly express the intricate IE task instructions and various
+edge cases, leading to misalignment in task comprehension with humans. In this
+paper, we propose a Guideline Learning (GL) framework for In-context IE which
+reflectively learns and follows guidelines. During the learning phrase, GL
+automatically synthesizes a set of guidelines based on a few error cases, and
+during inference, GL retrieves helpful guidelines for better ICL. Moreover, we
+propose a self-consistency-based active learning method to enhance the
+efficiency of GL. Experiments on event extraction and relation extraction show
+that GL can significantly improve the performance of in-context IE.
+"
+Harnessing the Power of Large Language Models for Empathetic Response  Generation: Empirical Investigations and Improvements,Yushan Qian,http://arxiv.org/pdf/2310.05140v1.pdf,2023-10-08,"['cs.cl', 'cs.ai']",2310.05140v1.pdf,"  Empathetic dialogue is an indispensable part of building harmonious social
+relationships and contributes to the development of a helpful AI. Previous
+approaches are mainly based on fine small-scale language models. With the
+advent of ChatGPT, the application effect of large language models (LLMs) in
+this field has attracted great attention. This work empirically investigates
+the performance of LLMs in generating empathetic responses and proposes three
+improvement methods of semantically similar in-context learning, two-stage
+interactive generation, and combination with the knowledge base. Extensive
+experiments show that LLMs can significantly benefit from our proposed methods
+and is able to achieve state-of-the-art performance in both automatic and human
+evaluations. Additionally, we explore the possibility of GPT-4 simulating human
+evaluators.
+"
+LLMLingua: Compressing Prompts for Accelerated Inference of Large  Language Models,Huiqiang Jiang,http://arxiv.org/pdf/2310.05736v1.pdf,2023-10-09,"['cs.cl', 'cs.lg']",2310.05736v1.pdf,"  Large language models (LLMs) have been applied in various applications due to
+their astonishing capabilities. With advancements in technologies such as
+chain-of-thought (CoT) prompting and in-context learning (ICL), the prompts fed
+to LLMs are becoming increasingly lengthy, even exceeding tens of thousands of
+tokens. To accelerate model inference and reduce cost, this paper presents
+LLMLingua, a coarse-to-fine prompt compression method that involves a budget
+controller to maintain semantic integrity under high compression ratios, a
+token-level iterative compression algorithm to better model the interdependence
+between compressed contents, and an instruction tuning based method for
+distribution alignment between language models. We conduct experiments and
+analysis over four datasets from different scenarios, i.e., GSM8K, BBH,
+ShareGPT, and Arxiv-March23; showing that the proposed approach yields
+state-of-the-art performance and allows for up to 20x compression with little
+performance loss. Our code is available at https://aka.ms/LLMLingua.
+"
+Selective Demonstrations for Cross-domain Text-to-SQL,Shuaichen Chang,http://arxiv.org/pdf/2310.06302v1.pdf,2023-10-10,['cs.cl'],2310.06302v1.pdf,"  Large language models (LLMs) with in-context learning have demonstrated
+impressive generalization capabilities in the cross-domain text-to-SQL task,
+without the use of in-domain annotations. However, incorporating in-domain
+demonstration examples has been found to greatly enhance LLMs' performance. In
+this paper, we delve into the key factors within in-domain examples that
+contribute to the improvement and explore whether we can harness these benefits
+without relying on in-domain annotations. Based on our findings, we propose a
+demonstration selection framework ODIS which utilizes both out-of-domain
+examples and synthetically generated in-domain examples to construct
+demonstrations. By retrieving demonstrations from hybrid sources, ODIS
+leverages the advantages of both, showcasing its effectiveness compared to
+baseline methods that rely on a single data source. Furthermore, ODIS
+outperforms state-of-the-art approaches on two cross-domain text-to-SQL
+datasets, with improvements of 1.1 and 11.8 points in execution accuracy,
+respectively.
+"
+Jailbreak and Guard Aligned Language Models with Only Few In-Context  Demonstrations,Zeming Wei,http://arxiv.org/pdf/2310.06387v1.pdf,2023-10-10,"['cs.lg', 'cs.ai', 'cs.cl', 'cs.cr']",2310.06387v1.pdf,"  Large Language Models (LLMs) have shown remarkable success in various tasks,
+but concerns about their safety and the potential for generating malicious
+content have emerged. In this paper, we explore the power of In-Context
+Learning (ICL) in manipulating the alignment ability of LLMs. We find that by
+providing just few in-context demonstrations without fine-tuning, LLMs can be
+manipulated to increase or decrease the probability of jailbreaking, i.e.
+answering malicious prompts. Based on these observations, we propose In-Context
+Attack (ICA) and In-Context Defense (ICD) methods for jailbreaking and guarding
+aligned language model purposes. ICA crafts malicious contexts to guide models
+in generating harmful outputs, while ICD enhances model robustness by
+demonstrations of rejecting to answer harmful prompts. Our experiments show the
+effectiveness of ICA and ICD in increasing or reducing the success rate of
+adversarial jailbreaking attacks. Overall, we shed light on the potential of
+ICL to influence LLM behavior and provide a new perspective for enhancing the
+safety and alignment of LLMs.
+"
+Humans and language models diverge when predicting repeating text,Aditya R. Vaidya,http://arxiv.org/pdf/2310.06408v2.pdf,2023-10-10,['cs.cl'],2310.06408v2.pdf,"  Language models that are trained on the next-word prediction task have been
+shown to accurately model human behavior in word prediction and reading speed.
+In contrast with these findings, we present a scenario in which the performance
+of humans and LMs diverges. We collected a dataset of human next-word
+predictions for five stimuli that are formed by repeating spans of text. Human
+and GPT-2 LM predictions are strongly aligned in the first presentation of a
+text span, but their performance quickly diverges when memory (or in-context
+learning) begins to play a role. We traced the cause of this divergence to
+specific attention heads in a middle layer. Adding a power-law recency bias to
+these attention heads yielded a model that performs much more similarly to
+humans. We hope that this scenario will spur future work in bringing LMs closer
+to human behavior.
+"
+The Limits of ChatGPT in Extracting Aspect-Category-Opinion-Sentiment  Quadruples: A Comparative Analysis,Xiancai Xu,http://arxiv.org/pdf/2310.06502v1.pdf,2023-10-10,['cs.cl'],2310.06502v1.pdf,"  Recently, ChatGPT has attracted great attention from both industry and
+academia due to its surprising abilities in natural language understanding and
+generation. We are particularly curious about whether it can achieve promising
+performance on one of the most complex tasks in aspect-based sentiment
+analysis, i.e., extracting aspect-category-opinion-sentiment quadruples from
+texts. To this end, in this paper we develop a specialized prompt template that
+enables ChatGPT to effectively tackle this complex quadruple extraction task.
+Further, we propose a selection method on few-shot examples to fully exploit
+the in-context learning ability of ChatGPT and uplift its effectiveness on this
+complex task. Finally, we provide a comparative evaluation on ChatGPT against
+existing state-of-the-art quadruple extraction models based on four public
+datasets and highlight some important findings regarding the capability
+boundaries of ChatGPT in the quadruple extraction.
+"
+AMAGO: Scalable In-Context Reinforcement Learning for Adaptive Agents,Jake Grigsby,http://arxiv.org/pdf/2310.09971v2.pdf,2023-10-15,['cs.lg'],2310.09971v2.pdf,"  We introduce AMAGO, an in-context Reinforcement Learning (RL) agent that uses
+sequence models to tackle the challenges of generalization, long-term memory,
+and meta-learning. Recent works have shown that off-policy learning can make
+in-context RL with recurrent policies viable. Nonetheless, these approaches
+require extensive tuning and limit scalability by creating key bottlenecks in
+agents' memory capacity, planning horizon, and model size. AMAGO revisits and
+redesigns the off-policy in-context approach to successfully train
+long-sequence Transformers over entire rollouts in parallel with end-to-end RL.
+Our agent is uniquely scalable and applicable to a wide range of problems. We
+demonstrate its strong performance empirically in meta-RL and long-term memory
+domains. AMAGO's focus on sparse rewards and off-policy data also allows
+in-context learning to extend to goal-conditioned problems with challenging
+exploration. When combined with a novel hindsight relabeling scheme, AMAGO can
+solve a previously difficult category of open-world domains, where agents
+complete many possible instructions in procedurally generated environments. We
+evaluate our agent on three goal-conditioned domains and study how its
+individual improvements connect to create a generalist policy.
+"
+A Search for Prompts: Generating Structured Answers from Contracts,Adam Roegiest,http://arxiv.org/pdf/2310.10141v1.pdf,2023-10-16,['cs.cv'],2310.10141v1.pdf,"  In many legal processes being able to action on the concrete implication of a
+legal question can be valuable to automating human review or signalling certain
+conditions (e.g., alerts around automatic renewal). To support such tasks, we
+present a form of legal question answering that seeks to return one (or more)
+fixed answers for a question about a contract clause. After showing that
+unstructured generative question answering can have questionable outcomes for
+such a task, we discuss our exploration methodology for legal question
+answering prompts using OpenAI's \textit{GPT-3.5-Turbo} and provide a summary
+of insights.
+  Using insights gleaned from our qualitative experiences, we compare our
+proposed template prompts against a common semantic matching approach and find
+that our prompt templates are far more accurate despite being less reliable in
+the exact response return. With some additional tweaks to prompts and the use
+of in-context learning, we are able to further improve the performance of our
+proposed strategy while maximizing the reliability of responses as best we can.
+"
+Large Language Models Meet Open-World Intent Discovery and Recognition:  An Evaluation of ChatGPT,Xiaoshuai Song,http://arxiv.org/pdf/2310.10176v1.pdf,2023-10-16,"['cs.cl', 'cs.ai', 'cs.lg']",2310.10176v1.pdf,"  The tasks of out-of-domain (OOD) intent discovery and generalized intent
+discovery (GID) aim to extend a closed intent classifier to open-world intent
+sets, which is crucial to task-oriented dialogue (TOD) systems. Previous
+methods address them by fine-tuning discriminative models. Recently, although
+some studies have been exploring the application of large language models
+(LLMs) represented by ChatGPT to various downstream tasks, it is still unclear
+for the ability of ChatGPT to discover and incrementally extent OOD intents. In
+this paper, we comprehensively evaluate ChatGPT on OOD intent discovery and
+GID, and then outline the strengths and weaknesses of ChatGPT. Overall, ChatGPT
+exhibits consistent advantages under zero-shot settings, but is still at a
+disadvantage compared to fine-tuned models. More deeply, through a series of
+analytical experiments, we summarize and discuss the challenges faced by LLMs
+including clustering, domain-specific understanding, and cross-domain
+in-context learning scenarios. Finally, we provide empirical guidance for
+future directions to address these challenges.
+"
+MoConVQ: Unified Physics-Based Motion Control via Scalable Discrete  Representations,Heyuan Yao,http://arxiv.org/pdf/2310.10198v2.pdf,2023-10-16,"['cs.cv', 'cs.gr']",2310.10198v2.pdf,"  In this work, we present MoConVQ, a novel unified framework for physics-based
+motion control leveraging scalable discrete representations. Building upon
+vector quantized variational autoencoders (VQ-VAE) and model-based
+reinforcement learning, our approach effectively learns motion embeddings from
+a large, unstructured dataset spanning tens of hours of motion examples. The
+resultant motion representation not only captures diverse motion skills but
+also offers a robust and intuitive interface for various applications. We
+demonstrate the versatility of MoConVQ through several applications: universal
+tracking control from various motion sources, interactive character control
+with latent motion representations using supervised learning, physics-based
+motion generation from natural language descriptions using the GPT framework,
+and, most interestingly, seamless integration with large language models (LLMs)
+with in-context learning to tackle complex and abstract tasks.
+"
+Semantic Parsing by Large Language Models for Intricate Updating  Strategies of Zero-Shot Dialogue State Tracking,Yuxiang Wu,http://arxiv.org/pdf/2310.10520v2.pdf,2023-10-16,"['cs.cl', 'cs.ai', 'cs.lg']",2310.10520v2.pdf,"  Zero-shot Dialogue State Tracking (DST) addresses the challenge of acquiring
+and annotating task-oriented dialogues, which can be time consuming and costly.
+However, DST extends beyond simple slot-filling and requires effective updating
+strategies for tracking dialogue state as conversations progress. In this
+paper, we propose ParsingDST, a new In-Context Learning (ICL) method, to
+introduce additional intricate updating strategies in zero-shot DST. Our
+approach reformulates the DST task by leveraging powerful Large Language Models
+(LLMs) and translating the original dialogue text to JSON through semantic
+parsing as an intermediate state. We also design a novel framework that
+includes more modules to ensure the effectiveness of updating strategies in the
+text-to-JSON process. Experimental results demonstrate that our approach
+outperforms existing zero-shot DST methods on MultiWOZ, exhibiting significant
+improvements in Joint Goal Accuracy (JGA) and slot accuracy compared to
+existing ICL methods.
+"
+Mastering the Task of Open Information Extraction with Large Language  Models and Consistent Reasoning Environment,Ji Qi,http://arxiv.org/pdf/2310.10590v1.pdf,2023-10-16,['cs.cl'],2310.10590v1.pdf,"  Open Information Extraction (OIE) aims to extract objective structured
+knowledge from natural texts, which has attracted growing attention to build
+dedicated models with human experience. As the large language models (LLMs)
+have exhibited remarkable in-context learning capabilities, a question arises
+as to whether the task of OIE can be effectively tackled with this paradigm? In
+this paper, we explore solving the OIE problem by constructing an appropriate
+reasoning environment for LLMs. Specifically, we first propose a method to
+effectively estimate the discrepancy of syntactic distribution between a LLM
+and test samples, which can serve as correlation evidence for preparing
+positive demonstrations. Upon the evidence, we introduce a simple yet effective
+mechanism to establish the reasoning environment for LLMs on specific tasks.
+Without bells and whistles, experimental results on the standard CaRB benchmark
+demonstrate that our $6$-shot approach outperforms state-of-the-art supervised
+method, achieving an $55.3$ $F_1$ score. Further experiments on TACRED and
+ACE05 show that our method can naturally generalize to other information
+extraction tasks, resulting in improvements of $5.7$ and $6.8$ $F_1$ scores,
+respectively.
+"
+Exploring Automatic Evaluation Methods based on a Decoder-based LLM for  Text Generation,Tomohito Kasahara,http://arxiv.org/pdf/2310.11026v1.pdf,2023-10-17,['cs.cl'],2310.11026v1.pdf,"  Automatic evaluation of text generation is essential for improving the
+accuracy of generation tasks. In light of the current trend towards
+increasingly larger decoder-based language models, we investigate automatic
+evaluation methods based on such models for text generation. This paper
+compares various methods, including tuning with encoder-based models and large
+language models under equal conditions, on two different tasks, machine
+translation evaluation and semantic textual similarity, in two languages,
+Japanese and English. Experimental results show that compared to the tuned
+encoder-based models, the tuned decoder-based models perform poorly. The
+analysis of the causes for this suggests that the decoder-based models focus on
+surface word sequences and do not capture meaning. It is also revealed that
+in-context learning of very large decoder-based models such as ChatGPT makes it
+difficult to identify fine-grained semantic differences.
+"
+Learning from Red Teaming: Gender Bias Provocation and Mitigation in  Large Language Models,Hsuan Su,http://arxiv.org/pdf/2310.11079v1.pdf,2023-10-17,"['cs.cl', 'cs.ai']",2310.11079v1.pdf,"  Recently, researchers have made considerable improvements in dialogue systems
+with the progress of large language models (LLMs) such as ChatGPT and GPT-4.
+These LLM-based chatbots encode the potential biases while retaining
+disparities that can harm humans during interactions. The traditional biases
+investigation methods often rely on human-written test cases. However, these
+test cases are usually expensive and limited. In this work, we propose a
+first-of-its-kind method that automatically generates test cases to detect
+LLMs' potential gender bias. We apply our method to three well-known LLMs and
+find that the generated test cases effectively identify the presence of biases.
+To address the biases identified, we propose a mitigation strategy that uses
+the generated test cases as demonstrations for in-context learning to
+circumvent the need for parameter fine-tuning. The experimental results show
+that LLMs generate fairer responses with the proposed approach.
+"
+Evaluating LLMs for Privilege-Escalation Scenarios,Andreas Happe,http://arxiv.org/pdf/2310.11409v2.pdf,2023-10-17,"['cs.cr', 'cs.ai']",2310.11409v2.pdf,"  Penetration testing, an essential component of cybersecurity, allows
+organizations to proactively identify and remediate vulnerabilities in their
+systems, thus bolstering their defense mechanisms against potential
+cyberattacks. One recent advancement in the realm of penetration testing is the
+utilization of Language Models (LLMs). We explore the intersection of LLMs and
+penetration testing to gain insight into their capabilities and challenges in
+the context of privilige escalation. We create an automated Linux
+privilege-escalation benchmark utilizing local virtual machines. We introduce
+an LLM-guided privilege-escalation tool designed for evaluating different LLMs
+and prompt strategies against our benchmark. We analyze the impact of different
+prompt designs, the benefits of in-context learning, and the advantages of
+offering high-level guidance to LLMs. We discuss challenging areas for LLMs,
+including maintaining focus during testing, coping with errors, and finally
+comparing them with both stochastic parrots as well as with human hackers.
+"
+Measuring Pointwise $\mathcal{V}$-Usable Information In-Context-ly,Sheng Lu,http://arxiv.org/pdf/2310.12300v1.pdf,2023-10-18,['cs.cl'],2310.12300v1.pdf,"  In-context learning (ICL) is a new learning paradigm that has gained
+popularity along with the development of large language models. In this work,
+we adapt a recently proposed hardness metric, pointwise $\mathcal{V}$-usable
+information (PVI), to an in-context version (in-context PVI). Compared to the
+original PVI, in-context PVI is more efficient in that it requires only a few
+exemplars and does not require fine-tuning. We conducted a comprehensive
+empirical analysis to evaluate the reliability of in-context PVI. Our findings
+indicate that in-context PVI estimates exhibit similar characteristics to the
+original PVI. Specific to the in-context setting, we show that in-context PVI
+estimates remain consistent across different exemplar selections and numbers of
+shots. The variance of in-context PVI estimates across different exemplar
+selections is insignificant, which suggests that in-context PVI are stable.
+Furthermore, we demonstrate how in-context PVI can be employed to identify
+challenging instances. Our work highlights the potential of in-context PVI and
+provides new insights into the capabilities of ICL.
+"
+Attack Prompt Generation for Red Teaming and Defending Large Language  Models,Boyi Deng,http://arxiv.org/pdf/2310.12505v1.pdf,2023-10-19,"['cs.cl', 'cs.cr', 'cs.lg']",2310.12505v1.pdf,"  Large language models (LLMs) are susceptible to red teaming attacks, which
+can induce LLMs to generate harmful content. Previous research constructs
+attack prompts via manual or automatic methods, which have their own
+limitations on construction cost and quality. To address these issues, we
+propose an integrated approach that combines manual and automatic methods to
+economically generate high-quality attack prompts. Specifically, considering
+the impressive capabilities of newly emerged LLMs, we propose an attack
+framework to instruct LLMs to mimic human-generated prompts through in-context
+learning. Furthermore, we propose a defense framework that fine-tunes victim
+LLMs through iterative interactions with the attack framework to enhance their
+safety against red teaming attacks. Extensive experiments on different LLMs
+validate the effectiveness of our proposed attack and defense frameworks.
+Additionally, we release a series of attack prompts datasets named SAP with
+varying sizes, facilitating the safety evaluation and enhancement of more LLMs.
+Our code and dataset is available on https://github.com/Aatrox103/SAP .
+"
+Are Structural Concepts Universal in Transformer Language Models?  Towards Interpretable Cross-Lingual Generalization,Ningyu Xu,http://arxiv.org/pdf/2310.12794v1.pdf,2023-10-19,['cs.cl'],2310.12794v1.pdf,"  Large language models (LLMs) have exhibited considerable cross-lingual
+generalization abilities, whereby they implicitly transfer knowledge across
+languages. However, the transfer is not equally successful for all languages,
+especially for low-resource ones, which poses an ongoing challenge. It is
+unclear whether we have reached the limits of implicit cross-lingual
+generalization and if explicit knowledge transfer is viable. In this paper, we
+investigate the potential for explicitly aligning conceptual correspondence
+between languages to enhance cross-lingual generalization. Using the syntactic
+aspect of language as a testbed, our analyses of 43 languages reveal a high
+degree of alignability among the spaces of structural concepts within each
+language for both encoder-only and decoder-only LLMs. We then propose a
+meta-learning-based method to learn to align conceptual spaces of different
+languages, which facilitates zero-shot and few-shot generalization in concept
+classification and also offers insights into the cross-lingual in-context
+learning phenomenon. Experiments on syntactic analysis tasks show that our
+approach achieves competitive results with state-of-the-art methods and narrows
+the performance gap between languages, particularly benefiting those with
+limited resources.
+"
+Mind the instructions: a holistic evaluation of consistency and  interactions in prompt-based learning,Lucas Weber,http://arxiv.org/pdf/2310.13486v1.pdf,2023-10-20,"['cs.cl', 'cs.ai']",2310.13486v1.pdf,"  Finding the best way of adapting pre-trained language models to a task is a
+big challenge in current NLP. Just like the previous generation of task-tuned
+models (TT), models that are adapted to tasks via in-context-learning (ICL) are
+robust in some setups but not in others. Here, we present a detailed analysis
+of which design choices cause instabilities and inconsistencies in LLM
+predictions. First, we show how spurious correlations between input
+distributions and labels -- a known issue in TT models -- form only a minor
+problem for prompted models. Then, we engage in a systematic, holistic
+evaluation of different factors that have been found to influence predictions
+in a prompting setup. We test all possible combinations of a range of factors
+on both vanilla and instruction-tuned (IT) LLMs of different scale and
+statistically analyse the results to show which factors are the most
+influential, interactive or stable. Our results show which factors can be used
+without precautions and which should be avoided or handled with care in most
+settings.
+"
+A Simple Baseline for Knowledge-Based Visual Question Answering,Alexandros Xenos,http://arxiv.org/pdf/2310.13570v2.pdf,2023-10-20,['cs.cv'],2310.13570v2.pdf,"  This paper is on the problem of Knowledge-Based Visual Question Answering
+(KB-VQA). Recent works have emphasized the significance of incorporating both
+explicit (through external databases) and implicit (through LLMs) knowledge to
+answer questions requiring external knowledge effectively. A common limitation
+of such approaches is that they consist of relatively complicated pipelines and
+often heavily rely on accessing GPT-3 API. Our main contribution in this paper
+is to propose a much simpler and readily reproducible pipeline which, in a
+nutshell, is based on efficient in-context learning by prompting LLaMA (1 and
+2) using question-informative captions as contextual information. Contrary to
+recent approaches, our method is training-free, does not require access to
+external databases or APIs, and yet achieves state-of-the-art accuracy on the
+OK-VQA and A-OK-VQA datasets. Finally, we perform several ablation studies to
+understand important aspects of our method. Our code is publicly available at
+https://github.com/alexandrosXe/ASimple-Baseline-For-Knowledge-Based-VQA
+"
+An In-Context Schema Understanding Method for Knowledge Base Question  Answering,Yantao Liu,http://arxiv.org/pdf/2310.14174v1.pdf,2023-10-22,['cs.cl'],2310.14174v1.pdf,"  The Knowledge Base Question Answering (KBQA) task aims to answer natural
+language questions based on a given knowledge base. As a kind of common method
+for this task, semantic parsing-based ones first convert natural language
+questions to logical forms (e.g., SPARQL queries) and then execute them on
+knowledge bases to get answers. Recently, Large Language Models (LLMs) have
+shown strong abilities in language understanding and may be adopted as semantic
+parsers in such kinds of methods. However, in doing so, a great challenge for
+LLMs is to understand the schema of knowledge bases. Therefore, in this paper,
+we propose an In-Context Schema Understanding (ICSU) method for facilitating
+LLMs to be used as a semantic parser in KBQA. Specifically, ICSU adopts the
+In-context Learning mechanism to instruct LLMs to generate SPARQL queries with
+examples. In order to retrieve appropriate examples from annotated
+question-query pairs, which contain comprehensive schema information related to
+questions, ICSU explores four different retrieval strategies. Experimental
+results on the largest KBQA benchmark, KQA Pro, show that ICSU with all these
+strategies outperforms that with a random retrieval strategy significantly
+(from 12\% to 78.76\% in accuracy).
+"
+From Chaos to Clarity: Claim Normalization to Empower Fact-Checking,Megha Sundriyal,http://arxiv.org/pdf/2310.14338v1.pdf,2023-10-22,"['cs.cl', 'cs.ai']",2310.14338v1.pdf,"  With the proliferation of social media platforms, users are exposed to vast
+information, including posts containing misleading claims. However, the
+pervasive noise inherent in these posts presents a challenge in identifying
+precise and prominent claims that require verification. Extracting the core
+assertions from such posts is arduous and time-consuming. We introduce a novel
+task called Claim Normalization (aka ClaimNorm) that aims to decompose complex
+and noisy social media posts into more straightforward and understandable
+forms, termed normalized claims. We propose CACN, a pioneering approach that
+leverages chain-of-thought and claim check-worthiness estimation, mimicking
+human reasoning processes, to comprehend intricate claims. Moreover, we
+capitalize on large language models' powerful in-context learning abilities to
+provide guidance and improve the claim normalization process. To evaluate the
+effectiveness of our proposed model, we meticulously compile a comprehensive
+real-world dataset, CLAN, comprising more than 6k instances of social media
+posts alongside their respective normalized claims. Experimentation
+demonstrates that CACN outperforms several baselines across various evaluation
+measures. A rigorous error analysis validates CACN's capabilities and pitfalls.
+"
+Retrieval-Augmented Chain-of-Thought in Semi-structured Domains,Vaibhav Mavi,http://arxiv.org/pdf/2310.14435v1.pdf,2023-10-22,"['cs.cl', 'cs.ai']",2310.14435v1.pdf,"  Applying existing question answering (QA) systems to specialized domains like
+law and finance presents challenges that necessitate domain expertise. Although
+large language models (LLMs) have shown impressive language comprehension and
+in-context learning capabilities, their inability to handle very long
+inputs/contexts is well known. Tasks specific to these domains need significant
+background knowledge, leading to contexts that can often exceed the maximum
+length that existing LLMs can process. This study explores leveraging the
+semi-structured nature of legal and financial data to efficiently retrieve
+relevant context, enabling the use of LLMs for domain-specialized QA. The
+resulting system outperforms contemporary models and also provides useful
+explanations for the answers, encouraging the integration of LLMs into legal
+and financial NLP systems for future research.
+"
+Statistical Depth for Ranking and Characterizing Transformer-Based Text  Embeddings,Parker Seegmiller,http://arxiv.org/pdf/2310.15010v1.pdf,2023-10-23,['cs.cl'],2310.15010v1.pdf,"  The popularity of transformer-based text embeddings calls for better
+statistical tools for measuring distributions of such embeddings. One such tool
+would be a method for ranking texts within a corpus by centrality, i.e.
+assigning each text a number signifying how representative that text is of the
+corpus as a whole. However, an intrinsic center-outward ordering of
+high-dimensional text representations is not trivial. A statistical depth is a
+function for ranking k-dimensional objects by measuring centrality with respect
+to some observed k-dimensional distribution. We adopt a statistical depth to
+measure distributions of transformer-based text embeddings, transformer-based
+text embedding (TTE) depth, and introduce the practical use of this depth for
+both modeling and distributional inference in NLP pipelines. We first define
+TTE depth and an associated rank sum test for determining whether two corpora
+differ significantly in embedding space. We then use TTE depth for the task of
+in-context learning prompt selection, showing that this approach reliably
+improves performance over statistical baseline approaches across six text
+classification tasks. Finally, we use TTE depth and the associated rank sum
+test to characterize the distributions of synthesized and human-generated
+corpora, showing that five recent synthetic data augmentation processes cause a
+measurable distributional shift away from associated human-generated text.
+"
+Meta- (out-of-context) learning in neural networks,Dmitrii Krasheninnikov,http://arxiv.org/pdf/2310.15047v2.pdf,2023-10-23,"['cs.lg', 'cs.ai']",2310.15047v2.pdf,"  Brown et al. (2020) famously introduced the phenomenon of in-context learning
+in large language models (LLMs). We establish the existence of a phenomenon we
+call meta-out-of-context learning (meta-OCL) via carefully designed synthetic
+experiments with LLMs. Our results suggest that meta-OCL leads LLMs to more
+readily ""internalize"" the semantic content of text that is, or appears to be,
+broadly useful (such as true statements, or text from authoritative sources)
+and use it in appropriate circumstances. We further demonstrate meta-OCL in a
+synthetic computer vision setting, and propose two hypotheses for the emergence
+of meta-OCL: one relying on the way models store knowledge in their parameters,
+and another suggesting that the implicit gradient alignment bias of
+gradient-descent-based optimizers may be responsible. Finally, we reflect on
+what our results might imply about capabilities of future AI systems, and
+discuss potential risks. Our code can be found at
+https://github.com/krasheninnikov/internalization.
+"
+The BLA Benchmark: Investigating Basic Language Abilities of Pre-Trained  Multimodal Models,Xinyi Chen,http://arxiv.org/pdf/2310.15061v1.pdf,2023-10-23,"['cs.cl', 'cs.ai', 'cs.cv']",2310.15061v1.pdf,"  Despite the impressive performance achieved by pre-trained
+language-and-vision models in downstream tasks, it remains an open question
+whether this reflects a proper understanding of image-text interaction. In this
+work, we explore to what extent they handle basic linguistic constructions --
+active-passive voice, coordination, and relative clauses -- that even preschool
+children can typically master. We present BLA, a novel, automatically
+constructed benchmark to evaluate multimodal models on these Basic Language
+Abilities. We show that different types of Transformer-based systems, such as
+CLIP, ViLBERT, and BLIP2, generally struggle with BLA in a zero-shot setting,
+in line with previous findings. Our experiments, in particular, show that most
+of the tested models only marginally benefit when fine-tuned or prompted with
+construction-specific samples. Yet, the generative BLIP2 shows promising
+trends, especially in an in-context learning setting. This opens the door to
+using BLA not only as an evaluation benchmark but also to improve models' basic
+language abilities.
+"
+LLM-in-the-loop: Leveraging Large Language Model for Thematic Analysis,Shih-Chieh Dai,http://arxiv.org/pdf/2310.15100v1.pdf,2023-10-23,['cs.cl'],2310.15100v1.pdf,"  Thematic analysis (TA) has been widely used for analyzing qualitative data in
+many disciplines and fields. To ensure reliable analysis, the same piece of
+data is typically assigned to at least two human coders. Moreover, to produce
+meaningful and useful analysis, human coders develop and deepen their data
+interpretation and coding over multiple iterations, making TA labor-intensive
+and time-consuming. Recently the emerging field of large language models (LLMs)
+research has shown that LLMs have the potential replicate human-like behavior
+in various tasks: in particular, LLMs outperform crowd workers on
+text-annotation tasks, suggesting an opportunity to leverage LLMs on TA. We
+propose a human-LLM collaboration framework (i.e., LLM-in-the-loop) to conduct
+TA with in-context learning (ICL). This framework provides the prompt to frame
+discussions with a LLM (e.g., GPT-3.5) to generate the final codebook for TA.
+We demonstrate the utility of this framework using survey datasets on the
+aspects of the music listening experience and the usage of a password manager.
+Results of the two case studies show that the proposed framework yields similar
+coding quality to that of human coders but reduces TA's labor and time demands.
+"
+UI Layout Generation with LLMs Guided by UI Grammar,Yuwen Lu,http://arxiv.org/pdf/2310.15455v1.pdf,2023-10-24,"['cs.hc', 'cs.ai']",2310.15455v1.pdf,"  The recent advances in Large Language Models (LLMs) have stimulated interest
+among researchers and industry professionals, particularly in their application
+to tasks concerning mobile user interfaces (UIs). This position paper
+investigates the use of LLMs for UI layout generation. Central to our
+exploration is the introduction of UI grammar -- a novel approach we proposed
+to represent the hierarchical structure inherent in UI screens. The aim of this
+approach is to guide the generative capacities of LLMs more effectively and
+improve the explainability and controllability of the process. Initial
+experiments conducted with GPT-4 showed the promising capability of LLMs to
+produce high-quality user interfaces via in-context learning. Furthermore, our
+preliminary comparative study suggested the potential of the grammar-based
+approach in improving the quality of generative results in specific aspects.
+"
+POE: Process of Elimination for Multiple Choice Reasoning,Chenkai Ma,http://arxiv.org/pdf/2310.15575v1.pdf,2023-10-24,['cs.cl'],2310.15575v1.pdf,"  Language models (LMs) are capable of conducting in-context learning for
+multiple choice reasoning tasks, but the options in these tasks are treated
+equally. As humans often first eliminate wrong options before picking the final
+correct answer, we argue a similar two-step strategy can make LMs better at
+these tasks. To this end, we present the Process of Elimination (POE), a
+two-step scoring method. In the first step, POE scores each option, and
+eliminates seemingly wrong options. In the second step, POE masks these wrong
+options, and makes the final prediction from the remaining options. Zero-shot
+experiments on 8 reasoning tasks illustrate the effectiveness of POE, and a
+following analysis finds our method to be especially performant on logical
+reasoning tasks. We further analyze the effect of masks, and show that POE
+applies to few-shot settings and large language models (LLMs) like ChatGPT.
+"
+WebWISE: Web Interface Control and Sequential Exploration with Large  Language Models,Heyi Tao,http://arxiv.org/pdf/2310.16042v2.pdf,2023-10-24,"['cs.cl', 'cs.ai']",2310.16042v2.pdf,"  The paper investigates using a Large Language Model (LLM) to automatically
+perform web software tasks using click, scroll, and text input operations.
+Previous approaches, such as reinforcement learning (RL) or imitation learning,
+are inefficient to train and task-specific. Our method uses filtered Document
+Object Model (DOM) elements as observations and performs tasks step-by-step,
+sequentially generating small programs based on the current observations. We
+use in-context learning, either benefiting from a single manually provided
+example, or an automatically generated example based on a successful zero-shot
+trial. We evaluate the proposed method on the MiniWob++ benchmark. With only
+one in-context example, our WebWISE method achieves similar or better
+performance than other methods that require many demonstrations or trials.
+"
+From Heuristic to Analytic: Cognitively Motivated Strategies for  Coherent Physical Commonsense Reasoning,Zheyuan Zhang,http://arxiv.org/pdf/2310.18364v1.pdf,2023-10-24,"['cs.cl', 'cs.ai']",2310.18364v1.pdf,"  Pre-trained language models (PLMs) have shown impressive performance in
+various language tasks. However, they are prone to spurious correlations, and
+often generate illusory information. In real-world applications, PLMs should
+justify decisions with formalized, coherent reasoning chains, but this
+challenge remains under-explored. Cognitive psychology theorizes that humans
+are capable of utilizing fast and intuitive heuristic thinking to make
+decisions based on past experience, then rationalizing the decisions through
+slower and deliberative analytic reasoning. We incorporate these interlinked
+dual processes in fine-tuning and in-context learning with PLMs, applying them
+to two language understanding tasks that require coherent physical commonsense
+reasoning. We show that our proposed Heuristic-Analytic Reasoning (HAR)
+strategies drastically improve the coherence of rationalizations for model
+decisions, yielding state-of-the-art results on Tiered Reasoning for Intuitive
+Physics (TRIP). We also find that this improved coherence is a direct result of
+more faithful attention to relevant language context in each step of reasoning.
+Our findings suggest that human-like reasoning strategies can effectively
+improve the coherence and reliability of PLM reasoning.
+"
+The Mystery and Fascination of LLMs: A Comprehensive Survey on the  Interpretation and Analysis of Emergent Abilities,Yuxiang Zhou,http://arxiv.org/pdf/2311.00237v1.pdf,2023-11-01,['cs.cl'],2311.00237v1.pdf,"  Understanding emergent abilities, such as in-context learning (ICL) and
+chain-of-thought (CoT) prompting in large language models (LLMs), is of utmost
+importance. This importance stems not only from the better utilization of these
+capabilities across various tasks, but also from the proactive identification
+and mitigation of potential risks, including concerns of truthfulness, bias,
+and toxicity, that may arise alongside these capabilities. In this paper, we
+present a thorough survey on the interpretation and analysis of emergent
+abilities of LLMs. First, we provide a concise introduction to the background
+and definition of emergent abilities. Then, we give an overview of advancements
+from two perspectives: 1) a macro perspective, emphasizing studies on the
+mechanistic interpretability and delving into the mathematical foundations
+behind emergent abilities; and 2) a micro-perspective, concerning studies that
+focus on empirical interpretability by examining factors associated with these
+abilities. We conclude by highlighting the challenges encountered and
+suggesting potential avenues for future research. We believe that our work
+establishes the basis for further exploration into the interpretation of
+emergent abilities.
+"
+Narrowing the Gap between Zero- and Few-shot Machine Translation by  Matching Styles,Weiting Tan,http://arxiv.org/pdf/2311.02310v1.pdf,2023-11-04,['cs.cl'],2311.02310v1.pdf,"  Large language models trained primarily in a monolingual setting have
+demonstrated their ability to generalize to machine translation using zero- and
+few-shot examples with in-context learning. However, even though zero-shot
+translations are relatively good, there remains a discernible gap comparing
+their performance with the few-shot setting. In this paper, we investigate the
+factors contributing to this gap and find that this gap can largely be closed
+(for about 70%) by matching the writing styles of the target corpus.
+Additionally, we explore potential approaches to enhance zero-shot baselines
+without the need for parallel demonstration examples, providing valuable
+insights into how these methods contribute to improving translation metrics.
+"
+Instructed Language Models with Retrievers Are Powerful Entity Linkers,Zilin Xiao,http://arxiv.org/pdf/2311.03250v1.pdf,2023-11-06,"['cs.cl', 'cs.ai']",2311.03250v1.pdf,"  Generative approaches powered by large language models (LLMs) have
+demonstrated emergent abilities in tasks that require complex reasoning
+abilities. Yet the generative nature still makes the generated content suffer
+from hallucinations, thus unsuitable for entity-centric tasks like entity
+linking (EL) requiring precise entity predictions over a large knowledge base.
+We present Instructed Generative Entity Linker (INSGENEL), the first approach
+that enables casual language models to perform entity linking over knowledge
+bases. Several methods to equip language models with EL capability were
+proposed in this work, including (i) a sequence-to-sequence training EL
+objective with instruction-tuning, (ii) a novel generative EL framework based
+on a light-weight potential mention retriever that frees the model from heavy
+and non-parallelizable decoding, achieving 4$\times$ speedup without compromise
+on linking metrics. INSGENEL outperforms previous generative alternatives with
++6.8 F1 points gain on average, also with a huge advantage in training data
+efficiency and training compute consumption. In addition, our skillfully
+engineered in-context learning (ICL) framework for EL still lags behind
+INSGENEL significantly, reaffirming that the EL task remains a persistent
+hurdle for general LLMs.
+"
+Meta-learning via Language Model In-context Tuning,Yanda Chen,http://arxiv.org/pdf/2110.07814v2.pdf,2021-10-15,"['cs.cl', 'cs.lg']",2110.07814v2.pdf,"  The goal of meta-learning is to learn to adapt to a new task with only a few
+labeled examples. To tackle this problem in NLP, we propose $\textit{in-context
+tuning}$, which recasts adaptation and prediction as a simple sequence
+prediction problem: to form the input sequence, we concatenate the task
+instruction, the labeled examples, and the target input to predict; to
+meta-train the model to learn from in-context examples, we fine-tune a
+pre-trained language model (LM) to predict the target label from the input
+sequences on a collection of tasks.
+  We benchmark our method on two collections of text classification tasks: LAMA
+and BinaryClfs. Compared to first-order MAML which adapts the model with
+gradient descent, our method better leverages the inductive bias of LMs to
+perform pattern matching, and outperforms MAML by an absolute $6\%$ AUC ROC
+score on BinaryClfs, with increasing advantage w.r.t. model size. Compared to
+non-fine-tuned in-context learning (i.e. prompting a raw LM), in-context tuning
+directly learns to learn from in-context examples. On BinaryClfs, in-context
+tuning improves the average AUC-ROC score by an absolute $10\%$, and reduces
+the variance with respect to example ordering by 6x and example choices by 2x.
+"
+Good Examples Make A Faster Learner: Simple Demonstration-based Learning  for Low-resource NER,Dong-Ho Lee,http://arxiv.org/pdf/2110.08454v3.pdf,2021-10-16,['cs.cl'],2110.08454v3.pdf,"  Recent advances in prompt-based learning have shown strong results on
+few-shot text classification by using cloze-style templates. Similar attempts
+have been made on named entity recognition (NER) which manually design
+templates to predict entity types for every text span in a sentence. However,
+such methods may suffer from error propagation induced by entity span
+detection, high cost due to enumeration of all possible text spans, and
+omission of inter-dependencies among token labels in a sentence. Here we
+present a simple demonstration-based learning method for NER, which lets the
+input be prefaced by task demonstrations for in-context learning. We perform a
+systematic study on demonstration strategy regarding what to include (entity
+examples, with or without surrounding context), how to select the examples, and
+what templates to use. Results on in-domain learning and domain adaptation show
+that the model's performance in low-resource settings can be largely improved
+with a suitable demonstration strategy (e.g., a 4-17% improvement on 25 train
+instances). We also find that good demonstration can save many labeled examples
+and consistency in demonstration contributes to better performance.
+"
+GLaM: Efficient Scaling of Language Models with Mixture-of-Experts,Nan Du,http://arxiv.org/pdf/2112.06905v2.pdf,2021-12-13,['cs.cl'],2112.06905v2.pdf,"  Scaling language models with more data, compute and parameters has driven
+significant progress in natural language processing. For example, thanks to
+scaling, GPT-3 was able to achieve strong results on in-context learning tasks.
+However, training these large dense models requires significant amounts of
+computing resources. In this paper, we propose and develop a family of language
+models named GLaM (Generalist Language Model), which uses a sparsely activated
+mixture-of-experts architecture to scale the model capacity while also
+incurring substantially less training cost compared to dense variants. The
+largest GLaM has 1.2 trillion parameters, which is approximately 7x larger than
+GPT-3. It consumes only 1/3 of the energy used to train GPT-3 and requires half
+of the computation flops for inference, while still achieving better overall
+zero-shot and one-shot performance across 29 NLP tasks.
+"
+Can language models learn from explanations in context?,Andrew K. Lampinen,http://arxiv.org/pdf/2204.02329v4.pdf,2022-04-05,"['cs.cl', 'cs.ai', 'cs.lg']",2204.02329v4.pdf,"  Language Models (LMs) can perform new tasks by adapting to a few in-context
+examples. For humans, explanations that connect examples to task principles can
+improve learning. We therefore investigate whether explanations of few-shot
+examples can help LMs. We annotate questions from 40 challenging tasks with
+answer explanations, and various matched control explanations. We evaluate how
+different types of explanations, instructions, and controls affect zero- and
+few-shot performance. We analyze these results using statistical multilevel
+modeling techniques that account for the nested dependencies among conditions,
+tasks, prompts, and models. We find that explanations can improve performance
+-- even without tuning. Furthermore, explanations hand-tuned for performance on
+a small validation set offer substantially larger benefits, and building a
+prompt by selecting examples and explanations together substantially improves
+performance over selecting examples alone. Finally, even untuned explanations
+outperform carefully matched controls, suggesting that the benefits are due to
+the link between an example and its explanation, rather than lower-level
+features. However, only large models benefit. In summary, explanations can
+support the in-context learning of large LMs on challenging tasks.
+"
+Automatic Short Math Answer Grading via In-context Meta-learning,Mengxue Zhang,http://arxiv.org/pdf/2205.15219v3.pdf,2022-05-30,"['cs.cl', 'cs.lg']",2205.15219v3.pdf,"  Automatic short answer grading is an important research direction in the
+exploration of how to use artificial intelligence (AI)-based tools to improve
+education. Current state-of-the-art approaches use neural language models to
+create vectorized representations of students responses, followed by
+classifiers to predict the score. However, these approaches have several key
+limitations, including i) they use pre-trained language models that are not
+well-adapted to educational subject domains and/or student-generated text and
+ii) they almost always train one model per question, ignoring the linkage
+across a question and result in a significant model storage problem due to the
+size of advanced language models. In this paper, we study the problem of
+automatic short answer grading for students' responses to math questions and
+propose a novel framework for this task. First, we use MathBERT, a variant of
+the popular language model BERT adapted to mathematical content, as our base
+model and fine-tune it for the downstream task of student response grading.
+Second, we use an in-context learning approach that provides scoring examples
+as input to the language model to provide additional context information and
+promote generalization to previously unseen questions. We evaluate our
+framework on a real-world dataset of student responses to open-ended math
+questions and show that our framework (often significantly) outperforms
+existing approaches, especially for new questions that are not seen during
+training.
+"
+ThinkSum: Probabilistic reasoning over sets using large language models,Batu Ozturkler,http://arxiv.org/pdf/2210.01293v2.pdf,2022-10-04,['cs.cl'],2210.01293v2.pdf,"  Large language models (LLMs) have a substantial capacity for high-level
+analogical reasoning: reproducing patterns in linear text that occur in their
+training data (zero-shot evaluation) or in the provided context (few-shot
+in-context learning). However, recent studies show that even the more advanced
+LLMs fail in scenarios that require reasoning over multiple objects or facts
+and making sequences of logical deductions. We propose a two-stage
+probabilistic inference paradigm, ThinkSum, which reasons over sets of objects
+or facts in a structured manner. In the first stage (Think - retrieval of
+associations), a LLM is queried in parallel over a set of phrases extracted
+from the prompt or an auxiliary model call. In the second stage (Sum -
+probabilistic inference or reasoning), the results of these queries are
+aggregated to make the final prediction. We demonstrate the possibilities and
+advantages of ThinkSum on the BIG-bench suite of LLM evaluation tasks,
+achieving improvements over the state of the art using GPT-family models on
+thirteen difficult tasks, often with far smaller model variants. We also
+compare and contrast ThinkSum with other proposed modifications to direct
+prompting of LLMs, such as variants of chain-of-thought prompting. Our results
+suggest that because the probabilistic inference in ThinkSum is performed
+outside of calls to the LLM, ThinkSum is less sensitive to prompt design,
+yields more interpretable predictions, and can be flexibly combined with latent
+variable models to extract structured knowledge from LLMs. Overall, our
+proposed paradigm represents a promising approach for enhancing the reasoning
+capabilities of LLMs.
+"
+Honest Students from Untrusted Teachers: Learning an Interpretable  Question-Answering Pipeline from a Pretrained Language Model,Jacob Eisenstein,http://arxiv.org/pdf/2210.02498v2.pdf,2022-10-05,"['cs.cl', 'cs.lg']",2210.02498v2.pdf,"  Explainable question answering systems should produce not only accurate
+answers but also rationales that justify their reasoning and allow humans to
+check their work. But what sorts of rationales are useful and how can we train
+systems to produce them? We propose a new style of rationale for open-book
+question answering, called \emph{markup-and-mask}, which combines aspects of
+extractive and free-text explanations. In the markup phase, the passage is
+augmented with free-text markup that enables each sentence to stand on its own
+outside the discourse context. In the masking phase, a sub-span of the
+marked-up passage is selected. To train a system to produce markup-and-mask
+rationales without annotations, we leverage in-context learning. Specifically,
+we generate silver annotated data by sending a series of prompts to a frozen
+pretrained language model, which acts as a teacher. We then fine-tune a smaller
+student model by training on the subset of rationales that led to correct
+answers. The student is ""honest"" in the sense that it is a pipeline: the
+rationale acts as a bottleneck between the passage and the answer, while the
+""untrusted"" teacher operates under no such constraints. Thus, we offer a new
+way to build trustworthy pipeline systems from a combination of end-task
+annotations and frozen pretrained language models.
+"
+Large Language Models can Implement Policy Iteration,Ethan Brooks,http://arxiv.org/pdf/2210.03821v2.pdf,2022-10-07,['cs.lg'],2210.03821v2.pdf,"  This work presents In-Context Policy Iteration, an algorithm for performing
+Reinforcement Learning (RL), in-context, using foundation models. While the
+application of foundation models to RL has received considerable attention,
+most approaches rely on either (1) the curation of expert demonstrations
+(either through manual design or task-specific pretraining) or (2) adaptation
+to the task of interest using gradient methods (either fine-tuning or training
+of adapter layers). Both of these techniques have drawbacks. Collecting
+demonstrations is labor-intensive, and algorithms that rely on them do not
+outperform the experts from which the demonstrations were derived. All gradient
+techniques are inherently slow, sacrificing the ""few-shot"" quality that made
+in-context learning attractive to begin with. In this work, we present an
+algorithm, ICPI, that learns to perform RL tasks without expert demonstrations
+or gradients. Instead we present a policy-iteration method in which the prompt
+content is the entire locus of learning. ICPI iteratively updates the contents
+of the prompt from which it derives its policy through trial-and-error
+interaction with an RL environment. In order to eliminate the role of
+in-weights learning (on which approaches like Decision Transformer rely
+heavily), we demonstrate our algorithm using Codex, a language model with no
+prior knowledge of the domains on which we evaluate it.
+"
+Transformers generalize differently from information stored in context  vs in weights,Stephanie C. Y. Chan,http://arxiv.org/pdf/2210.05675v2.pdf,2022-10-11,"['cs.cl', 'cs.ai', 'cs.lg']",2210.05675v2.pdf,"  Transformer models can use two fundamentally different kinds of information:
+information stored in weights during training, and information provided
+``in-context'' at inference time. In this work, we show that transformers
+exhibit different inductive biases in how they represent and generalize from
+the information in these two sources. In particular, we characterize whether
+they generalize via parsimonious rules (rule-based generalization) or via
+direct comparison with observed examples (exemplar-based generalization). This
+is of important practical consequence, as it informs whether to encode
+information in weights or in context, depending on how we want models to use
+that information. In transformers trained on controlled stimuli, we find that
+generalization from weights is more rule-based whereas generalization from
+context is largely exemplar-based. In contrast, we find that in transformers
+pre-trained on natural language, in-context learning is significantly
+rule-based, with larger models showing more rule-basedness. We hypothesise that
+rule-based generalization from in-context information might be an emergent
+consequence of large-scale training on language, which has sparse rule-like
+structure. Using controlled stimuli, we verify that transformers pretrained on
+data containing sparse rule-like structure exhibit more rule-based
+generalization.
+"
+Large Language Models Meet Harry Potter: A Bilingual Dataset for  Aligning Dialogue Agents with Characters,Nuo Chen,http://arxiv.org/pdf/2211.06869v4.pdf,2022-11-13,"['cs.cl', 'cs.ai']",2211.06869v4.pdf,"  In recent years, Dialogue-style Large Language Models (LLMs) such as ChatGPT
+and GPT4 have demonstrated immense potential in constructing open-domain
+dialogue agents. However, aligning these agents with specific characters or
+individuals remains a considerable challenge due to the complexities of
+character representation and the lack of comprehensive annotations. In this
+paper, we introduce the Harry Potter Dialogue (HPD) dataset, designed to
+advance the study of dialogue agents and character alignment. The dataset
+encompasses all dialogue sessions (in both English and Chinese) from the Harry
+Potter series and is annotated with vital background information, including
+dialogue scenes, speakers, character relationships, and attributes. These
+extensive annotations may empower LLMs to unlock character-driven dialogue
+capabilities. Furthermore, it can serve as a universal benchmark for evaluating
+how well can a LLM aligning with a specific character. We benchmark LLMs on HPD
+using both fine-tuning and in-context learning settings. Evaluation results
+reveal that although there is substantial room for improvement in generating
+high-quality, character-aligned responses, the proposed dataset is valuable in
+guiding models toward responses that better align with the character of Harry
+Potter.
+"
+Retrieval-Augmented Multimodal Language Modeling,Michihiro Yasunaga,http://arxiv.org/pdf/2211.12561v2.pdf,2022-11-22,"['cs.cv', 'cs.cl', 'cs.lg']",2211.12561v2.pdf,"  Recent multimodal models such as DALL-E and CM3 have achieved remarkable
+progress in text-to-image and image-to-text generation. However, these models
+store all learned knowledge (e.g., the appearance of the Eiffel Tower) in the
+model parameters, requiring increasingly larger models and training data to
+capture more knowledge. To integrate knowledge in a more scalable and modular
+way, we propose a retrieval-augmented multimodal model, which enables a base
+multimodal model (generator) to refer to relevant text and images fetched by a
+retriever from external memory (e.g., documents on the web). Specifically, for
+the retriever, we use a pretrained CLIP, and for the generator, we train a CM3
+Transformer on the LAION dataset. Our resulting model, named
+Retrieval-Augmented CM3 (RA-CM3), is the first multimodal model that can
+retrieve and generate both text and images. We show that RA-CM3 significantly
+outperforms baseline multimodal models such as DALL-E and CM3 on both image and
+caption generation tasks (12 FID and 17 CIDEr improvements on MS-COCO), while
+requiring much less compute for training (<30% of DALL-E). Moreover, we show
+that RA-CM3 exhibits novel capabilities, such as faithful image generation and
+multimodal in-context learning (e.g., image generation from demonstrations).
+"
+"Operationalizing Specifications, In Addition to Test Sets for Evaluating  Constrained Generative Models",Vikas Raunak,http://arxiv.org/pdf/2212.00006v1.pdf,2022-11-19,"['cs.hc', 'cs.cl', 'cs.cv', 'cs.cy']",2212.00006v1.pdf,"  In this work, we present some recommendations on the evaluation of
+state-of-the-art generative models for constrained generation tasks. The
+progress on generative models has been rapid in recent years. These large-scale
+models have had three impacts: firstly, the fluency of generation in both
+language and vision modalities has rendered common average-case evaluation
+metrics much less useful in diagnosing system errors. Secondly, the same
+substrate models now form the basis of a number of applications, driven both by
+the utility of their representations as well as phenomena such as in-context
+learning, which raise the abstraction level of interacting with such models.
+Thirdly, the user expectations around these models and their feted public
+releases have made the technical challenge of out of domain generalization much
+less excusable in practice. Subsequently, our evaluation methodologies haven't
+adapted to these changes. More concretely, while the associated utility and
+methods of interacting with generative models have expanded, a similar
+expansion has not been observed in their evaluation practices. In this paper,
+we argue that the scale of generative models could be exploited to raise the
+abstraction level at which evaluation itself is conducted and provide
+recommendations for the same. Our recommendations are based on leveraging
+specifications as a powerful instrument to evaluate generation quality and are
+readily applicable to a variety of tasks.
+"
+Language model acceptability judgements are not always robust to context,Koustuv Sinha,http://arxiv.org/pdf/2212.08979v1.pdf,2022-12-18,"['cs.cl', 'cs.lg']",2212.08979v1.pdf,"  Targeted syntactic evaluations of language models ask whether models show
+stable preferences for syntactically acceptable content over minimal-pair
+unacceptable inputs. Most targeted syntactic evaluation datasets ask models to
+make these judgements with just a single context-free sentence as input. This
+does not match language models' training regime, in which input sentences are
+always highly contextualized by the surrounding corpus. This mismatch raises an
+important question: how robust are models' syntactic judgements in different
+contexts? In this paper, we investigate the stability of language models'
+performance on targeted syntactic evaluations as we vary properties of the
+input context: the length of the context, the types of syntactic phenomena it
+contains, and whether or not there are violations of grammaticality. We find
+that model judgements are generally robust when placed in randomly sampled
+linguistic contexts. However, they are substantially unstable for contexts
+containing syntactic structures matching those in the critical test content.
+Among all tested models (GPT-2 and five variants of OPT), we significantly
+improve models' judgements by providing contexts with matching syntactic
+structures, and conversely significantly worsen them using unacceptable
+contexts with matching but violated syntactic structures. This effect is
+amplified by the length of the context, except for unrelated inputs. We show
+that these changes in model performance are not explainable by simple features
+matching the context and the test inputs, such as lexical overlap and
+dependency overlap. This sensitivity to highly specific syntactic features of
+the context can only be explained by the models' implicit in-context learning
+abilities.
+"
+Low-Resource Authorship Style Transfer: Can Non-Famous Authors Be  Imitated?,Ajay Patel,http://arxiv.org/pdf/2212.08986v2.pdf,2022-12-18,['cs.cl'],2212.08986v2.pdf,"  Authorship style transfer involves altering text to match the style of a
+target author whilst preserving the original meaning. Existing unsupervised
+approaches like STRAP have largely focused on style transfer to target authors
+with many examples of their writing style in books, speeches, or other
+published works. This high-resource training data requirement (often greater
+than 100,000 words) makes these approaches primarily useful for style transfer
+to published authors, politicians, or other well-known figures and authorship
+styles, while style transfer to non-famous authors has not been well-studied.
+We introduce the \textit{low-resource authorship style transfer} task, a more
+challenging class of authorship style transfer where only a limited amount of
+text in the target author's style may exist. In our experiments, we
+specifically choose source and target authors from Reddit and style transfer
+their Reddit posts, limiting ourselves to just 16 posts (on average ~500 words)
+of the target author's style. Style transfer accuracy is typically measured by
+how often a classifier or human judge will classify an output as written by the
+target author. Recent authorship representations models excel at authorship
+identification even with just a few writing samples, making automatic
+evaluation of this task possible for the first time through evaluation metrics
+we propose. Our results establish an in-context learning technique we develop
+as the strongest baseline, though we find current approaches do not yet achieve
+mastery of this challenging task. We release our data and implementations to
+encourage further investigation.
+"
+Training Trajectories of Language Models Across Scales,Mengzhou Xia,http://arxiv.org/pdf/2212.09803v3.pdf,2022-12-19,"['cs.cl', 'cs.ai', 'cs.lg']",2212.09803v3.pdf,"  Scaling up language models has led to unprecedented performance gains, but
+little is understood about how the training dynamics change as models get
+larger. How do language models of different sizes learn during pre-training?
+Why do larger language models demonstrate more desirable behaviors? In this
+paper, we analyze the intermediate training checkpoints of differently sized
+OPT models (Zhang et al.,2022)--from 125M to 175B parameters--on next-token
+prediction, sequence-level generation, and downstream tasks. We find that 1) at
+a given perplexity and independent of model sizes, a similar subset of training
+tokens see the most significant reduction in loss, with the rest stagnating or
+showing double-descent behavior; 2) early in training, all models learn to
+reduce the perplexity of grammatical sequences that contain hallucinations,
+with small models halting at this suboptimal distribution and larger ones
+eventually learning to assign these sequences lower probabilities; 3)
+perplexity is a strong predictor of in-context learning performance on 74
+multiple-choice tasks from BIG-Bench, and this holds independent of the model
+size. Together, these results show that perplexity is more predictive of model
+behaviors than model size or training computation.
+"
+Dialog2API: Task-Oriented Dialogue with API Description and Example  Programs,Raphael Shu,http://arxiv.org/pdf/2212.09946v1.pdf,2022-12-20,['cs.cl'],2212.09946v1.pdf,"  Functionality and dialogue experience are two important factors of
+task-oriented dialogue systems. Conventional approaches with closed schema
+(e.g., conversational semantic parsing) often fail as both the functionality
+and dialogue experience are strongly constrained by the underlying schema. We
+introduce a new paradigm for task-oriented dialogue - Dialog2API - to greatly
+expand the functionality and provide seamless dialogue experience. The
+conversational model interacts with the environment by generating and executing
+programs triggering a set of pre-defined APIs. The model also manages the
+dialogue policy and interact with the user through generating appropriate
+natural language responses. By allowing generating free-form programs,
+Dialog2API supports composite goals by combining different APIs, whereas
+unrestricted program revision provides natural and robust dialogue experience.
+To facilitate Dialog2API, the core model is provided with API documents, an
+execution environment and optionally some example dialogues annotated with
+programs. We propose an approach tailored for the Dialog2API, where the
+dialogue states are represented by a stack of programs, with most recently
+mentioned program on the top of the stack. Dialog2API can work with many
+application scenarios such as software automation and customer service. In this
+paper, we construct a dataset for AWS S3 APIs and present evaluation results of
+in-context learning baselines.
+"
+HINT: Hypernetwork Instruction Tuning for Efficient Zero- & Few-Shot  Generalisation,Hamish Ivison,http://arxiv.org/pdf/2212.10315v2.pdf,2022-12-20,['cs.cl'],2212.10315v2.pdf,"  Recent NLP models have shown the remarkable ability to effectively generalise
+`zero-shot' to new tasks using only natural language instructions as guidance.
+However, many of these approaches suffer from high computational costs due to
+their reliance on concatenating lengthy instructions with every input example,
+resulting in costly reprocessing of the instruction. To avoid this, we
+introduce Hypernetworks for INstruction Tuning (HINT), which convert task
+instructions and examples into parameter-efficient modules inserted into an
+underlying model using a pretrained text encoder, eliminating the need to
+include instructions in the model input. The hypernetwork in HINT also produces
+an encoded instruction, which we concatenate with encoded inputs during
+decoding to further improve performance. HINT models outperform strong
+state-of-the-art baselines by over 10% when controlling for compute (measured
+in FLOPs). By converting instructions into modules, HINT models can effectively
+disregard the length of instructions and few-shot example inputs in terms of
+compute usage. As a result, HINT can enhance its performance by up to 25% by
+incorporating additional few-shot data, while utilizing only up to 5% more
+compute. This combines the strengths of parameter-efficient fine-tuning and
+in-context learning.
+"
+Prompt-Augmented Linear Probing: Scaling beyond the Limit of Few-shot  In-Context Learners,Hyunsoo Cho,http://arxiv.org/pdf/2212.10873v3.pdf,2022-12-21,"['cs.cl', 'cs.lg']",2212.10873v3.pdf,"  Through in-context learning (ICL), large-scale language models are effective
+few-shot learners without additional model fine-tuning. However, the ICL
+performance does not scale well with the number of available training samples
+as it is limited by the inherent input length constraint of the underlying
+language model. Meanwhile, many studies have revealed that language models are
+also powerful feature extractors, allowing them to be utilized in a black-box
+manner and enabling the linear probing paradigm, where lightweight
+discriminators are trained on top of the pre-extracted input representations.
+This paper proposes prompt-augmented linear probing (PALP), a hybrid of linear
+probing and ICL, which leverages the best of both worlds. PALP inherits the
+scalability of linear probing and the capability of enforcing language models
+to derive more meaningful representations via tailoring input into a more
+conceivable form. Throughout in-depth investigations on various datasets, we
+verified that PALP significantly enhances the input representations closing the
+gap between ICL in the data-hungry scenario and fine-tuning in the
+data-abundant scenario with little training overhead, potentially making PALP a
+strong alternative in a black-box scenario.
+"
+Parallel Context Windows for Large Language Models,Nir Ratner,http://arxiv.org/pdf/2212.10947v3.pdf,2022-12-21,['cs.cl'],2212.10947v3.pdf,"  When applied to processing long text, Large Language Models (LLMs) are
+limited by their context window. Existing efforts to address this limitation
+involve training specialized architectures, and cannot be easily applied to
+off-the-shelf LLMs. We present Parallel Context Windows (PCW), a method that
+alleviates the context window restriction for any off-the-shelf LLM without
+further training. The key to the approach is to carve a long context into
+chunks (``windows''), restrict the attention mechanism to apply only within
+each window, and re-use the positional embeddings across the windows. Our main
+results test the PCW approach on in-context learning with models that range in
+size between 750 million and 178 billion parameters, and show substantial
+improvements for tasks with diverse input and output spaces. We show additional
+benefits in other settings where long context windows may be beneficial:
+multi-hop questions and retrieval-augmented question answering with multiple
+retrieved documents. Our results highlight Parallel Context Windows as a
+promising method for applying off-the-shelf LLMs in a range of settings that
+require long text sequences. We make our code publicly available at
+https://github.com/ai21labs/parallel-context-windows.
+"
+Collaborating with language models for embodied reasoning,Ishita Dasgupta,http://arxiv.org/pdf/2302.00763v1.pdf,2023-02-01,"['cs.lg', 'cs.ai', 'cs.cl']",2302.00763v1.pdf,"  Reasoning in a complex and ambiguous environment is a key goal for
+Reinforcement Learning (RL) agents. While some sophisticated RL agents can
+successfully solve difficult tasks, they require a large amount of training
+data and often struggle to generalize to new unseen environments and new tasks.
+On the other hand, Large Scale Language Models (LSLMs) have exhibited strong
+reasoning ability and the ability to to adapt to new tasks through in-context
+learning. However, LSLMs do not inherently have the ability to interrogate or
+intervene on the environment. In this work, we investigate how to combine these
+complementary abilities in a single system consisting of three parts: a
+Planner, an Actor, and a Reporter. The Planner is a pre-trained language model
+that can issue commands to a simple embodied agent (the Actor), while the
+Reporter communicates with the Planner to inform its next command. We present a
+set of tasks that require reasoning, test this system's ability to generalize
+zero-shot and investigate failure cases, and demonstrate how components of this
+system can be trained with reinforcement-learning to improve performance.
+"
+Controlling Personality Style in Dialogue with Zero-Shot Prompt-Based  Learning,Angela Ramirez,http://arxiv.org/pdf/2302.03848v1.pdf,2023-02-08,['cs.cl'],2302.03848v1.pdf,"  Prompt-based or in-context learning has achieved high zero-shot performance
+on many natural language generation (NLG) tasks. Here we explore the
+performance of prompt-based learning for simultaneously controlling the
+personality and the semantic accuracy of an NLG for task-oriented dialogue. We
+experiment with prompt-based learning on the PERSONAGE restaurant
+recommendation corpus to generate semantically and stylistically-controlled
+text for 5 different Big-5 personality types: agreeable, disagreeable,
+conscientious, unconscientious, and extravert. We test two different classes of
+discrete prompts to generate utterances for a particular personality style: (1)
+prompts that demonstrate generating directly from a meaning representation that
+includes a personality specification; and (2) prompts that rely on first
+converting the meaning representation to a textual pseudo-reference, and then
+using the pseudo-reference in a textual style transfer (TST) prompt. In each
+case, we show that we can vastly improve performance by over-generating outputs
+and ranking them, testing several ranking functions based on automatic metrics
+for semantic accuracy, personality-match, and fluency. We also test whether NLG
+personality demonstrations from the restaurant domain can be used with meaning
+representations for the video game domain to generate personality stylized
+utterances about video games. Our findings show that the TST prompts produces
+the highest semantic accuracy (78.46% for restaurants and 87.6% for video
+games) and personality accuracy (100% for restaurants and 97% for video games).
+Our results on transferring personality style to video game utterances are
+surprisingly good. To our knowledge, there is no previous work testing the
+application of prompt-based learning to simultaneously controlling both style
+and semantic accuracy in NLG.
+"
+Distinguishability Calibration to In-Context Learning,Hongjing Li,http://arxiv.org/pdf/2302.06198v3.pdf,2023-02-13,['cs.cl'],2302.06198v3.pdf,"  Recent years have witnessed increasing interests in prompt-based learning in
+which models can be trained on only a few annotated instances, making them
+suitable in low-resource settings. When using prompt-based learning for text
+classification, the goal is to use a pre-trained language model (PLM) to
+predict a missing token in a pre-defined template given an input text, which
+can be mapped to a class label. However, PLMs built on the transformer
+architecture tend to generate similar output embeddings, making it difficult to
+discriminate between different class labels. The problem is further exacerbated
+when dealing with classification tasks involving many fine-grained class
+labels. In this work, we alleviate this information diffusion issue, i.e.,
+different tokens share a large proportion of similar information after going
+through stacked multiple self-attention layers in a transformer, by proposing a
+calibration method built on feature transformations through rotation and
+scaling to map a PLM-encoded embedding into a new metric space to guarantee the
+distinguishability of the resulting embeddings. Furthermore, we take the
+advantage of hyperbolic embeddings to capture the hierarchical relations among
+fine-grained class-associated token embedding by a coarse-to-fine metric
+learning strategy to enhance the distinguishability of the learned output
+embeddings. Extensive experiments on the three datasets under various settings
+demonstrate the effectiveness of our approach. Our code can be found at
+https://github.com/donttal/TARA.
+"
+Do We Still Need Clinical Language Models?,Eric Lehman,http://arxiv.org/pdf/2302.08091v1.pdf,2023-02-16,['cs.cl'],2302.08091v1.pdf,"  Although recent advances in scaling large language models (LLMs) have
+resulted in improvements on many NLP tasks, it remains unclear whether these
+models trained primarily with general web text are the right tool in highly
+specialized, safety critical domains such as clinical text. Recent results have
+suggested that LLMs encode a surprising amount of medical knowledge. This
+raises an important question regarding the utility of smaller domain-specific
+language models. With the success of general-domain LLMs, is there still a need
+for specialized clinical models? To investigate this question, we conduct an
+extensive empirical analysis of 12 language models, ranging from 220M to 175B
+parameters, measuring their performance on 3 different clinical tasks that test
+their ability to parse and reason over electronic health records. As part of
+our experiments, we train T5-Base and T5-Large models from scratch on clinical
+notes from MIMIC III and IV to directly investigate the efficiency of clinical
+tokens. We show that relatively small specialized clinical models substantially
+outperform all in-context learning approaches, even when finetuned on limited
+annotated data. Further, we find that pretraining on clinical tokens allows for
+smaller, more parameter-efficient models that either match or outperform much
+larger language models trained on general text. We release the code and the
+models used under the PhysioNet Credentialed Health Data license and data use
+agreement.
+"
+eP-ALM: Efficient Perceptual Augmentation of Language Models,Mustafa Shukor,http://arxiv.org/pdf/2303.11403v4.pdf,2023-03-20,"['cs.cv', 'cs.cl', 'cs.lg']",2303.11403v4.pdf,"  Large Language Models (LLMs) have so far impressed the world, with
+unprecedented capabilities that emerge in models at large scales. On the vision
+side, transformer models (i.e., ViT) are following the same trend, achieving
+the best performance on challenging benchmarks. With the abundance of such
+unimodal models, a natural question arises; do we need also to follow this
+trend to tackle multimodal tasks? In this work, we propose to rather direct
+effort to efficient adaptations of existing models, and propose to augment
+Language Models with perception. Existing approaches for adapting pretrained
+models for vision-language tasks still rely on several key components that
+hinder their efficiency. In particular, they still train a large number of
+parameters, rely on large multimodal pretraining, use encoders (e.g., CLIP)
+trained on huge image-text datasets, and add significant inference overhead. In
+addition, most of these approaches have focused on Zero-Shot and In Context
+Learning, with little to no effort on direct finetuning. We investigate the
+minimal computational effort needed to adapt unimodal models for multimodal
+tasks and propose a new challenging setup, alongside different approaches, that
+efficiently adapts unimodal pretrained models. We show that by freezing more
+than 99% of total parameters, training only one linear projection layer, and
+prepending only one trainable token, our approach (dubbed eP-ALM) significantly
+outperforms other baselines on VQA and Captioning across Image, Video, and
+Audio modalities, following the proposed setup. The code is available here:
+https://github.com/mshukor/eP-ALM.
+"
+Towards Making the Most of ChatGPT for Machine Translation,Keqin Peng,http://arxiv.org/pdf/2303.13780v4.pdf,2023-03-24,['cs.cl'],2303.13780v4.pdf,"  ChatGPT shows remarkable capabilities for machine translation (MT). Several
+prior studies have shown that it achieves comparable results to commercial
+systems for high-resource languages, but lags behind in complex tasks, e.g.,
+low-resource and distant-language-pairs translation. However, they usually
+adopt simple prompts which can not fully elicit the capability of ChatGPT. In
+this paper, we aim to further mine ChatGPT's translation ability by revisiting
+several aspects: temperature, task information, and domain information, and
+correspondingly propose an optimal temperature setting and two (simple but
+effective) prompts: Task-Specific Prompts (TSP) and Domain-Specific Prompts
+(DSP). We show that: 1) The performance of ChatGPT depends largely on
+temperature, and a lower temperature usually can achieve better performance; 2)
+Emphasizing the task information can further improve ChatGPT's performance,
+particularly in complex MT tasks; 3) Introducing domain information can elicit
+ChatGPT's generalization ability and improve its performance in the specific
+domain; 4) ChatGPT tends to generate hallucinations for non-English-centric MT
+tasks, which can be partially addressed by our proposed prompts but still need
+to be highlighted for the MT/NLP community. We also explore the effects of
+advanced in-context learning strategies and find a (negative but interesting)
+observation: the powerful chain-of-thought prompt leads to word-by-word
+translation behavior, thus bringing significant translation degradation.
+"
+$k$NN Prompting: Beyond-Context Learning with Calibration-Free Nearest  Neighbor Inference,Benfeng Xu,http://arxiv.org/pdf/2303.13824v1.pdf,2023-03-24,"['cs.cl', 'cs.ai']",2303.13824v1.pdf,"  In-Context Learning (ICL), which formulates target tasks as prompt completion
+conditioned on in-context demonstrations, has become the prevailing utilization
+of LLMs. In this paper, we first disclose an actual predicament for this
+typical usage that it can not scale up with training data due to context length
+restriction. Besides, existing works have shown that ICL also suffers from
+various biases and requires delicate calibration treatment. To address both
+challenges, we advocate a simple and effective solution, $k$NN Prompting, which
+first queries LLM with training data for distributed representations, then
+predicts test instances by simply referring to nearest neighbors. We conduct
+comprehensive experiments to demonstrate its two-fold superiority: 1)
+Calibration-Free: $k$NN Prompting does not directly align LLM output
+distribution with task-specific label space, instead leverages such
+distribution to align test and training instances. It significantly outperforms
+state-of-the-art calibration-based methods under comparable few-shot scenario.
+2) Beyond-Context: $k$NN Prompting can further scale up effectively with as
+many training data as are available, continually bringing substantial
+improvements. The scaling trend holds across 10 orders of magnitude ranging
+from 2 shots to 1024 shots as well as different LLMs scales ranging from 0.8B
+to 30B. It successfully bridges data scaling into model scaling, and brings new
+potentials for the gradient-free paradigm of LLM deployment. Code is publicly
+available.
+"
+Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender  System,Yunfan Gao,http://arxiv.org/pdf/2303.14524v2.pdf,2023-03-25,"['cs.ir', 'cs.cl', 'cs.lg']",2303.14524v2.pdf,"  Large language models (LLMs) have demonstrated their significant potential to
+be applied for addressing various application tasks. However, traditional
+recommender systems continue to face great challenges such as poor
+interactivity and explainability, which actually also hinder their broad
+deployment in real-world systems. To address these limitations, this paper
+proposes a novel paradigm called Chat-Rec (ChatGPT Augmented Recommender
+System) that innovatively augments LLMs for building conversational recommender
+systems by converting user profiles and historical interactions into prompts.
+Chat-Rec is demonstrated to be effective in learning user preferences and
+establishing connections between users and products through in-context
+learning, which also makes the recommendation process more interactive and
+explainable. What's more, within the Chat-Rec framework, user's preferences can
+transfer to different products for cross-domain recommendations, and
+prompt-based injection of information into LLMs can also handle the cold-start
+scenarios with new items. In our experiments, Chat-Rec effectively improve the
+results of top-k recommendations and performs better in zero-shot rating
+prediction task. Chat-Rec offers a novel approach to improving recommender
+systems and presents new practical scenarios for the implementation of AIGC (AI
+generated content) in recommender system studies.
+"
+What Makes Good In-context Demonstrations for Code Intelligence Tasks  with LLMs?,Shuzheng Gao,http://arxiv.org/pdf/2304.07575v2.pdf,2023-04-15,['cs.se'],2304.07575v2.pdf,"  Pre-trained models of source code have gained widespread popularity in many
+code intelligence tasks. Recently, with the scaling of the model and corpus
+size, large language models have shown the ability of in-context learning
+(ICL). ICL employs task instructions and a few examples as demonstrations, and
+then inputs the demonstrations to the language models for making predictions.
+This new learning paradigm is training-free and has shown impressive
+performance in various natural language processing and code intelligence tasks.
+However, the performance of ICL heavily relies on the quality of
+demonstrations, e.g., the selected examples. It is important to systematically
+investigate how to construct a good demonstration for code-related tasks. In
+this paper, we empirically explore the impact of three key factors on the
+performance of ICL in code intelligence tasks: the selection, order, and number
+of demonstration examples. We conduct extensive experiments on three code
+intelligence tasks including code summarization, bug fixing, and program
+synthesis. Our experimental results demonstrate that all the above three
+factors dramatically impact the performance of ICL in code intelligence tasks.
+Additionally, we summarize our findings and provide takeaway suggestions on how
+to construct effective demonstrations, taking into account these three
+perspectives. We also show that a carefully-designed demonstration based on our
+findings can lead to substantial improvements over widely-used demonstration
+construction methods, e.g., improving BLEU-4, EM, and EM by at least 9.90%,
+175.96%, and 50.81% on code summarization, bug fixing, and program synthesis,
+respectively
+"
+Sparks of GPTs in Edge Intelligence for Metaverse: Caching and Inference  for Mobile AIGC Services,Minrui Xu,http://arxiv.org/pdf/2304.08782v2.pdf,2023-04-18,['cs.ni'],2304.08782v2.pdf,"  Aiming at achieving artificial general intelligence (AGI) for Metaverse,
+pretrained foundation models (PFMs), e.g., generative pretrained transformers
+(GPTs), can effectively provide various AI services, such as autonomous
+driving, digital twins, and AI-generated content (AIGC) for extended reality.
+With the advantages of low latency and privacy-preserving, serving PFMs of
+mobile AI services in edge intelligence is a viable solution for caching and
+executing PFMs on edge servers with limited computing resources and GPU memory.
+However, PFMs typically consist of billions of parameters that are computation
+and memory-intensive for edge servers during loading and execution. In this
+article, we investigate edge PFM serving problems for mobile AIGC services of
+Metaverse. First, we introduce the fundamentals of PFMs and discuss their
+characteristic fine-tuning and inference methods in edge intelligence. Then, we
+propose a novel framework of joint model caching and inference for managing
+models and allocating resources to satisfy users' requests efficiently.
+Furthermore, considering the in-context learning ability of PFMs, we propose a
+new metric to evaluate the freshness and relevance between examples in
+demonstrations and executing tasks, namely the Age of Context (AoC). Finally,
+we propose a least context algorithm for managing cached models at edge servers
+by balancing the tradeoff among latency, energy consumption, and accuracy.
+"
+Controlled Text Generation with Natural Language Instructions,Wangchunshu Zhou,http://arxiv.org/pdf/2304.14293v2.pdf,2023-04-27,"['cs.cl', 'cs.ai', 'cs.lg']",2304.14293v2.pdf,"  Large language models generate fluent texts and can follow natural language
+instructions to solve a wide range of tasks without task-specific training.
+Nevertheless, it is notoriously difficult to control their generation to
+satisfy the various constraints required by different applications. In this
+work, we present InstructCTG, a controlled text generation framework that
+incorporates different constraints by conditioning on natural language
+descriptions and demonstrations of the constraints. In particular, we first
+extract the underlying constraints of natural texts through a combination of
+off-the-shelf NLP tools and simple heuristics. We then verbalize the
+constraints into natural language instructions to form weakly supervised
+training data. By prepending natural language descriptions of the constraints
+and a few demonstrations, we fine-tune a pre-trained language model to
+incorporate various types of constraints. Compared to existing search-based or
+score-based methods, InstructCTG is more flexible to different constraint types
+and has a much smaller impact on the generation quality and speed because it
+does not modify the decoding procedure. Additionally, InstructCTG allows the
+model to adapt to new constraints without re-training through the use of
+few-shot task generalization and in-context learning abilities of
+instruction-tuned language models.
+"
+TALLRec: An Effective and Efficient Tuning Framework to Align Large  Language Model with Recommendation,Keqin Bao,http://arxiv.org/pdf/2305.00447v3.pdf,2023-04-30,['cs.ir'],2305.00447v3.pdf,"  Large Language Models (LLMs) have demonstrated remarkable performance across
+diverse domains, thereby prompting researchers to explore their potential for
+use in recommendation systems. Initial attempts have leveraged the exceptional
+capabilities of LLMs, such as rich knowledge and strong generalization through
+In-context Learning, which involves phrasing the recommendation task as
+prompts. Nevertheless, the performance of LLMs in recommendation tasks remains
+suboptimal due to a substantial disparity between the training tasks for LLMs
+and recommendation tasks, as well as inadequate recommendation data during
+pre-training. To bridge the gap, we consider building a Large Recommendation
+Language Model by tunning LLMs with recommendation data. To this end, we
+propose an efficient and effective Tuning framework for Aligning LLMs with
+Recommendation, namely TALLRec. We have demonstrated that the proposed TALLRec
+framework can significantly enhance the recommendation capabilities of LLMs in
+the movie and book domains, even with a limited dataset of fewer than 100
+samples. Additionally, the proposed framework is highly efficient and can be
+executed on a single RTX 3090 with LLaMA-7B. Furthermore, the fine-tuned LLM
+exhibits robust cross-domain generalization. Our code and data are available at
+https://github.com/SAI990323/TALLRec.
+"
+Cognitive Reframing of Negative Thoughts through Human-Language Model  Interaction,Ashish Sharma,http://arxiv.org/pdf/2305.02466v1.pdf,2023-05-04,"['cs.cl', 'cs.hc', 'cs.si']",2305.02466v1.pdf,"  A proven therapeutic technique to overcome negative thoughts is to replace
+them with a more hopeful ""reframed thought."" Although therapy can help people
+practice and learn this Cognitive Reframing of Negative Thoughts, clinician
+shortages and mental health stigma commonly limit people's access to therapy.
+In this paper, we conduct a human-centered study of how language models may
+assist people in reframing negative thoughts. Based on psychology literature,
+we define a framework of seven linguistic attributes that can be used to
+reframe a thought. We develop automated metrics to measure these attributes and
+validate them with expert judgements from mental health practitioners. We
+collect a dataset of 600 situations, thoughts and reframes from practitioners
+and use it to train a retrieval-enhanced in-context learning model that
+effectively generates reframed thoughts and controls their linguistic
+attributes. To investigate what constitutes a ""high-quality"" reframe, we
+conduct an IRB-approved randomized field study on a large mental health website
+with over 2,000 participants. Amongst other findings, we show that people
+prefer highly empathic or specific reframes, as opposed to reframes that are
+overly positive. Our findings provide key implications for the use of LMs to
+assist people in overcoming negative thoughts.
+"
+Using ChatGPT for Entity Matching,Ralph Peeters,http://arxiv.org/pdf/2305.03423v2.pdf,2023-05-05,['cs.cl'],2305.03423v2.pdf,"  Entity Matching is the task of deciding if two entity descriptions refer to
+the same real-world entity. State-of-the-art entity matching methods often rely
+on fine-tuning Transformer models such as BERT or RoBERTa. Two major drawbacks
+of using these models for entity matching are that (i) the models require
+significant amounts of fine-tuning data for reaching a good performance and
+(ii) the fine-tuned models are not robust concerning out-of-distribution
+entities. In this paper, we investigate using ChatGPT for entity matching as a
+more robust, training data-efficient alternative to traditional Transformer
+models. We perform experiments along three dimensions: (i) general prompt
+design, (ii) in-context learning, and (iii) provision of higher-level matching
+knowledge. We show that ChatGPT is competitive with a fine-tuned RoBERTa model,
+reaching a zero-shot performance of 82.35% F1 on a challenging matching task on
+which RoBERTa requires 2000 training examples for reaching a similar
+performance. Adding in-context demonstrations to the prompts further improves
+the F1 by up to 7.85% when using similarity-based example selection. Always
+using the same set of 10 handpicked demonstrations leads to an improvement of
+4.92% over the zero-shot performance. Finally, we show that ChatGPT can also be
+guided by adding higher-level matching knowledge in the form of rules to the
+prompts. Providing matching rules leads to similar performance gains as
+providing in-context demonstrations.
+"
+Multilingual LLMs are Better Cross-lingual In-context Learners with  Alignment,Eshaan Tanwar,http://arxiv.org/pdf/2305.05940v3.pdf,2023-05-10,['cs.cl'],2305.05940v3.pdf,"  In-context learning (ICL) unfolds as large language models become capable of
+inferring test labels conditioned on a few labeled samples without any gradient
+update. ICL-enabled large language models provide a promising step forward
+toward bypassing recurrent annotation costs in a low-resource setting. Yet,
+only a handful of past studies have explored ICL in a cross-lingual setting, in
+which the need for transferring label-knowledge from a high-resource language
+to a low-resource one is immensely crucial. To bridge the gap, we provide the
+first in-depth analysis of ICL for cross-lingual text classification. We find
+that the prevalent mode of selecting random input-label pairs to construct the
+prompt-context is severely limited in the case of cross-lingual ICL, primarily
+due to the lack of alignment in the input as well as the output spaces. To
+mitigate this, we propose a novel prompt construction strategy -- Cross-lingual
+In-context Source-Target Alignment (X-InSTA). With an injected coherence in the
+semantics of the input examples and a task-based alignment across the source
+and target languages, X-InSTA is able to outperform random prompt selection by
+a large margin across three different tasks using 44 different cross-lingual
+pairs.
+"
+Can Language Models Solve Graph Problems in Natural Language?,Heng Wang,http://arxiv.org/pdf/2305.10037v2.pdf,2023-05-17,"['cs.cl', 'cs.ai']",2305.10037v2.pdf,"  Large language models (LLMs) are increasingly adopted for a variety of tasks
+with implicit graphical structures, such as planning in robotics, multi-hop
+question answering or knowledge probing, structured commonsense reasoning, and
+more. While LLMs have advanced the state-of-the-art on these tasks with
+structure implications, whether LLMs could explicitly process textual
+descriptions of graphs and structures, map them to grounded conceptual spaces,
+and perform structured operations remains underexplored. To this end, we
+propose NLGraph (Natural Language Graph), a comprehensive benchmark of
+graph-based problem solving designed in natural language. NLGraph contains
+29,370 problems, covering eight graph reasoning tasks with varying complexity
+from simple tasks such as connectivity and shortest path up to complex problems
+such as maximum flow and simulating graph neural networks. We evaluate LLMs
+(GPT-3/4) with various prompting approaches on the NLGraph benchmark and find
+that 1) language models do demonstrate preliminary graph reasoning abilities,
+2) the benefit of advanced prompting and in-context learning diminishes on more
+complex graph problems, while 3) LLMs are also (un)surprisingly brittle in the
+face of spurious correlations in graph and problem settings. We then propose
+Build-a-Graph Prompting and Algorithmic Prompting, two instruction-based
+approaches to enhance LLMs in solving natural language graph problems.
+Build-a-Graph and Algorithmic prompting improve the performance of LLMs on
+NLGraph by 3.07% to 16.85% across multiple tasks and settings, while how to
+solve the most complicated graph reasoning tasks in our setup with language
+models remains an open research question. The NLGraph benchmark and evaluation
+code are available at https://github.com/Arthur-Heng/NLGraph.
+"
+Joint Foundation Model Caching and Inference of Generative AI Services  for Edge Intelligence,Minrui Xu,http://arxiv.org/pdf/2305.12130v1.pdf,2023-05-20,['cs.ni'],2305.12130v1.pdf,"  With the rapid development of artificial general intelligence (AGI), various
+multimedia services based on pretrained foundation models (PFMs) need to be
+effectively deployed. With edge servers that have cloud-level computing power,
+edge intelligence can extend the capabilities of AGI to mobile edge networks.
+However, compared with cloud data centers, resource-limited edge servers can
+only cache and execute a small number of PFMs, which typically consist of
+billions of parameters and require intensive computing power and GPU memory
+during inference. To address this challenge, in this paper, we propose a joint
+foundation model caching and inference framework that aims to balance the
+tradeoff among inference latency, accuracy, and resource consumption by
+managing cached PFMs and user requests efficiently during the provisioning of
+generative AI services. Specifically, considering the in-context learning
+ability of PFMs, a new metric named the Age of Context (AoC), is proposed to
+model the freshness and relevance between examples in past demonstrations and
+current service requests. Based on the AoC, we propose a least context caching
+algorithm to manage cached PFMs at edge servers with historical prompts and
+inference results. The numerical results demonstrate that the proposed
+algorithm can reduce system costs compared with existing baselines by
+effectively utilizing contextual information.
+"
+Enhancing Few-shot Text-to-SQL Capabilities of Large Language Models: A  Study on Prompt Design Strategies,Linyong Nan,http://arxiv.org/pdf/2305.12586v1.pdf,2023-05-21,['cs.cl'],2305.12586v1.pdf,"  In-context learning (ICL) has emerged as a new approach to various natural
+language processing tasks, utilizing large language models (LLMs) to make
+predictions based on context that has been supplemented with a few examples or
+task-specific instructions. In this paper, we aim to extend this method to
+question answering tasks that utilize structured knowledge sources, and improve
+Text-to-SQL systems by exploring various prompt design strategies for employing
+LLMs. We conduct a systematic investigation into different demonstration
+selection methods and optimal instruction formats for prompting LLMs in the
+Text-to-SQL task. Our approach involves leveraging the syntactic structure of
+an example's SQL query to retrieve demonstrations, and we demonstrate that
+pursuing both diversity and similarity in demonstration selection leads to
+enhanced performance. Furthermore, we show that LLMs benefit from
+database-related knowledge augmentations. Our most effective strategy
+outperforms the state-of-the-art system by 2.5 points (Execution Accuracy) and
+the best fine-tuned system by 5.1 points on the Spider dataset. These results
+highlight the effectiveness of our approach in adapting LLMs to the Text-to-SQL
+task, and we present an analysis of the factors contributing to the success of
+our strategy.
+"
+Exploring Chain-of-Thought Style Prompting for Text-to-SQL,Chang-You Tai,http://arxiv.org/pdf/2305.14215v2.pdf,2023-05-23,['cs.cl'],2305.14215v2.pdf,"  In-context learning with large language models (LLMs) has recently caught
+increasing attention due to its superior few-shot performance on various tasks.
+However, its performance on text-to-SQL parsing still has much room for
+improvement. In this paper, we hypothesize that a crucial aspect of LLMs to
+improve for text-to-SQL parsing is their multi-step reasoning ability. Thus, we
+systematically study how to enhance LLMs' reasoning ability through chain of
+thought (CoT) style prompting, including the original chain-of-thought
+prompting (Wei et al., 2022b) and least-to-most prompting (Zhou et al., 2023).
+Our experiments demonstrate that iterative prompting as in Zhou et al. (2023)
+may be unnecessary for text-to-SQL parsing, and using detailed reasoning steps
+tends to have more error propagation issues. Based on these findings, we
+propose a new CoT-style prompting method for text-to-SQL parsing. It brings 5.2
+and 6.5 point absolute gains on the Spider development set and the Spider
+Realistic set, respectively, compared to the standard prompting method without
+reasoning steps; 2.4 and 1.5 point absolute gains, compared to the
+least-to-most prompting method.
+"
+Sociocultural Norm Similarities and Differences via Situational  Alignment and Explainable Textual Entailment,Sky CH-Wang,http://arxiv.org/pdf/2305.14492v2.pdf,2023-05-23,['cs.cl'],2305.14492v2.pdf,"  Designing systems that can reason across cultures requires that they are
+grounded in the norms of the contexts in which they operate. However, current
+research on developing computational models of social norms has primarily
+focused on American society. Here, we propose a novel approach to discover and
+compare descriptive social norms across Chinese and American cultures. We
+demonstrate our approach by leveraging discussions on a Chinese Q&A platform
+(Zhihu) and the existing SocialChemistry dataset as proxies for contrasting
+cultural axes, align social situations cross-culturally, and extract social
+norms from texts using in-context learning. Embedding Chain-of-Thought
+prompting in a human-AI collaborative framework, we build a high-quality
+dataset of 3,069 social norms aligned with social situations across Chinese and
+American cultures alongside corresponding free-text explanations. To test the
+ability of models to reason about social norms across cultures, we introduce
+the task of explainable social norm entailment, showing that existing models
+under 3B parameters have significant room for improvement in both automatic and
+human evaluation. Further analysis of cross-cultural norm differences based on
+our dataset shows empirical alignment with the social orientations framework,
+revealing several situational and descriptive nuances in norms across these
+cultures.
+"
+Increasing Probability Mass on Answer Choices Does Not Always Improve  Accuracy,Sarah Wiegreffe,http://arxiv.org/pdf/2305.14596v2.pdf,2023-05-24,"['cs.cl', 'cs.lg']",2305.14596v2.pdf,"  When pretrained language models (LMs) are applied to discriminative tasks
+such as multiple-choice questions, they place probability mass on vocabulary
+tokens that aren't among the given answer choices. Spreading probability mass
+across multiple surface forms with identical meaning (such as ""bath"" and
+""bathtub"") is thought to cause an underestimation of a model's true
+performance, referred to as the ""surface form competition"" (SFC) hypothesis.
+This has motivated the introduction of various probability normalization
+methods. However, many core questions remain unanswered. How do we measure SFC?
+Are there direct ways of reducing it, and does doing so improve task
+performance?
+  We propose a mathematical formalism for SFC which allows us to quantify and
+bound its impact for the first time. We identify a simple method for reducing
+it -- namely, increasing probability mass on the given answer choices by a)
+including them in the prompt and b) using in-context learning with even just
+one example. We show this method eliminates the impact of SFC in the majority
+of instances. Our experiments on three diverse datasets and six LMs reveal
+several additional surprising findings. For example, both normalization and
+prompting methods for reducing SFC can be ineffective or even detrimental to
+task performance for some LMs. We conclude with practical insights for
+effectively prompting LMs for multiple-choice tasks.
+"
+Universal Self-Adaptive Prompting,Xingchen Wan,http://arxiv.org/pdf/2305.14926v2.pdf,2023-05-24,"['cs.cl', 'cs.ai', 'cs.lg']",2305.14926v2.pdf,"  A hallmark of modern large language models (LLMs) is their impressive general
+zero-shot and few-shot abilities, often elicited through in-context learning
+(ICL) via prompting. However, while highly coveted and being the most general,
+zero-shot performances in LLMs are still typically weaker due to the lack of
+guidance and the difficulty of applying existing automatic prompt design
+methods in general tasks when ground-truth labels are unavailable. In this
+study, we address this by presenting Universal Self-Adaptive Prompting (USP),
+an automatic prompt design approach specifically tailored for zero-shot
+learning (while compatible with few-shot). Requiring only a small amount of
+unlabeled data and an inference-only LLM, USP is highly versatile: to achieve
+universal prompting, USP categorizes a possible NLP task into one of the three
+possible task types and then uses a corresponding selector to select the most
+suitable queries and zero-shot model-generated responses as
+pseudo-demonstrations, thereby generalizing ICL to the zero-shot setup in a
+fully automated way. We evaluate USP with PaLM and PaLM 2 models and
+demonstrate performances that are considerably stronger than standard zero-shot
+baselines and often comparable to or even superior to few-shot baselines across
+more than 40 natural language understanding, natural language generation, and
+reasoning tasks.
+"
+Are Chatbots Ready for Privacy-Sensitive Applications? An Investigation  into Input Regurgitation and Prompt-Induced Sanitization,Aman Priyanshu,http://arxiv.org/pdf/2305.15008v1.pdf,2023-05-24,"['cs.cl', 'cs.ai', 'cs.cy']",2305.15008v1.pdf,"  LLM-powered chatbots are becoming widely adopted in applications such as
+healthcare, personal assistants, industry hiring decisions, etc. In many of
+these cases, chatbots are fed sensitive, personal information in their prompts,
+as samples for in-context learning, retrieved records from a database, or as
+part of the conversation. The information provided in the prompt could directly
+appear in the output, which might have privacy ramifications if there is
+sensitive information there. As such, in this paper, we aim to understand the
+input copying and regurgitation capabilities of these models during inference
+and how they can be directly instructed to limit this copying by complying with
+regulations such as HIPAA and GDPR, based on their internal knowledge of them.
+More specifically, we find that when ChatGPT is prompted to summarize cover
+letters of a 100 candidates, it would retain personally identifiable
+information (PII) verbatim in 57.4% of cases, and we find this retention to be
+non-uniform between different subgroups of people, based on attributes such as
+gender identity. We then probe ChatGPT's perception of privacy-related policies
+and privatization mechanisms by directly instructing it to provide compliant
+outputs and observe a significant omission of PII from output.
+"
+Fine-Tuning Language Models with Just Forward Passes,Sadhika Malladi,http://arxiv.org/pdf/2305.17333v2.pdf,2023-05-27,"['cs.lg', 'cs.cl']",2305.17333v2.pdf,"  Fine-tuning language models (LMs) has yielded success on diverse downstream
+tasks, but as LMs grow in size, backpropagation requires a prohibitively large
+amount of memory. Zeroth-order (ZO) methods can in principle estimate gradients
+using only two forward passes but are theorized to be catastrophically slow for
+optimizing large models. In this work, we propose a memory-efficient
+zerothorder optimizer (MeZO), adapting the classical ZO-SGD method to operate
+in-place, thereby fine-tuning LMs with the same memory footprint as inference.
+For example, with a single A100 80GB GPU, MeZO can train a 30-billion parameter
+model, whereas fine-tuning with backpropagation can train only a 2.7B LM with
+the same budget. We conduct comprehensive experiments across model types
+(masked and autoregressive LMs), model scales (up to 66B), and downstream tasks
+(classification, multiple-choice, and generation). Our results demonstrate that
+(1) MeZO significantly outperforms in-context learning and linear probing; (2)
+MeZO achieves comparable performance to fine-tuning with backpropagation across
+multiple tasks, with up to 12x memory reduction and up to 2x GPU-hour reduction
+in our implementation; (3) MeZO is compatible with both full-parameter and
+parameter-efficient tuning techniques such as LoRA and prefix tuning; (4) MeZO
+can effectively optimize non-differentiable objectives (e.g., maximizing
+accuracy or F1). We support our empirical findings with theoretical insights,
+highlighting how adequate pre-training and task prompts enable MeZO to
+fine-tune huge models, despite classical ZO analyses suggesting otherwise.
+"
+Do Large Language Models Know What They Don't Know?,Zhangyue Yin,http://arxiv.org/pdf/2305.18153v2.pdf,2023-05-29,['cs.cl'],2305.18153v2.pdf,"  Large language models (LLMs) have a wealth of knowledge that allows them to
+excel in various Natural Language Processing (NLP) tasks. Current research
+focuses on enhancing their performance within their existing knowledge. Despite
+their vast knowledge, LLMs are still limited by the amount of information they
+can accommodate and comprehend. Therefore, the ability to understand their own
+limitations on the unknows, referred to as self-knowledge, is of paramount
+importance. This study aims to evaluate LLMs' self-knowledge by assessing their
+ability to identify unanswerable or unknowable questions. We introduce an
+automated methodology to detect uncertainty in the responses of these models,
+providing a novel measure of their self-knowledge. We further introduce a
+unique dataset, SelfAware, consisting of unanswerable questions from five
+diverse categories and their answerable counterparts. Our extensive analysis,
+involving 20 LLMs including GPT-3, InstructGPT, and LLaMA, discovering an
+intrinsic capacity for self-knowledge within these models. Moreover, we
+demonstrate that in-context learning and instruction tuning can further enhance
+this self-knowledge. Despite this promising insight, our findings also
+highlight a considerable gap between the capabilities of these models and human
+proficiency in recognizing the limits of their knowledge.
+"
+Improving CLIP Training with Language Rewrites,Lijie Fan,http://arxiv.org/pdf/2305.20088v2.pdf,2023-05-31,"['cs.cv', 'cs.cl', 'cs.lg']",2305.20088v2.pdf,"  Contrastive Language-Image Pre-training (CLIP) stands as one of the most
+effective and scalable methods for training transferable vision models using
+paired image and text data. CLIP models are trained using contrastive loss,
+which typically relies on data augmentations to prevent overfitting and
+shortcuts. However, in the CLIP training paradigm, data augmentations are
+exclusively applied to image inputs, while language inputs remain unchanged
+throughout the entire training process, limiting the exposure of diverse texts
+to the same image. In this paper, we introduce Language augmented CLIP
+(LaCLIP), a simple yet highly effective approach to enhance CLIP training
+through language rewrites. Leveraging the in-context learning capability of
+large language models, we rewrite the text descriptions associated with each
+image. These rewritten texts exhibit diversity in sentence structure and
+vocabulary while preserving the original key concepts and meanings. During
+training, LaCLIP randomly selects either the original texts or the rewritten
+versions as text augmentations for each image. Extensive experiments on CC3M,
+CC12M, RedCaps and LAION-400M datasets show that CLIP pre-training with
+language rewrites significantly improves the transfer performance without
+computation or memory overhead during training. Specifically for ImageNet
+zero-shot accuracy, LaCLIP outperforms CLIP by 8.2% on CC12M and 2.4% on
+LAION-400M. Code is available at https://github.com/LijieFan/LaCLIP.
+"
+SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL,Ruoxi Sun,http://arxiv.org/pdf/2306.00739v3.pdf,2023-05-26,"['cs.cl', 'cs.ai', 'cs.db']",2306.00739v3.pdf,"  One impressive emergent capability of large language models (LLMs) is
+generation of code, including Structured Query Language (SQL) for databases.
+For the task of converting natural language text to SQL queries, Text-to-SQL,
+adaptation of LLMs is of paramount importance, both in in-context learning and
+fine-tuning settings, depending on the amount of adaptation data used. In this
+paper, we propose an LLM-based Text-to-SQL model SQL-PaLM, leveraging on
+PaLM-2, that pushes the state-of-the-art in both settings. Few-shot SQL-PaLM is
+based on an execution-based self-consistency prompting approach designed for
+Text-to-SQL, and achieves 77.3% in test-suite accuracy on Spider, which to our
+best knowledge is the first to outperform previous state-of-the-art with
+fine-tuning by a significant margin, 4%. Furthermore, we demonstrate that the
+fine-tuned SQL-PALM outperforms it further by another 1%. Towards applying
+SQL-PaLM to real-world scenarios we further evaluate its robustness on other
+challenging variants of Spider and demonstrate the superior generalization
+capability of SQL-PaLM. In addition, via extensive case studies, we demonstrate
+the impressive intelligent capabilities and various success enablers of
+LLM-based Text-to-SQL.
+"
+Zero-Shot 3D Shape Correspondence,Ahmed Abdelreheem,http://arxiv.org/pdf/2306.03253v2.pdf,2023-06-05,['cs.cv'],2306.03253v2.pdf,"  We propose a novel zero-shot approach to computing correspondences between 3D
+shapes. Existing approaches mainly focus on isometric and near-isometric shape
+pairs (e.g., human vs. human), but less attention has been given to strongly
+non-isometric and inter-class shape matching (e.g., human vs. cow). To this
+end, we introduce a fully automatic method that exploits the exceptional
+reasoning capabilities of recent foundation models in language and vision to
+tackle difficult shape correspondence problems. Our approach comprises multiple
+stages. First, we classify the 3D shapes in a zero-shot manner by feeding
+rendered shape views to a language-vision model (e.g., BLIP2) to generate a
+list of class proposals per shape. These proposals are unified into a single
+class per shape by employing the reasoning capabilities of ChatGPT. Second, we
+attempt to segment the two shapes in a zero-shot manner, but in contrast to the
+co-segmentation problem, we do not require a mutual set of semantic regions.
+Instead, we propose to exploit the in-context learning capabilities of ChatGPT
+to generate two different sets of semantic regions for each shape and a
+semantic mapping between them. This enables our approach to match strongly
+non-isometric shapes with significant differences in geometric structure.
+Finally, we employ the generated semantic mapping to produce coarse
+correspondences that can further be refined by the functional maps framework to
+produce dense point-to-point maps. Our approach, despite its simplicity,
+produces highly plausible results in a zero-shot manner, especially between
+strongly non-isometric shapes. Project webpage:
+https://samir55.github.io/3dshapematch/.
+"
+MIMIC-IT: Multi-Modal In-Context Instruction Tuning,Bo Li,http://arxiv.org/pdf/2306.05425v1.pdf,2023-06-08,"['cs.cv', 'cs.ai', 'cs.cl', 'cs.hc']",2306.05425v1.pdf,"  High-quality instructions and responses are essential for the zero-shot
+performance of large language models on interactive natural language tasks. For
+interactive vision-language tasks involving intricate visual scenes, a large
+quantity of diverse and creative instruction-response pairs should be
+imperative to tune vision-language models (VLMs). Nevertheless, the current
+availability of vision-language instruction-response pairs in terms of
+quantity, diversity, and creativity remains limited, posing challenges to the
+generalization of interactive VLMs. Here we present MultI-Modal In-Context
+Instruction Tuning (MIMIC-IT), a dataset comprising 2.8 million multimodal
+instruction-response pairs, with 2.2 million unique instructions derived from
+images and videos. Each pair is accompanied by multi-modal in-context
+information, forming conversational contexts aimed at empowering VLMs in
+perception, reasoning, and planning. The instruction-response collection
+process, dubbed as Syphus, is scaled using an automatic annotation pipeline
+that combines human expertise with GPT's capabilities. Using the MIMIC-IT
+dataset, we train a large VLM named Otter. Based on extensive evaluations
+conducted on vision-language benchmarks, it has been observed that Otter
+demonstrates remarkable proficiency in multi-modal perception, reasoning, and
+in-context learning. Human evaluation reveals it effectively aligns with the
+user's intentions. We release the MIMIC-IT dataset, instruction-response
+collection pipeline, benchmarks, and the Otter model.
+"
+MedFMC: A Real-world Dataset and Benchmark For Foundation Model  Adaptation in Medical Image Classification,Dequan Wang,http://arxiv.org/pdf/2306.09579v1.pdf,2023-06-16,['cs.cv'],2306.09579v1.pdf,"  Foundation models, often pre-trained with large-scale data, have achieved
+paramount success in jump-starting various vision and language applications.
+Recent advances further enable adapting foundation models in downstream tasks
+efficiently using only a few training samples, e.g., in-context learning. Yet,
+the application of such learning paradigms in medical image analysis remains
+scarce due to the shortage of publicly accessible data and benchmarks. In this
+paper, we aim at approaches adapting the foundation models for medical image
+classification and present a novel dataset and benchmark for the evaluation,
+i.e., examining the overall performance of accommodating the large-scale
+foundation models downstream on a set of diverse real-world clinical tasks. We
+collect five sets of medical imaging data from multiple institutes targeting a
+variety of real-world clinical tasks (22,349 images in total), i.e., thoracic
+diseases screening in X-rays, pathological lesion tissue screening, lesion
+detection in endoscopy images, neonatal jaundice evaluation, and diabetic
+retinopathy grading. Results of multiple baseline methods are demonstrated
+using the proposed dataset from both accuracy and cost-effective perspectives.
+"
+JiuZhang 2.0: A Unified Chinese Pre-trained Language Model for  Multi-task Mathematical Problem Solving,Wayne Xin Zhao,http://arxiv.org/pdf/2306.11027v1.pdf,2023-06-19,"['cs.cl', 'cs.ai']",2306.11027v1.pdf,"  Although pre-trained language models~(PLMs) have recently advanced the
+research progress in mathematical reasoning, they are not specially designed as
+a capable multi-task solver, suffering from high cost for multi-task deployment
+(\eg a model copy for a task) and inferior performance on complex mathematical
+problems in practical applications. To address these issues, in this paper, we
+propose \textbf{JiuZhang~2.0}, a unified Chinese PLM specially for multi-task
+mathematical problem solving. Our idea is to maintain a moderate-sized model
+and employ the \emph{cross-task knowledge sharing} to improve the model
+capacity in a multi-task setting. Specially, we construct a
+Mixture-of-Experts~(MoE) architecture for modeling mathematical text, so as to
+capture the common mathematical knowledge across tasks. For optimizing the MoE
+architecture, we design \emph{multi-task continual pre-training} and
+\emph{multi-task fine-tuning} strategies for multi-task adaptation. These
+training strategies can effectively decompose the knowledge from the task data
+and establish the cross-task sharing via expert networks. In order to further
+improve the general capacity of solving different complex tasks, we leverage
+large language models~(LLMs) as complementary models to iteratively refine the
+generated solution by our PLM, via in-context learning. Extensive experiments
+have demonstrated the effectiveness of our model.
+"
+A Chain of AI-based Solutions for Resolving FQNs and Fixing Syntax  Errors in Partial Code,Qing Huang,http://arxiv.org/pdf/2306.11981v1.pdf,2023-06-21,['cs.se'],2306.11981v1.pdf,"  API documentation, technical blogs and programming Q&A sites contain numerous
+partial code that can be reused in programming tasks, but often these code are
+uncompilable due to unresolved names and syntax errors. To facilitate partial
+code reuse, we propose the Partial Code Reuse Chain (PCR-Chain) for resolving
+fully-qualified names (FQNs) and fixing last-mile syntax errors in partial code
+based on a giant large language model (LLM) like ChatGPT. Methodologically,
+PCR-Chain is backed up by the underlying global-level prompt architecture
+(which combines three design ideas: hierarchical task breakdown, prompt
+composition, and a mix of prompt-based AI and non-AI units) and the local-level
+prompt design. Technically, we propose PCR-Chain, which employs in-context
+learning rather than symbolic, costly training methods. Experimental results
+demonstrate that in dynamically-typed languages (Python), PCR-Chain outperforms
+current state-of-the-art (SOTA) 5% accuracy like RING. For statically-type
+languages (Java), our approach achieves high accuracy of 80.5% in resolving
+both non-FQNs and last-mile syntax errors, surpassing SOTA methods (RING) that
+can only address last-mile syntax errors. The correct execution of the unit,
+module, and PCR-Chain demonstrates the effectiveness of the prompt design,
+composition, and architecture and opens up possibilities for building software
+engineering tools based on LLMs, replacing traditional program analysis
+methods.
+"
+Generative Multimodal Entity Linking,Senbao Shi,http://arxiv.org/pdf/2306.12725v2.pdf,2023-06-22,['cs.cl'],2306.12725v2.pdf,"  Multimodal Entity Linking (MEL) is the task of mapping mentions with
+multimodal contexts to the referent entities from a knowledge base (e.g.
+Wikipedia). Existing MEL methods mainly focus on designing complex multimodal
+interaction mechanisms and require fine-tuning all model parameters, which can
+be prohibitively costly and difficult to scale in the era of Large Language
+Models (LLMs). In this work, we propose GEMEL, a simple yet effective
+Generative Multimodal Entity Linking framework based on LLMs, which directly
+generates target entity names. We keep the vision and language model frozen and
+only train a feature mapper to enable cross-modality interactions. To adapt
+LLMs to the MEL task, we take advantage of the emergent in-context learning
+capability of LLMs by retrieving multimodal instances as demonstrations.
+Extensive experiments show that, with only ~0.3% of the model parameters
+fine-tuned, GEMEL achieves state-of-the-art results on two well-established MEL
+datasets (7.7% accuracy gains on WikiDiverse and 8.8% accuracy gains on
+WikiMEL). The performance gain stems from mitigating the popularity bias of LLM
+predictions and disambiguating less common entities effectively. Further
+analysis verifies the generality and scalability of GEMEL. Our approach is
+compatible with any off-the-shelf language model, paving the way towards an
+efficient and general solution for utilizing LLMs in the MEL task.
+"
+Kosmos-2: Grounding Multimodal Large Language Models to the World,Zhiliang Peng,http://arxiv.org/pdf/2306.14824v3.pdf,2023-06-26,"['cs.cl', 'cs.cv']",2306.14824v3.pdf,"  We introduce Kosmos-2, a Multimodal Large Language Model (MLLM), enabling new
+capabilities of perceiving object descriptions (e.g., bounding boxes) and
+grounding text to the visual world. Specifically, we represent refer
+expressions as links in Markdown, i.e., ``[text span](bounding boxes)'', where
+object descriptions are sequences of location tokens. Together with multimodal
+corpora, we construct large-scale data of grounded image-text pairs (called
+GrIT) to train the model. In addition to the existing capabilities of MLLMs
+(e.g., perceiving general modalities, following instructions, and performing
+in-context learning), Kosmos-2 integrates the grounding capability into
+downstream applications. We evaluate Kosmos-2 on a wide range of tasks,
+including (i) multimodal grounding, such as referring expression comprehension,
+and phrase grounding, (ii) multimodal referring, such as referring expression
+generation, (iii) perception-language tasks, and (iv) language understanding
+and generation. This work lays out the foundation for the development of
+Embodiment AI and sheds light on the big convergence of language, multimodal
+perception, action, and world modeling, which is a key step toward artificial
+general intelligence. Code and pretrained models are available at
+https://aka.ms/kosmos-2.
+"
+Supervised Pretraining Can Learn In-Context Reinforcement Learning,Jonathan N. Lee,http://arxiv.org/pdf/2306.14892v1.pdf,2023-06-26,"['cs.lg', 'cs.ai']",2306.14892v1.pdf,"  Large transformer models trained on diverse datasets have shown a remarkable
+ability to learn in-context, achieving high few-shot performance on tasks they
+were not explicitly trained to solve. In this paper, we study the in-context
+learning capabilities of transformers in decision-making problems, i.e.,
+reinforcement learning (RL) for bandits and Markov decision processes. To do
+so, we introduce and study Decision-Pretrained Transformer (DPT), a supervised
+pretraining method where the transformer predicts an optimal action given a
+query state and an in-context dataset of interactions, across a diverse set of
+tasks. This procedure, while simple, produces a model with several surprising
+capabilities. We find that the pretrained transformer can be used to solve a
+range of RL problems in-context, exhibiting both exploration online and
+conservatism offline, despite not being explicitly trained to do so. The model
+also generalizes beyond the pretraining distribution to new tasks and
+automatically adapts its decision-making strategies to unknown structure.
+Theoretically, we show DPT can be viewed as an efficient implementation of
+Bayesian posterior sampling, a provably sample-efficient RL algorithm. We
+further leverage this connection to provide guarantees on the regret of the
+in-context algorithm yielded by DPT, and prove that it can learn faster than
+algorithms used to generate the pretraining data. These results suggest a
+promising yet simple path towards instilling strong in-context decision-making
+abilities in transformers.
+"
+A GPT-4 Reticular Chemist for Guiding MOF Discovery,Zhiling Zheng,http://arxiv.org/pdf/2306.14915v2.pdf,2023-06-20,"['cs.ai', 'cond-mat.mtrl-sci', 'physics.chem-ph']",2306.14915v2.pdf,"  We present a new framework integrating the AI model GPT-4 into the iterative
+process of reticular chemistry experimentation, leveraging a cooperative
+workflow of interaction between AI and a human researcher. This GPT-4 Reticular
+Chemist is an integrated system composed of three phases. Each of these
+utilizes GPT-4 in various capacities, wherein GPT-4 provides detailed
+instructions for chemical experimentation and the human provides feedback on
+the experimental outcomes, including both success and failures, for the
+in-context learning of AI in the next iteration. This iterative human-AI
+interaction enabled GPT-4 to learn from the outcomes, much like an experienced
+chemist, by a prompt-learning strategy. Importantly, the system is based on
+natural language for both development and operation, eliminating the need for
+coding skills, and thus, make it accessible to all chemists. Our collaboration
+with GPT-4 Reticular Chemist guided the discovery of an isoreticular series of
+MOFs, with each synthesis fine-tuned through iterative feedback and expert
+suggestions. This workflow presents a potential for broader applications in
+scientific research by harnessing the capability of large language models like
+GPT-4 to enhance the feasibility and efficiency of research activities.
+"
+Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale,Matthew Le,http://arxiv.org/pdf/2306.15687v2.pdf,2023-06-23,"['eess.as', 'cs.cl', 'cs.lg', 'cs.sd']",2306.15687v2.pdf,"  Large-scale generative models such as GPT and DALL-E have revolutionized the
+research community. These models not only generate high fidelity outputs, but
+are also generalists which can solve tasks not explicitly taught. In contrast,
+speech generative models are still primitive in terms of scale and task
+generalization. In this paper, we present Voicebox, the most versatile
+text-guided generative model for speech at scale. Voicebox is a
+non-autoregressive flow-matching model trained to infill speech, given audio
+context and text, trained on over 50K hours of speech that are not filtered or
+enhanced. Similar to GPT, Voicebox can perform many different tasks through
+in-context learning, but is more flexible as it can also condition on future
+context. Voicebox can be used for mono or cross-lingual zero-shot
+text-to-speech synthesis, noise removal, content editing, style conversion, and
+diverse sample generation. In particular, Voicebox outperforms the
+state-of-the-art zero-shot TTS model VALL-E on both intelligibility (5.9% vs
+1.9% word error rates) and audio similarity (0.580 vs 0.681) while being up to
+20 times faster. Audio samples can be found in
+\url{https://voicebox.metademolab.com}.
+"
+SPAE: Semantic Pyramid AutoEncoder for Multimodal Generation with Frozen  LLMs,Lijun Yu,http://arxiv.org/pdf/2306.17842v3.pdf,2023-06-30,"['cs.cv', 'cs.cl', 'cs.mm']",2306.17842v3.pdf,"  In this work, we introduce Semantic Pyramid AutoEncoder (SPAE) for enabling
+frozen LLMs to perform both understanding and generation tasks involving
+non-linguistic modalities such as images or videos. SPAE converts between raw
+pixels and interpretable lexical tokens (or words) extracted from the LLM's
+vocabulary. The resulting tokens capture both the semantic meaning and the
+fine-grained details needed for visual reconstruction, effectively translating
+the visual content into a language comprehensible to the LLM, and empowering it
+to perform a wide array of multimodal tasks. Our approach is validated through
+in-context learning experiments with frozen PaLM 2 and GPT 3.5 on a diverse set
+of image understanding and generation tasks. Our method marks the first
+successful attempt to enable a frozen LLM to generate image content while
+surpassing state-of-the-art performance in image understanding tasks, under the
+same setting, by over 25%.
+"
+RecallM: An Adaptable Memory Mechanism with Temporal Understanding for  Large Language Models,Brandon Kynoch,http://arxiv.org/pdf/2307.02738v3.pdf,2023-07-06,"['cs.ai', 'cs.cl', 'cs.sc']",2307.02738v3.pdf,"  Large Language Models (LLMs) have made extraordinary progress in the field of
+Artificial Intelligence and have demonstrated remarkable capabilities across a
+large variety of tasks and domains. However, as we venture closer to creating
+Artificial General Intelligence (AGI) systems, we recognize the need to
+supplement LLMs with long-term memory to overcome the context window limitation
+and more importantly, to create a foundation for sustained reasoning,
+cumulative learning and long-term user interaction. In this paper we propose
+RecallM, a novel architecture for providing LLMs with an adaptable and
+updatable long-term memory mechanism. Unlike previous methods, the RecallM
+architecture is particularly effective at belief updating and maintaining a
+temporal understanding of the knowledge provided to it. We demonstrate through
+various experiments the effectiveness of this architecture. Furthermore,
+through our own temporal understanding and belief updating experiments, we show
+that RecallM is four times more effective than using a vector database for
+updating knowledge previously stored in long-term memory. We also demonstrate
+that RecallM shows competitive performance on general question-answering and
+in-context learning tasks.
+"
+One Step of Gradient Descent is Provably the Optimal In-Context Learner  with One Layer of Linear Self-Attention,Arvind Mahankali,http://arxiv.org/pdf/2307.03576v1.pdf,2023-07-07,['cs.lg'],2307.03576v1.pdf,"  Recent works have empirically analyzed in-context learning and shown that
+transformers trained on synthetic linear regression tasks can learn to
+implement ridge regression, which is the Bayes-optimal predictor, given
+sufficient capacity [Aky\""urek et al., 2023], while one-layer transformers with
+linear self-attention and no MLP layer will learn to implement one step of
+gradient descent (GD) on a least-squares linear regression objective [von
+Oswald et al., 2022]. However, the theory behind these observations remains
+poorly understood. We theoretically study transformers with a single layer of
+linear self-attention, trained on synthetic noisy linear regression data.
+First, we mathematically show that when the covariates are drawn from a
+standard Gaussian distribution, the one-layer transformer which minimizes the
+pre-training loss will implement a single step of GD on the least-squares
+linear regression objective. Then, we find that changing the distribution of
+the covariates and weight vector to a non-isotropic Gaussian distribution has a
+strong impact on the learned algorithm: the global minimizer of the
+pre-training loss now implements a single step of $\textit{pre-conditioned}$
+GD. However, if only the distribution of the responses is changed, then this
+does not have a large effect on the learned algorithm: even when the response
+comes from a more general family of $\textit{nonlinear}$ functions, the global
+minimizer of the pre-training loss still implements a single step of GD on a
+least-squares linear regression objective.
+"
+Large Language Models as General Pattern Machines,Suvir Mirchandani,http://arxiv.org/pdf/2307.04721v2.pdf,2023-07-10,"['cs.ai', 'cs.cl', 'cs.ro']",2307.04721v2.pdf,"  We observe that pre-trained large language models (LLMs) are capable of
+autoregressively completing complex token sequences -- from arbitrary ones
+procedurally generated by probabilistic context-free grammars (PCFG), to more
+rich spatial patterns found in the Abstraction and Reasoning Corpus (ARC), a
+general AI benchmark, prompted in the style of ASCII art. Surprisingly, pattern
+completion proficiency can be partially retained even when the sequences are
+expressed using tokens randomly sampled from the vocabulary. These results
+suggest that without any additional training, LLMs can serve as general
+sequence modelers, driven by in-context learning. In this work, we investigate
+how these zero-shot capabilities may be applied to problems in robotics -- from
+extrapolating sequences of numbers that represent states over time to complete
+simple motions, to least-to-most prompting of reward-conditioned trajectories
+that can discover and represent closed-loop policies (e.g., a stabilizing
+controller for CartPole). While difficult to deploy today for real systems due
+to latency, context size limitations, and compute costs, the approach of using
+LLMs to drive low-level control may provide an exciting glimpse into how the
+patterns among words could be transferred to actions.
+"
+Mega-TTS 2: Zero-Shot Text-to-Speech with Arbitrary Length Speech  Prompts,Ziyue Jiang,http://arxiv.org/pdf/2307.07218v2.pdf,2023-07-14,"['eess.as', 'cs.sd']",2307.07218v2.pdf,"  Zero-shot text-to-speech aims at synthesizing voices with unseen speech
+prompts. Previous large-scale multispeaker TTS models have successfully
+achieved this goal with an enrolled recording within 10 seconds. However, most
+of them are designed to utilize only short speech prompts. The limited
+information in short speech prompts significantly hinders the performance of
+fine-grained identity imitation. In this paper, we introduce Mega-TTS 2, a
+generic zero-shot multispeaker TTS model that is capable of synthesizing speech
+for unseen speakers with arbitrary-length prompts. Specifically, we 1) design a
+multi-reference timbre encoder to extract timbre information from multiple
+reference speeches; 2) and train a prosody language model with arbitrary-length
+speech prompts; With these designs, our model is suitable for prompts of
+different lengths, which extends the upper bound of speech quality for
+zero-shot text-to-speech. Besides arbitrary-length prompts, we introduce
+arbitrary-source prompts, which leverages the probabilities derived from
+multiple P-LLM outputs to produce expressive and controlled prosody.
+Furthermore, we propose a phoneme-level auto-regressive duration model to
+introduce in-context learning capabilities to duration modeling. Experiments
+demonstrate that our method could not only synthesize identity-preserving
+speech with a short prompt of an unseen speaker but also achieve improved
+performance with longer speech prompts. Audio samples can be found in
+https://mega-tts.github.io/mega2_demo/.
+"
+Do Emergent Abilities Exist in Quantized Large Language Models: An  Empirical Study,Peiyu Liu,http://arxiv.org/pdf/2307.08072v2.pdf,2023-07-16,"['cs.cl', 'cs.ai']",2307.08072v2.pdf,"  Despite the superior performance, Large Language Models~(LLMs) require
+significant computational resources for deployment and use. To overcome this
+issue, quantization methods have been widely applied to reduce the memory
+footprint of LLMs as well as increasing the inference rate. However, a major
+challenge is that low-bit quantization methods often lead to performance
+degradation. It is important to understand how quantization impacts the
+capacity of LLMs. Different from previous studies focused on overall
+performance, this work aims to investigate the impact of quantization on
+\emph{emergent abilities}, which are important characteristics that distinguish
+LLMs from small language models. Specially, we examine the abilities of
+in-context learning, chain-of-thought reasoning, and instruction-following in
+quantized LLMs. Our empirical experiments show that these emergent abilities
+still exist in 4-bit quantization models, while 2-bit models encounter severe
+performance degradation on the test of these abilities. To improve the
+performance of low-bit models, we conduct two special experiments: (1)
+fine-gained impact analysis that studies which components (or substructures)
+are more sensitive to quantization, and (2) performance compensation through
+model fine-tuning. Our work derives a series of important findings to
+understand the impact of quantization on emergent abilities, and sheds lights
+on the possibilities of extremely low-bit quantization for LLMs.
+"
+Generating Mathematical Derivations with Large Language Models,Jordan Meadows,http://arxiv.org/pdf/2307.09998v3.pdf,2023-07-19,"['cs.cl', 'math.ho']",2307.09998v3.pdf,"  The derivation of mathematical results in specialised fields, using Large
+Language Models (LLMs), is an emerging research direction that can help
+identify models' limitations, and potentially support mathematical discovery.
+In this paper, we leverage a symbolic engine to generate derivations of
+equations at scale, and investigate the capabilities of LLMs when deriving goal
+equations from premises. Specifically, we employ in-context learning for GPT
+and fine-tune a range of T5 models to compare the robustness and generalisation
+of pre-training strategies to specialised models. Empirical results show that
+fine-tuned FLAN-T5-large (MathT5) outperforms GPT models on all static and
+out-of-distribution test sets in conventional scores. However, an in-depth
+analysis reveals that the fine-tuned models are more sensitive to perturbations
+involving unseen symbols and (to a lesser extent) changes to equation
+structure. In addition, we analyse 1.7K equations, and over 200 derivations, to
+highlight common reasoning errors such as the inclusion of incorrect,
+irrelevant, and redundant equations. Finally, we explore the suitability of
+existing metrics for evaluating mathematical derivations and find evidence
+that, while they can capture general properties such as sensitivity to
+perturbations, they fail to highlight fine-grained reasoning errors and
+essential differences between models. Overall, this work demonstrates that
+training models on synthetic data may improve their math capabilities beyond
+much larger LLMs, but current metrics are not appropriately assessing the
+quality of generated mathematical text.
+"
+LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA  Composition,Chengsong Huang,http://arxiv.org/pdf/2307.13269v1.pdf,2023-07-25,"['cs.cl', 'cs.ai']",2307.13269v1.pdf,"  Low-rank adaptations (LoRA) are often employed to fine-tune large language
+models (LLMs) for new tasks. This paper investigates LoRA composability for
+cross-task generalization and introduces LoraHub, a strategic framework devised
+for the purposive assembly of LoRA modules trained on diverse given tasks, with
+the objective of achieving adaptable performance on unseen tasks. With just a
+few examples from a novel task, LoraHub enables the fluid combination of
+multiple LoRA modules, eradicating the need for human expertise. Notably, the
+composition requires neither additional model parameters nor gradients. Our
+empirical results, derived from the Big-Bench Hard (BBH) benchmark, suggest
+that LoraHub can effectively mimic the performance of in-context learning in
+few-shot scenarios, excluding the necessity of in-context examples alongside
+each inference input. A significant contribution of our research is the
+fostering of a community for LoRA, where users can share their trained LoRA
+modules, thereby facilitating their application to new tasks. We anticipate
+this resource will widen access to and spur advancements in general
+intelligence as well as LLMs in production. Code will be available at
+https://github.com/sail-sg/lorahub.
+"
+LayoutLLM-T2I: Eliciting Layout Guidance from LLM for Text-to-Image  Generation,Leigang Qu,http://arxiv.org/pdf/2308.05095v2.pdf,2023-08-09,"['cs.cv', 'cs.ai']",2308.05095v2.pdf,"  In the text-to-image generation field, recent remarkable progress in Stable
+Diffusion makes it possible to generate rich kinds of novel photorealistic
+images. However, current models still face misalignment issues (e.g.,
+problematic spatial relation understanding and numeration failure) in complex
+natural scenes, which impedes the high-faithfulness text-to-image generation.
+Although recent efforts have been made to improve controllability by giving
+fine-grained guidance (e.g., sketch and scribbles), this issue has not been
+fundamentally tackled since users have to provide such guidance information
+manually. In this work, we strive to synthesize high-fidelity images that are
+semantically aligned with a given textual prompt without any guidance. Toward
+this end, we propose a coarse-to-fine paradigm to achieve layout planning and
+image generation. Concretely, we first generate the coarse-grained layout
+conditioned on a given textual prompt via in-context learning based on Large
+Language Models. Afterward, we propose a fine-grained object-interaction
+diffusion method to synthesize high-faithfulness images conditioned on the
+prompt and the automatically generated layout. Extensive experiments
+demonstrate that our proposed method outperforms the state-of-the-art models in
+terms of layout and image generation. Our code and settings are available at
+https://layoutllm-t2i.github.io.
+"
+AudioLDM 2: Learning Holistic Audio Generation with Self-supervised  Pretraining,Haohe Liu,http://arxiv.org/pdf/2308.05734v2.pdf,2023-08-10,"['cs.sd', 'cs.ai', 'cs.mm', 'eess.as', 'eess.sp']",2308.05734v2.pdf,"  Although audio generation shares commonalities across different types of
+audio, such as speech, music, and sound effects, designing models for each type
+requires careful consideration of specific objectives and biases that can
+significantly differ from those of other types. To bring us closer to a unified
+perspective of audio generation, this paper proposes a framework that utilizes
+the same learning method for speech, music, and sound effect generation. Our
+framework introduces a general representation of audio, called ""language of
+audio"" (LOA). Any audio can be translated into LOA based on AudioMAE, a
+self-supervised pre-trained representation learning model. In the generation
+process, we translate any modalities into LOA by using a GPT-2 model, and we
+perform self-supervised audio generation learning with a latent diffusion model
+conditioned on LOA. The proposed framework naturally brings advantages such as
+in-context learning abilities and reusable self-supervised pretrained AudioMAE
+and latent diffusion models. Experiments on the major benchmarks of
+text-to-audio, text-to-music, and text-to-speech demonstrate state-of-the-art
+or competitive performance against previous approaches. Our code, pretrained
+model, and demo are available at https://audioldm.github.io/audioldm2.
+"
+Time Travel in LLMs: Tracing Data Contamination in Large Language Models,Shahriar Golchin,http://arxiv.org/pdf/2308.08493v2.pdf,2023-08-16,"['cs.cl', 'cs.cr', 'cs.lg']",2308.08493v2.pdf,"  Data contamination, i.e., the presence of test data from downstream tasks in
+the training data of large language models (LLMs), is a potential major issue
+in measuring LLMs' real effectiveness on other tasks. We propose a
+straightforward yet effective method for identifying data contamination within
+LLMs. At its core, our approach starts by identifying potential contamination
+at the instance level; using this information, our approach then assesses wider
+contamination at the partition level. To estimate contamination of individual
+instances, we employ ""guided instruction:"" a prompt consisting of the dataset
+name, partition type, and the random-length initial segment of a reference
+instance, asking the LLM to complete it. An instance is flagged as contaminated
+if the LLM's output either exactly or nearly matches the latter segment of the
+reference. To understand if an entire partition is contaminated, we propose two
+ideas. The first idea marks a dataset partition as contaminated if the average
+overlap score with the reference instances (as measured by ROUGE-L or BLEURT)
+is statistically significantly better with the completions from guided
+instruction compared to a ""general instruction"" that does not include the
+dataset and partition name. The second idea marks a dataset partition as
+contaminated if a classifier based on GPT-4 with few-shot in-context learning
+prompt marks multiple generated completions as exact/near-exact matches of the
+corresponding reference instances. Our best method achieves an accuracy between
+92% and 100% in detecting if an LLM is contaminated with seven datasets,
+containing train and test/validation partitions, when contrasted with manual
+evaluation by human experts. Further, our findings indicate that GPT-4 is
+contaminated with AG News, WNLI, and XSum datasets.
+"
+Inductive-bias Learning: Generating Code Models with Large Language  Model,Toma Tanaka,http://arxiv.org/pdf/2308.09890v1.pdf,2023-08-19,"['cs.lg', 'cs.ai', 'cs.cl', 'cs.pl']",2308.09890v1.pdf,"  Large Language Models(LLMs) have been attracting attention due to a ability
+called in-context learning(ICL). ICL, without updating the parameters of a LLM,
+it is possible to achieve highly accurate inference based on rules ``in the
+context'' by merely inputting a training data into the prompt. Although ICL is
+a developing field with many unanswered questions, LLMs themselves serves as a
+inference model, seemingly realizing inference without explicitly indicate
+``inductive bias''. On the other hand, a code generation is also a highlighted
+application of LLMs. The accuracy of code generation has dramatically improved,
+enabling even non-engineers to generate code to perform the desired tasks by
+crafting appropriate prompts. In this paper, we propose a novel ``learning''
+method called an ``Inductive-Bias Learning (IBL)'', which combines the
+techniques of ICL and code generation. An idea of IBL is straightforward. Like
+ICL, IBL inputs a training data into the prompt and outputs a code with a
+necessary structure for inference (we referred to as ``Code Model'') from a
+``contextual understanding''. Despite being a seemingly simple approach, IBL
+encompasses both a ``property of inference without explicit inductive bias''
+inherent in ICL and a ``readability and explainability'' of the code
+generation. Surprisingly, generated Code Models have been found to achieve
+predictive accuracy comparable to, and in some cases surpassing, ICL and
+representative machine learning models. Our IBL code is open source:
+https://github.com/fuyu-quant/IBLM
+"
+Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation  with Large Language Models,Martin Weyssow,http://arxiv.org/pdf/2308.10462v1.pdf,2023-08-21,"['cs.se', 'cs.cl', 'cs.lg']",2308.10462v1.pdf,"  Large Language Models (LLMs) possess impressive capabilities to generate
+meaningful code snippets given natural language intents in zero-shot, i.e.,
+without the need for specific fine-tuning. In the perspective of unleashing
+their full potential, prior work has demonstrated the benefits of fine-tuning
+the models to task-specific data. However, fine-tuning process demands heavy
+computational costs and is intractable when resources are scarce, especially
+for models with billions of parameters. In light of these challenges, previous
+studies explored In-Context Learning (ICL) as an effective strategy to generate
+contextually appropriate code without fine-tuning. However, it operates at
+inference time and does not involve learning task-specific parameters,
+potentially limiting the model's performance on downstream tasks. In this
+context, we foresee that Parameter-Efficient Fine-Tuning (PEFT) techniques
+carry a high potential for efficiently specializing LLMs to task-specific data.
+In this paper, we deliver a comprehensive study of LLMs with the impact of PEFT
+techniques under the automated code generation scenario. Our experimental
+results reveal the superiority and potential of such techniques over ICL on a
+wide range of LLMs in reducing the computational burden and improving
+performance. Therefore, the study opens opportunities for broader applications
+of PEFT in software engineering scenarios.
+"
+Analyzing Transformer Dynamics as Movement through Embedding Space,Sumeet S. Singh,http://arxiv.org/pdf/2308.10874v1.pdf,2023-08-21,"['cs.lg', 'cs.ai', 'cs.cl', 'cs.ne']",2308.10874v1.pdf,"  Transformer language models exhibit intelligent behaviors such as
+understanding natural language, recognizing patterns, acquiring knowledge,
+reasoning, planning, reflecting and using tools. This paper explores how their
+underlying mechanics give rise to intelligent behaviors. We adopt a systems
+approach to analyze Transformers in detail and develop a mathematical framework
+that frames their dynamics as movement through embedding space. This novel
+perspective provides a principled way of thinking about the problem and reveals
+important insights related to the emergence of intelligence:
+  1. At its core the Transformer is a Embedding Space walker, mapping
+intelligent behavior to trajectories in this vector space.
+  2. At each step of the walk, it composes context into a single composite
+vector whose location in Embedding Space defines the next step.
+  3. No learning actually occurs during decoding; in-context learning and
+generalization are simply the result of different contexts composing into
+different vectors.
+  4. Ultimately the knowledge, intelligence and skills exhibited by the model
+are embodied in the organization of vectors in Embedding Space rather than in
+specific neurons or layers. These abilities are properties of this
+organization.
+  5. Attention's contribution boils down to the association-bias it lends to
+vector composition and which influences the aforementioned organization.
+However, more investigation is needed to ascertain its significance.
+  6. The entire model is composed from two principal operations: data
+independent filtering and data dependent aggregation. This generalization
+unifies Transformers with other sequence models and across modalities.
+  Building upon this foundation we formalize and test a semantic space theory
+which posits that embedding vectors represent semantic concepts and find some
+evidence of its validity.
+"
+Causal Intersectionality and Dual Form of Gradient Descent for  Multimodal Analysis: a Case Study on Hateful Memes,Yosuke Miyanishi,http://arxiv.org/pdf/2308.11585v1.pdf,2023-08-19,"['cs.ai', 'cs.cl']",2308.11585v1.pdf,"  In the wake of the explosive growth of machine learning (ML) usage,
+particularly within the context of emerging Large Language Models (LLMs),
+comprehending the semantic significance rooted in their internal workings is
+crucial. While causal analyses focus on defining semantics and its
+quantification, the gradient-based approach is central to explainable AI (XAI),
+tackling the interpretation of the black box. By synergizing these approaches,
+the exploration of how a model's internal mechanisms illuminate its causal
+effect has become integral for evidence-based decision-making. A parallel line
+of research has revealed that intersectionality - the combinatory impact of
+multiple demographics of an individual - can be structured in the form of an
+Averaged Treatment Effect (ATE). Initially, this study illustrates that the
+hateful memes detection problem can be formulated as an ATE, assisted by the
+principles of intersectionality, and that a modality-wise summarization of
+gradient-based attention attribution scores can delineate the distinct
+behaviors of three Transformerbased models concerning ATE. Subsequently, we
+show that the latest LLM LLaMA2 has the ability to disentangle the
+intersectional nature of memes detection in an in-context learning setting,
+with their mechanistic properties elucidated via meta-gradient, a secondary
+form of gradient. In conclusion, this research contributes to the ongoing
+dialogue surrounding XAI and the multifaceted nature of ML models.
+"
+Knowledge-Driven CoT: Exploring Faithful Reasoning in LLMs for  Knowledge-intensive Question Answering,Keheng Wang,http://arxiv.org/pdf/2308.13259v2.pdf,2023-08-25,"['cs.cl', 'cs.ai']",2308.13259v2.pdf,"  Equipped with Chain-of-Thought (CoT), Large language models (LLMs) have shown
+impressive reasoning ability in various downstream tasks. Even so, suffering
+from hallucinations and the inability to access external knowledge, LLMs often
+come with incorrect or unfaithful intermediate reasoning steps, especially in
+the context of answering knowledge-intensive tasks such as KBQA. To alleviate
+this issue, we propose a framework called Knowledge-Driven Chain-of-Thought
+(KD-CoT) to verify and modify reasoning traces in CoT via interaction with
+external knowledge, and thus overcome the hallucinations and error propagation.
+Concretely, we formulate the CoT rationale process of LLMs into a structured
+multi-round QA format. In each round, LLMs interact with a QA system that
+retrieves external knowledge and produce faithful reasoning traces based on
+retrieved precise answers. The structured CoT reasoning of LLMs is facilitated
+by our developed KBQA CoT collection, which serves as in-context learning
+demonstrations and can also be utilized as feedback augmentation to train a
+robust retriever. Extensive experiments on WebQSP and ComplexWebQuestion
+datasets demonstrate the effectiveness of proposed KD-CoT in task-solving
+reasoning generation, which outperforms the vanilla CoT ICL with an absolute
+success rate of 8.0% and 5.1%. Furthermore, our proposed feedback-augmented
+retriever outperforms the state-of-the-art baselines for retrieving knowledge,
+achieving significant improvement in Hit and recall performance. Our code and
+data are released on https://github.com/AdelWang/KD-CoT/tree/main.
+"
+Empowering Dynamics-aware Text-to-Video Diffusion with Large Language  Models,Hao Fei,http://arxiv.org/pdf/2308.13812v1.pdf,2023-08-26,"['cs.ai', 'cs.cv']",2308.13812v1.pdf,"  Text-to-video (T2V) synthesis has gained increasing attention in the
+community, in which the recently emerged diffusion models (DMs) have
+promisingly shown stronger performance than the past approaches. While existing
+state-of-the-art DMs are competent to achieve high-resolution video generation,
+they may largely suffer from key limitations (e.g., action occurrence
+disorders, crude video motions) with respect to the intricate temporal dynamics
+modeling, one of the crux of video synthesis. In this work, we investigate
+strengthening the awareness of video dynamics for DMs, for high-quality T2V
+generation. Inspired by human intuition, we design an innovative dynamic scene
+manager (dubbed as Dysen) module, which includes (step-1) extracting from input
+text the key actions with proper time-order arrangement, (step-2) transforming
+the action schedules into the dynamic scene graph (DSG) representations, and
+(step-3) enriching the scenes in the DSG with sufficient and reasonable
+details. Taking advantage of the existing powerful LLMs (e.g., ChatGPT) via
+in-context learning, Dysen realizes (nearly) human-level temporal dynamics
+understanding. Finally, the resulting video DSG with rich action scene details
+is encoded as fine-grained spatio-temporal features, integrated into the
+backbone T2V DM for video generating. Experiments on popular T2V datasets
+suggest that our framework consistently outperforms prior arts with significant
+margins, especially in the scenario with complex actions. Project page at
+https://haofei.vip/Dysen-VDM
+"
+Identifying and Mitigating the Security Risks of Generative AI,Clark Barrett,http://arxiv.org/pdf/2308.14840v3.pdf,2023-08-28,['cs.ai'],2308.14840v3.pdf,"  Every major technical invention resurfaces the dual-use dilemma -- the new
+technology has the potential to be used for good as well as for harm.
+Generative AI (GenAI) techniques, such as large language models (LLMs) and
+diffusion models, have shown remarkable capabilities (e.g., in-context
+learning, code-completion, and text-to-image generation and editing). However,
+GenAI can be used just as well by attackers to generate new attacks and
+increase the velocity and efficacy of existing attacks.
+  This paper reports the findings of a workshop held at Google (co-organized by
+Stanford University and the University of Wisconsin-Madison) on the dual-use
+dilemma posed by GenAI. This paper is not meant to be comprehensive, but is
+rather an attempt to synthesize some of the interesting findings from the
+workshop. We discuss short-term and long-term goals for the community on this
+topic. We hope this paper provides both a launching point for a discussion on
+this important topic as well as interesting problems that the research
+community can work to address.
+"
+AnomalyGPT: Detecting Industrial Anomalies using Large Vision-Language  Models,Zhaopeng Gu,http://arxiv.org/pdf/2308.15366v3.pdf,2023-08-29,['cs.cv'],2308.15366v3.pdf,"  Large Vision-Language Models (LVLMs) such as MiniGPT-4 and LLaVA have
+demonstrated the capability of understanding images and achieved remarkable
+performance in various visual tasks. Despite their strong abilities in
+recognizing common objects due to extensive training datasets, they lack
+specific domain knowledge and have a weaker understanding of localized details
+within objects, which hinders their effectiveness in the Industrial Anomaly
+Detection (IAD) task. On the other hand, most existing IAD methods only provide
+anomaly scores and necessitate the manual setting of thresholds to distinguish
+between normal and abnormal samples, which restricts their practical
+implementation. In this paper, we explore the utilization of LVLM to address
+the IAD problem and propose AnomalyGPT, a novel IAD approach based on LVLM. We
+generate training data by simulating anomalous images and producing
+corresponding textual descriptions for each image. We also employ an image
+decoder to provide fine-grained semantic and design a prompt learner to
+fine-tune the LVLM using prompt embeddings. Our AnomalyGPT eliminates the need
+for manual threshold adjustments, thus directly assesses the presence and
+locations of anomalies. Additionally, AnomalyGPT supports multi-turn dialogues
+and exhibits impressive few-shot in-context learning capabilities. With only
+one normal shot, AnomalyGPT achieves the state-of-the-art performance with an
+accuracy of 86.1%, an image-level AUC of 94.1%, and a pixel-level AUC of 95.3%
+on the MVTec-AD dataset. Code is available at
+https://github.com/CASIA-IVA-Lab/AnomalyGPT.
+"
+Taken out of context: On measuring situational awareness in LLMs,Lukas Berglund,http://arxiv.org/pdf/2309.00667v1.pdf,2023-09-01,"['cs.cl', 'cs.lg']",2309.00667v1.pdf,"  We aim to better understand the emergence of `situational awareness' in large
+language models (LLMs). A model is situationally aware if it's aware that it's
+a model and can recognize whether it's currently in testing or deployment.
+Today's LLMs are tested for safety and alignment before they are deployed. An
+LLM could exploit situational awareness to achieve a high score on safety
+tests, while taking harmful actions after deployment. Situational awareness may
+emerge unexpectedly as a byproduct of model scaling. One way to better foresee
+this emergence is to run scaling experiments on abilities necessary for
+situational awareness. As such an ability, we propose `out-of-context
+reasoning' (in contrast to in-context learning). We study out-of-context
+reasoning experimentally. First, we finetune an LLM on a description of a test
+while providing no examples or demonstrations. At test time, we assess whether
+the model can pass the test. To our surprise, we find that LLMs succeed on this
+out-of-context reasoning task. Their success is sensitive to the training setup
+and only works when we apply data augmentation. For both GPT-3 and LLaMA-1,
+performance improves with model size. These findings offer a foundation for
+further empirical study, towards predicting and potentially controlling the
+emergence of situational awareness in LLMs. Code is available at:
+https://github.com/AsaCooperStickland/situational-awareness-evals.
+"
+Business Process Text Sketch Automation Generation Using Large Language  Model,Rui Zhu,http://arxiv.org/pdf/2309.01071v1.pdf,2023-09-03,['cs.cl'],2309.01071v1.pdf,"  Business Process Management (BPM) is gaining increasing attention as it has
+the potential to cut costs while boosting output and quality. Business process
+document generation is a crucial stage in BPM. However, due to a shortage of
+datasets, data-driven deep learning techniques struggle to deliver the expected
+results. We propose an approach to transform Conditional Process Trees (CPTs)
+into Business Process Text Sketches (BPTSs) using Large Language Models (LLMs).
+The traditional prompting approach (Few-shot In-Context Learning) tries to get
+the correct answer in one go, and it can find the pattern of transforming
+simple CPTs into BPTSs, but for close-domain and CPTs with complex hierarchy,
+the traditional prompts perform weakly and with low correctness. We suggest
+using this technique to break down a difficult CPT into a number of basic CPTs
+and then solve each one in turn, drawing inspiration from the
+divide-and-conquer strategy. We chose 100 process trees with depths ranging
+from 2 to 5 at random, as well as CPTs with many nodes, many degrees of
+selection, and cyclic nesting. Experiments show that our method can achieve a
+correct rate of 93.42%, which is 45.17% better than traditional prompting
+methods. Our proposed method provides a solution for business process document
+generation in the absence of datasets, and secondly, it becomes potentially
+possible to provide a large number of datasets for the process model extraction
+(PME) domain.
+"
+Textbooks Are All You Need II: phi-1.5 technical report,Yuanzhi Li,http://arxiv.org/pdf/2309.05463v1.pdf,2023-09-11,"['cs.cl', 'cs.ai']",2309.05463v1.pdf,"  We continue the investigation into the power of smaller Transformer-based
+language models as initiated by \textbf{TinyStories} -- a 10 million parameter
+model that can produce coherent English -- and the follow-up work on
+\textbf{phi-1}, a 1.3 billion parameter model with Python coding performance
+close to the state-of-the-art. The latter work proposed to use existing Large
+Language Models (LLMs) to generate ``textbook quality"" data as a way to enhance
+the learning process compared to traditional web data. We follow the
+``Textbooks Are All You Need"" approach, focusing this time on common sense
+reasoning in natural language, and create a new 1.3 billion parameter model
+named \textbf{phi-1.5}, with performance on natural language tasks comparable
+to models 5x larger, and surpassing most non-frontier LLMs on more complex
+reasoning tasks such as grade-school mathematics and basic coding. More
+generally, \textbf{phi-1.5} exhibits many of the traits of much larger LLMs,
+both good -- such as the ability to ``think step by step"" or perform some
+rudimentary in-context learning -- and bad, including hallucinations and the
+potential for toxic and biased generations -- encouragingly though, we are
+seeing improvement on that front thanks to the absence of web data. We
+open-source \textbf{phi-1.5} to promote further research on these urgent
+topics.
+"
+Uncovering mesa-optimization algorithms in Transformers,Johannes von Oswald,http://arxiv.org/pdf/2309.05858v1.pdf,2023-09-11,"['cs.lg', 'cs.ai']",2309.05858v1.pdf,"  Transformers have become the dominant model in deep learning, but the reason
+for their superior performance is poorly understood. Here, we hypothesize that
+the strong performance of Transformers stems from an architectural bias towards
+mesa-optimization, a learned process running within the forward pass of a model
+consisting of the following two steps: (i) the construction of an internal
+learning objective, and (ii) its corresponding solution found through
+optimization. To test this hypothesis, we reverse-engineer a series of
+autoregressive Transformers trained on simple sequence modeling tasks,
+uncovering underlying gradient-based mesa-optimization algorithms driving the
+generation of predictions. Moreover, we show that the learned forward-pass
+optimization algorithm can be immediately repurposed to solve supervised
+few-shot tasks, suggesting that mesa-optimization might underlie the in-context
+learning capabilities of large language models. Finally, we propose a novel
+self-attention layer, the mesa-layer, that explicitly and efficiently solves
+optimization problems specified in context. We find that this layer can lead to
+improved performance in synthetic and preliminary language modeling
+experiments, adding weight to our hypothesis that mesa-optimization is an
+important operation hidden within the weights of trained Transformers.
+"
+Narrowing the Gap between Supervised and Unsupervised Sentence  Representation Learning with Large Language Model,Mingxin Li,http://arxiv.org/pdf/2309.06453v1.pdf,2023-09-12,"['cs.cl', 'cs.lg']",2309.06453v1.pdf,"  Sentence Representation Learning (SRL) is a fundamental task in Natural
+Language Processing (NLP), with Contrastive learning of Sentence Embeddings
+(CSE) as the mainstream technique due to its superior performance. An
+intriguing phenomenon in CSE is the significant performance gap between
+supervised and unsupervised methods, even when their sentence encoder and loss
+function are the same. Previous works attribute this performance gap to
+differences in two representation properties (alignment and uniformity).
+However, alignment and uniformity only measure the results, which means they
+cannot answer ""What happens during the training process that leads to the
+performance gap?"" and ""How can the performance gap be narrowed?"". In this
+paper, we conduct empirical experiments to answer these ""What"" and ""How""
+questions. We first answer the ""What"" question by thoroughly comparing the
+behavior of supervised and unsupervised CSE during their respective training
+processes. From the comparison, We observe a significant difference in fitting
+difficulty. Thus, we introduce a metric, called Fitting Difficulty Increment
+(FDI), to measure the fitting difficulty gap between the evaluation dataset and
+the held-out training dataset, and use the metric to answer the ""What""
+question. Then, based on the insights gained from the ""What"" question, we
+tackle the ""How"" question by increasing the fitting difficulty of the training
+dataset. We achieve this by leveraging the In-Context Learning (ICL) capability
+of the Large Language Model (LLM) to generate data that simulates complex
+patterns. By utilizing the hierarchical patterns in the LLM-generated data, we
+effectively narrow the gap between supervised and unsupervised CSE.
+"
+Understanding Catastrophic Forgetting in Language Models via Implicit  Inference,Suhas Kotha,http://arxiv.org/pdf/2309.10105v1.pdf,2023-09-18,"['cs.cl', 'cs.lg']",2309.10105v1.pdf,"  Fine-tuning (via methods such as instruction-tuning or reinforcement learning
+from human feedback) is a crucial step in training language models to robustly
+carry out tasks of interest. However, we lack a systematic understanding of the
+effects of fine-tuning, particularly on tasks outside the narrow fine-tuning
+distribution. In a simplified scenario, we demonstrate that improving
+performance on tasks within the fine-tuning data distribution comes at the
+expense of suppressing model capabilities on other tasks. This degradation is
+especially pronounced for tasks ""closest"" to the fine-tuning distribution. We
+hypothesize that language models implicitly infer the task of the prompt
+corresponds, and the fine-tuning process predominantly skews this task
+inference towards tasks in the fine-tuning distribution. To test this
+hypothesis, we propose Conjugate Prompting to see if we can recover pretrained
+capabilities. Conjugate prompting artificially makes the task look farther from
+the fine-tuning distribution while requiring the same capability. We find that
+conjugate prompting systematically recovers some of the pretraining
+capabilities on our synthetic setup. We then apply conjugate prompting to
+real-world LLMs using the observation that fine-tuning distributions are
+typically heavily skewed towards English. We find that simply translating the
+prompts to different languages can cause the fine-tuned models to respond like
+their pretrained counterparts instead. This allows us to recover the in-context
+learning abilities lost via instruction tuning, and more concerningly, to
+recover harmful content generation suppressed by safety fine-tuning in chatbots
+like ChatGPT.
+"
+GPT4AIGChip: Towards Next-Generation AI Accelerator Design Automation  via Large Language Models,Yonggan Fu,http://arxiv.org/pdf/2309.10730v1.pdf,2023-09-19,"['cs.lg', 'cs.ar']",2309.10730v1.pdf,"  The remarkable capabilities and intricate nature of Artificial Intelligence
+(AI) have dramatically escalated the imperative for specialized AI
+accelerators. Nonetheless, designing these accelerators for various AI
+workloads remains both labor- and time-intensive. While existing design
+exploration and automation tools can partially alleviate the need for extensive
+human involvement, they still demand substantial hardware expertise, posing a
+barrier to non-experts and stifling AI accelerator development. Motivated by
+the astonishing potential of large language models (LLMs) for generating
+high-quality content in response to human language instructions, we embark on
+this work to examine the possibility of harnessing LLMs to automate AI
+accelerator design. Through this endeavor, we develop GPT4AIGChip, a framework
+intended to democratize AI accelerator design by leveraging human natural
+languages instead of domain-specific languages. Specifically, we first perform
+an in-depth investigation into LLMs' limitations and capabilities for AI
+accelerator design, thus aiding our understanding of our current position and
+garnering insights into LLM-powered automated AI accelerator design.
+Furthermore, drawing inspiration from the above insights, we develop a
+framework called GPT4AIGChip, which features an automated demo-augmented
+prompt-generation pipeline utilizing in-context learning to guide LLMs towards
+creating high-quality AI accelerator design. To our knowledge, this work is the
+first to demonstrate an effective pipeline for LLM-powered automated AI
+accelerator generation. Accordingly, we anticipate that our insights and
+framework can serve as a catalyst for innovations in next-generation
+LLM-powered design automation tools.
+"
+User Simulation with Large Language Models for Evaluating Task-Oriented  Dialogue,Sam Davidson,http://arxiv.org/pdf/2309.13233v1.pdf,2023-09-23,['cs.cl'],2309.13233v1.pdf,"  One of the major impediments to the development of new task-oriented dialogue
+(TOD) systems is the need for human evaluation at multiple stages and
+iterations of the development process. In an effort to move toward automated
+evaluation of TOD, we propose a novel user simulator built using recently
+developed large pretrained language models (LLMs). In order to increase the
+linguistic diversity of our system relative to the related previous work, we do
+not fine-tune the LLMs used by our system on existing TOD datasets; rather we
+use in-context learning to prompt the LLMs to generate robust and
+linguistically diverse output with the goal of simulating the behavior of human
+interlocutors. Unlike previous work, which sought to maximize goal success rate
+(GSR) as the primary metric of simulator performance, our goal is a system
+which achieves a GSR similar to that observed in human interactions with TOD
+systems. Using this approach, our current simulator is effectively able to
+interact with several TOD systems, especially on single-intent conversational
+goals, while generating lexically and syntactically diverse output relative to
+previous simulators that rely upon fine-tuned models. Finally, we collect a
+Human2Bot dataset of humans interacting with the same TOD systems with which we
+experimented in order to better quantify these achievements.
+"
+A Benchmark for Learning to Translate a New Language from One Grammar  Book,Garrett Tanzer,http://arxiv.org/pdf/2309.16575v1.pdf,2023-09-28,['cs.cl'],2309.16575v1.pdf,"  Large language models (LLMs) can perform impressive feats with in-context
+learning or lightweight finetuning. It is natural to wonder how well these
+models adapt to genuinely new tasks, but how does one find tasks that are
+unseen in internet-scale training sets? We turn to a field that is explicitly
+motivated and bottlenecked by a scarcity of web data: low-resource languages.
+In this paper, we introduce MTOB (Machine Translation from One Book), a
+benchmark for learning to translate between English and Kalamang -- a language
+with less than 200 speakers and therefore virtually no presence on the web --
+using several hundred pages of field linguistics reference materials. This task
+framing is novel in that it asks a model to learn a language from a single
+human-readable book of grammar explanations, rather than a large mined corpus
+of in-domain data, more akin to L2 learning than L1 acquisition. We demonstrate
+that baselines using current LLMs are promising but fall short of human
+performance, achieving 44.7 chrF on Kalamang to English translation and 45.8
+chrF on English to Kalamang translation, compared to 51.6 and 57.0 chrF by a
+human who learned Kalamang from the same reference materials. We hope that MTOB
+will help measure LLM capabilities along a new dimension, and that the methods
+developed to solve it could help expand access to language technology for
+underserved communities by leveraging qualitatively different kinds of data
+than traditional machine translation.
+"
+Benchmarking Cognitive Biases in Large Language Models as Evaluators,Ryan Koo,http://arxiv.org/pdf/2309.17012v1.pdf,2023-09-29,"['cs.cl', 'cs.ai', 'cs.lg']",2309.17012v1.pdf,"  Large Language Models (LLMs) have recently been shown to be effective as
+automatic evaluators with simple prompting and in-context learning. In this
+work, we assemble 15 LLMs of four different size ranges and evaluate their
+output responses by preference ranking from the other LLMs as evaluators, such
+as System Star is better than System Square. We then evaluate the quality of
+ranking outputs introducing the Cognitive Bias Benchmark for LLMs as Evaluators
+(CoBBLEr), a benchmark to measure six different cognitive biases in LLM
+evaluation outputs, such as the Egocentric bias where a model prefers to rank
+its own outputs highly in evaluation. We find that LLMs are biased text quality
+evaluators, exhibiting strong indications on our bias benchmark (average of 40%
+of comparisons across all models) within each of their evaluations that
+question their robustness as evaluators. Furthermore, we examine the
+correlation between human and machine preferences and calculate the average
+Rank-Biased Overlap (RBO) score to be 49.6%, indicating that machine
+preferences are misaligned with humans. According to our findings, LLMs may
+still be unable to be utilized for automatic annotation aligned with human
+preferences. Our project page is at: https://minnesotanlp.github.io/cobbler.
+"
+Fewer-token Neural Speech Codec with Time-invariant Codes,Yong Ren,http://arxiv.org/pdf/2310.00014v1.pdf,2023-09-15,"['cs.sd', 'eess.as']",2310.00014v1.pdf,"  Language model based text-to-speech (TTS) models, like VALL-E, have gained
+attention for their outstanding in-context learning capability in zero-shot
+scenarios. Neural speech codec is a critical component of these models, which
+can convert speech into discrete token representations. However, excessive
+token sequences from the codec may negatively affect prediction accuracy and
+restrict the progression of Language model based TTS models. To address this
+issue, this paper proposes a novel neural speech codec with time-invariant
+codes named TiCodec. By encoding and quantizing time-invariant information into
+a separate code, TiCodec can reduce the amount of frame-level information that
+needs encoding, effectively decreasing the number of tokens as codes of speech.
+Furthermore, this paper introduces a time-invariant encoding consistency loss
+to enhance the consistency of time-invariant code within an utterance and force
+it to capture more global information, which can benefit the zero-shot TTS
+task. Experimental results demonstrate that TiCodec can not only enhance the
+quality of reconstruction speech with fewer tokens but also increase the
+similarity and naturalness, as well as reduce the word error rate of the
+synthesized speech by the TTS model.
+"
+ReAcTable: Enhancing ReAct for Table Question Answering,Yunjia Zhang,http://arxiv.org/pdf/2310.00815v1.pdf,2023-10-01,['cs.db'],2310.00815v1.pdf,"  Table Question Answering (TQA) presents a substantial challenge at the
+intersection of natural language processing and data analytics. This task
+involves answering natural language (NL) questions on top of tabular data,
+demanding proficiency in logical reasoning, understanding of data semantics,
+and fundamental analytical capabilities. Due to its significance, a substantial
+volume of research has been dedicated to exploring a wide range of strategies
+aimed at tackling this challenge including approaches that leverage Large
+Language Models (LLMs) through in-context learning or Chain-of-Thought (CoT)
+prompting as well as approaches that train and fine-tune custom models.
+  Nonetheless, a conspicuous gap exists in the research landscape, where there
+is limited exploration of how innovative foundational research, which
+integrates incremental reasoning with external tools in the context of LLMs, as
+exemplified by the ReAct paradigm, could potentially bring advantages to the
+TQA task. In this paper, we aim to fill this gap, by introducing ReAcTable
+(ReAct for Table Question Answering tasks), a framework inspired by the ReAct
+paradigm that is carefully enhanced to address the challenges uniquely
+appearing in TQA tasks such as interpreting complex data semantics, dealing
+with errors generated by inconsistent data and generating intricate data
+transformations. ReAcTable relies on external tools such as SQL and Python code
+executors, to progressively enhance the data by generating intermediate data
+representations, ultimately transforming it into a more accessible format for
+answering the questions with greater ease. We demonstrate that ReAcTable
+achieves remarkable performance even when compared to fine-tuned approaches. In
+particular, it outperforms the best prior result on the WikiTQ benchmark,
+achieving an accuracy of 68.0% without requiring training a new model or
+fine-tuning.
+"
+GraphText: Graph Reasoning in Text Space,Jianan Zhao,http://arxiv.org/pdf/2310.01089v1.pdf,2023-10-02,"['cs.cl', 'cs.lg']",2310.01089v1.pdf,"  Large Language Models (LLMs) have gained the ability to assimilate human
+knowledge and facilitate natural language interactions with both humans and
+other LLMs. However, despite their impressive achievements, LLMs have not made
+significant advancements in the realm of graph machine learning. This
+limitation arises because graphs encapsulate distinct relational data, making
+it challenging to transform them into natural language that LLMs understand. In
+this paper, we bridge this gap with a novel framework, GraphText, that
+translates graphs into natural language. GraphText derives a graph-syntax tree
+for each graph that encapsulates both the node attributes and inter-node
+relationships. Traversal of the tree yields a graph text sequence, which is
+then processed by an LLM to treat graph tasks as text generation tasks.
+Notably, GraphText offers multiple advantages. It introduces training-free
+graph reasoning: even without training on graph data, GraphText with ChatGPT
+can achieve on par with, or even surpassing, the performance of
+supervised-trained graph neural networks through in-context learning (ICL).
+Furthermore, GraphText paves the way for interactive graph reasoning, allowing
+both humans and LLMs to communicate with the model seamlessly using natural
+language. These capabilities underscore the vast, yet-to-be-explored potential
+of LLMs in the domain of graph machine learning.
+"
+LLMParser: A LLM-based Log Parsing Framework,Zhihan Jiang,http://arxiv.org/pdf/2310.01796v1.pdf,2023-10-03,['cs.se'],2310.01796v1.pdf,"  The process of log parsing, which converts log messages into structured
+formats, is a crucial step for various log analysis tasks. Although numerous
+log parsers have been proposed, their effectiveness on complex log data is
+often hindered due to reliance on human-made rules or learning-based models
+with limited training data. The recent rise of powerful large language models
+(LLMs) shows potential for log parsing due to their extensive pre-trained
+knowledge related to code and logging. However, their accuracy is currently
+limited due to the lack of specialized log parsing capabilities. Additionally,
+the inconsistency of their answers and significant overhead obstruct the
+practical implementation of LLM-based log parsing.
+  To tackle these challenges, we introduce LLMParser, the first practical
+LLM-based log parsing framework. LLMParser enables accurate and robust log
+parsing by leveraging the in-context learning (ICL) capability of the LLM,
+employing a hierarchical candidate sampling algorithm, and selecting
+high-quality demonstrations. LLMParser also includes a novel adaptive parsing
+cache component to store and refine the templates generated by the LLM. This
+design aids in addressing the inefficiency of LLMs by rapid matching to
+previously parsed log templates. LLMParser also adaptively updates the
+templates in the parsing cache to ensure consistent parsed results. Extensive
+evaluation on large-scale public datasets demonstrates that LLMParser surpasses
+the state-of-the-art methods. Furthermore, LLMParser significantly reduces the
+query times to LLMs, achieving efficiency comparable to the most efficient
+baseline, Drain.
+"
+Uncovering hidden geometry in Transformers via disentangling position  and context,Jiajun Song,http://arxiv.org/pdf/2310.04861v1.pdf,2023-10-07,"['cs.lg', 'cs.ai', 'stat.ml']",2310.04861v1.pdf,"  Transformers are widely used to extract complex semantic meanings from input
+tokens, yet they usually operate as black-box models. In this paper, we present
+a simple yet informative decomposition of hidden states (or embeddings) of
+trained transformers into interpretable components. For any layer, embedding
+vectors of input sequence samples are represented by a tensor $\boldsymbol{h}
+\in \mathbb{R}^{C \times T \times d}$. Given embedding vector
+$\boldsymbol{h}_{c,t} \in \mathbb{R}^d$ at sequence position $t \le T$ in a
+sequence (or context) $c \le C$, extracting the mean effects yields the
+decomposition \[ \boldsymbol{h}_{c,t} = \boldsymbol{\mu} + \mathbf{pos}_t +
+\mathbf{ctx}_c + \mathbf{resid}_{c,t} \] where $\boldsymbol{\mu}$ is the global
+mean vector, $\mathbf{pos}_t$ and $\mathbf{ctx}_c$ are the mean vectors across
+contexts and across positions respectively, and $\mathbf{resid}_{c,t}$ is the
+residual vector. For popular transformer architectures and diverse text
+datasets, empirically we find pervasive mathematical structure: (1)
+$(\mathbf{pos}_t)_{t}$ forms a low-dimensional, continuous, and often spiral
+shape across layers, (2) $(\mathbf{ctx}_c)_c$ shows clear cluster structure
+that falls into context topics, and (3) $(\mathbf{pos}_t)_{t}$ and
+$(\mathbf{ctx}_c)_c$ are mutually incoherent -- namely $\mathbf{pos}_t$ is
+almost orthogonal to $\mathbf{ctx}_c$ -- which is canonical in compressed
+sensing and dictionary learning. This decomposition offers structural insights
+about input formats in in-context learning (especially for induction heads) and
+in arithmetic tasks.
+"
+Lightweight In-Context Tuning for Multimodal Unified Models,Yixin Chen,http://arxiv.org/pdf/2310.05109v1.pdf,2023-10-08,['cs.cv'],2310.05109v1.pdf,"  In-context learning (ICL) involves reasoning from given contextual examples.
+As more modalities comes, this procedure is becoming more challenging as the
+interleaved input modalities convolutes the understanding process. This is
+exemplified by the observation that multimodal models often struggle to
+effectively extrapolate from contextual examples to perform ICL. To address
+these challenges, we introduce MultiModal In-conteXt Tuning (M$^2$IXT), a
+lightweight module to enhance the ICL capabilities of multimodal unified
+models. The proposed M$^2$IXT module perceives an expandable context window to
+incorporate various labeled examples of multiple modalities (e.g., text, image,
+and coordinates). It can be prepended to various multimodal unified models
+(e.g., OFA, Unival, LLaVA) of different architectures and trained via a
+mixed-tasks strategy to enable rapid few-shot adaption on multiple tasks and
+datasets. When tuned on as little as 50K multimodal data, M$^2$IXT can boost
+the few-shot ICL performance significantly (e.g., 18\% relative increase for
+OFA), and obtained state-of-the-art results across an array of tasks including
+visual question answering, image captioning, visual grounding, and visual
+entailment, while being considerably small in terms of model parameters (e.g.,
+$\sim$$20\times$ smaller than Flamingo or MMICL), highlighting the flexibility
+and effectiveness of M$^2$IXT as a multimodal in-context learner.
+"
+Explainable Claim Verification via Knowledge-Grounded Reasoning with  Large Language Models,Haoran Wang,http://arxiv.org/pdf/2310.05253v2.pdf,2023-10-08,"['cs.cl', 'cs.ai', 'cs.lg']",2310.05253v2.pdf,"  Claim verification plays a crucial role in combating misinformation. While
+existing works on claim verification have shown promising results, a crucial
+piece of the puzzle that remains unsolved is to understand how to verify claims
+without relying on human-annotated data, which is expensive to create at a
+large scale. Additionally, it is important for models to provide comprehensive
+explanations that can justify their decisions and assist human fact-checkers.
+This paper presents First-Order-Logic-Guided Knowledge-Grounded (FOLK)
+Reasoning that can verify complex claims and generate explanations without the
+need for annotated evidence using Large Language Models (LLMs). FOLK leverages
+the in-context learning ability of LLMs to translate the claim into a
+First-Order-Logic (FOL) clause consisting of predicates, each corresponding to
+a sub-claim that needs to be verified. Then, FOLK performs FOL-Guided reasoning
+over a set of knowledge-grounded question-and-answer pairs to make veracity
+predictions and generate explanations to justify its decision-making process.
+This process makes our model highly explanatory, providing clear explanations
+of its reasoning process in human-readable form. Our experiment results
+indicate that FOLK outperforms strong baselines on three datasets encompassing
+various claim verification challenges. Our code and data are available.
+"
+Glitter or Gold? Deriving Structured Insights from Sustainability  Reports via Large Language Models,Marco Bronzini,http://arxiv.org/pdf/2310.05628v2.pdf,2023-10-09,"['cs.cl', 'cs.ce', 'cs.cy']",2310.05628v2.pdf,"  Over the last decade, several regulatory bodies have started requiring the
+disclosure of non-financial information from publicly listed companies, in
+light of the investors' increasing attention to Environmental, Social, and
+Governance (ESG) issues. Such information is publicly released in a variety of
+non-structured and multi-modal documentation. Hence, it is not straightforward
+to aggregate and consolidate such data in a cohesive framework to further
+derive insights about sustainability practices across companies and markets.
+Given these premises, it is natural to resort to Information Extraction (IE)
+techniques to provide concise, informative, and actionable data to the
+stakeholders. Moving beyond traditional text processing techniques, in this
+work we leverage Large Language Models (LLMs), along with the prominent
+in-context learning technique and the Retrieved Augmented Generation (RAG)
+paradigm, to extract semantically structured ESG-related information from
+companies' sustainability reports. We then adopt graph-based representations to
+conduct meaningful statistical, similarity and correlation analyses concerning
+the ESG-related actions disclosed by companies in their sustainability reports.
+These analyses unveiled that companies address ESG-related issues through
+several actions encompassing recognition, compliance, and partnerships;
+highlighting the complexity and joint efforts needed to address them. Moreover,
+disclosure similarities emerged among companies from the same region or sector.
+Lastly, we investigate which factual aspects impact the most on companies' ESG
+scores using our findings and other company information. This analysis unveiled
+that companies' disclosures affect ESG scores more than other financial or
+company characteristics.
+"
+Are Large Language Models Post Hoc Explainers?,Nicholas Kroeger,http://arxiv.org/pdf/2310.05797v2.pdf,2023-10-09,"['cs.cl', 'cs.ai', 'cs.lg']",2310.05797v2.pdf,"  Large Language Models (LLMs) are increasingly used as powerful tools for a
+plethora of natural language processing (NLP) applications. A recent
+innovation, in-context learning (ICL), enables LLMs to learn new tasks by
+supplying a few examples in the prompt during inference time, thereby
+eliminating the need for model fine-tuning. While LLMs have been utilized in
+several applications, their applicability in explaining the behavior of other
+models remains relatively unexplored. Despite the growing number of new
+explanation techniques, many require white-box access to the model and/or are
+computationally expensive, highlighting a need for next-generation post hoc
+explainers. In this work, we present the first framework to study the
+effectiveness of LLMs in explaining other predictive models. More specifically,
+we propose a novel framework encompassing multiple prompting strategies: i)
+Perturbation-based ICL, ii) Prediction-based ICL, iii) Instruction-based ICL,
+and iv) Explanation-based ICL, with varying levels of information about the
+underlying ML model and the local neighborhood of the test sample. We conduct
+extensive experiments with real-world benchmark datasets to demonstrate that
+LLM-generated explanations perform on par with state-of-the-art post hoc
+explainers using their ability to leverage ICL examples and their internal
+knowledge in generating model explanations. On average, across four datasets
+and two ML models, we observe that LLMs identify the most important feature
+with 72.19% accuracy, opening up new frontiers in explainable artificial
+intelligence (XAI) to explore LLM-based explanation frameworks.
+"
+SALMON: Self-Alignment with Principle-Following Reward Models,Zhiqing Sun,http://arxiv.org/pdf/2310.05910v1.pdf,2023-10-09,"['cs.cl', 'cs.ai', 'cs.lg']",2310.05910v1.pdf,"  Supervised Fine-Tuning (SFT) on response demonstrations combined with
+Reinforcement Learning from Human Feedback (RLHF) constitutes a powerful
+paradigm for aligning LLM-based AI agents. However, a significant limitation of
+such an approach is its dependency on high-quality human annotations, making
+its application to intricate tasks challenging due to difficulties in obtaining
+consistent response demonstrations and in-distribution response preferences.
+This paper presents a novel approach, namely SALMON (Self-ALignMent with
+principle-fOllowiNg reward models), to align base language models with minimal
+human supervision, using only a small set of human-defined principles, yet
+achieving superior performance. Central to our approach is a
+principle-following reward model. Trained on synthetic preference data, this
+model can generate reward scores based on arbitrary human-defined principles.
+By merely adjusting these principles during the RL training phase, we gain full
+control over the preferences with the reward model, subsequently influencing
+the behavior of the RL-trained policies, and eliminating the reliance on the
+collection of online human preferences. Applying our method to the LLaMA-2-70b
+base language model, we developed an AI assistant named Dromedary-2. With only
+6 exemplars for in-context learning and 31 human-defined principles,
+Dromedary-2 significantly surpasses the performance of several state-of-the-art
+AI systems, including LLaMA-2-Chat-70b, on various benchmark datasets. We have
+open-sourced the code and model weights to encourage further research into
+aligning LLM-based AI agents with enhanced supervision efficiency, improved
+controllability, and scalable oversight.
+"
+OpsEval: A Comprehensive Task-Oriented AIOps Benchmark for Large  Language Models,Yuhe Liu,http://arxiv.org/pdf/2310.07637v2.pdf,2023-10-11,"['cs.ai', 'cs.ni']",2310.07637v2.pdf,"  Large language models (LLMs) have exhibited remarkable capabilities in
+NLP-related tasks such as translation, summarizing, and generation. The
+application of LLMs in specific areas, notably AIOps (Artificial Intelligence
+for IT Operations), holds great potential due to their advanced abilities in
+information summarizing, report analyzing, and ability of API calling.
+Nevertheless, the performance of current LLMs in AIOps tasks is yet to be
+determined. Furthermore, a comprehensive benchmark is required to steer the
+optimization of LLMs tailored for AIOps. Compared with existing benchmarks that
+focus on evaluating specific fields like network configuration, in this paper,
+we present \textbf{OpsEval}, a comprehensive task-oriented AIOps benchmark
+designed for LLMs. For the first time, OpsEval assesses LLMs' proficiency in
+three crucial scenarios (Wired Network Operation, 5G Communication Operation,
+and Database Operation) at various ability levels (knowledge recall, analytical
+thinking, and practical application). The benchmark includes 7,200 questions in
+both multiple-choice and question-answer (QA) formats, available in English and
+Chinese. With quantitative and qualitative results, we show how various LLM
+tricks can affect the performance of AIOps, including zero-shot,
+chain-of-thought, and few-shot in-context learning. We find that GPT4-score is
+more consistent with experts than widely used Bleu and Rouge, which can be used
+to replace automatic metrics for large-scale qualitative evaluations.
+"
+EIPE-text: Evaluation-Guided Iterative Plan Extraction for Long-Form  Narrative Text Generation,Wang You,http://arxiv.org/pdf/2310.08185v1.pdf,2023-10-12,"['cs.cl', 'cs.ai']",2310.08185v1.pdf,"  Plan-and-Write is a common hierarchical approach in long-form narrative text
+generation, which first creates a plan to guide the narrative writing.
+Following this approach, several studies rely on simply prompting large
+language models for planning, which often yields suboptimal results. In this
+paper, we propose a new framework called Evaluation-guided Iterative Plan
+Extraction for long-form narrative text generation (EIPE-text), which extracts
+plans from the corpus of narratives and utilizes the extracted plans to
+construct a better planner. EIPE-text has three stages: plan extraction,
+learning, and inference. In the plan extraction stage, it iteratively extracts
+and improves plans from the narrative corpus and constructs a plan corpus. We
+propose a question answer (QA) based evaluation mechanism to automatically
+evaluate the plans and generate detailed plan refinement instructions to guide
+the iterative improvement. In the learning stage, we build a better planner by
+fine-tuning with the plan corpus or in-context learning with examples in the
+plan corpus. Finally, we leverage a hierarchical approach to generate long-form
+narratives. We evaluate the effectiveness of EIPE-text in the domains of novels
+and storytelling. Both GPT-4-based evaluations and human evaluations
+demonstrate that our method can generate more coherent and relevant long-form
+narratives. Our code will be released in the future.
+"
+Prompting Large Language Models with Chain-of-Thought for Few-Shot  Knowledge Base Question Generation,Yuanyuan Liang,http://arxiv.org/pdf/2310.08395v3.pdf,2023-10-12,"['cs.cl', 'cs.ai']",2310.08395v3.pdf,"  The task of Question Generation over Knowledge Bases (KBQG) aims to convert a
+logical form into a natural language question. For the sake of expensive cost
+of large-scale question annotation, the methods of KBQG under low-resource
+scenarios urgently need to be developed. However, current methods heavily rely
+on annotated data for fine-tuning, which is not well-suited for few-shot
+question generation. The emergence of Large Language Models (LLMs) has shown
+their impressive generalization ability in few-shot tasks. Inspired by
+Chain-of-Thought (CoT) prompting, which is an in-context learning strategy for
+reasoning, we formulate KBQG task as a reasoning problem, where the generation
+of a complete question is splitted into a series of sub-question generation.
+Our proposed prompting method KQG-CoT first retrieves supportive logical forms
+from the unlabeled data pool taking account of the characteristics of the
+logical form. Then, we write a prompt to explicit the reasoning chain of
+generating complicated questions based on the selected demonstrations. To
+further ensure prompt quality, we extend KQG-CoT into KQG-CoT+ via sorting the
+logical forms by their complexity. We conduct extensive experiments over three
+public KBQG datasets. The results demonstrate that our prompting method
+consistently outperforms other prompting baselines on the evaluated datasets.
+Remarkably, our KQG-CoT+ method could surpass existing few-shot SoTA results of
+the PathQuestions dataset by 18.25, 10.72, and 10.18 absolute points on BLEU-4,
+METEOR, and ROUGE-L, respectively.
+"
+Do pretrained Transformers Really Learn In-context by Gradient Descent?,Lingfeng Shen,http://arxiv.org/pdf/2310.08540v1.pdf,2023-10-12,"['cs.cl', 'cs.ai', 'cs.lg']",2310.08540v1.pdf,"  Is In-Context Learning (ICL) implicitly equivalent to Gradient Descent (GD)?
+Several recent works draw analogies between the dynamics of GD and the emergent
+behavior of ICL in large language models. However, these works make assumptions
+far from the realistic natural language setting in which language models are
+trained. Such discrepancies between theory and practice, therefore, necessitate
+further investigation to validate their applicability.
+  We start by highlighting the weaknesses in prior works that construct
+Transformer weights to simulate gradient descent. Their experiments with
+training Transformers on ICL objective, inconsistencies in the order
+sensitivity of ICL and GD, sparsity of the constructed weights, and sensitivity
+to parameter changes are some examples of a mismatch from the real-world
+setting.
+  Furthermore, we probe and compare the ICL vs. GD hypothesis in a natural
+setting. We conduct comprehensive empirical analyses on language models
+pretrained on natural data (LLaMa-7B). Our comparisons on various performance
+metrics highlight the inconsistent behavior of ICL and GD as a function of
+various factors such as datasets, models, and number of demonstrations. We
+observe that ICL and GD adapt the output distribution of language models
+differently. These results indicate that the equivalence between ICL and GD is
+an open hypothesis, requires nuanced considerations and calls for further
+studies.
+"
+Mastering Robot Manipulation with Multimodal Prompts through Pretraining  and Multi-task Fine-tuning,Jiachen Li,http://arxiv.org/pdf/2310.09676v1.pdf,2023-10-14,"['cs.ro', 'cs.ai']",2310.09676v1.pdf,"  Prompt-based learning has been demonstrated as a compelling paradigm
+contributing to large language models' tremendous success (LLMs). Inspired by
+their success in language tasks, existing research has leveraged LLMs in
+embodied instruction following and task planning. However, not much attention
+has been paid to embodied tasks with multimodal prompts, combining vision
+signals with text descriptions. This type of task poses a major challenge to
+robots' capability to understand the interconnection and complementarity
+between vision and language signals. In this work, we introduce an effective
+framework that learns a policy to perform robot manipulation with multimodal
+prompts from multi-task expert trajectories. Our methods consist of a two-stage
+training pipeline that performs inverse dynamics pretraining and multi-task
+finetuning. To facilitate multimodal understanding, we design our multimodal
+prompt encoder by augmenting a pretrained LM with a residual connection to the
+visual input and model the dependencies among action dimensions. Empirically,
+we evaluate the efficacy of our method on the VIMA-BENCH and establish a new
+state-of-the-art (10% improvement in success rate). Moreover, we demonstrate
+that our model exhibits remarkable in-context learning ability.
+"
+Unifying Image Processing as Visual Prompting Question Answering,Yihao Liu,http://arxiv.org/pdf/2310.10513v1.pdf,2023-10-16,"['cs.cv', 'eess.iv']",2310.10513v1.pdf,"  Image processing is a fundamental task in computer vision, which aims at
+enhancing image quality and extracting essential features for subsequent vision
+applications. Traditionally, task-specific models are developed for individual
+tasks and designing such models requires distinct expertise. Building upon the
+success of large language models (LLMs) in natural language processing (NLP),
+there is a similar trend in computer vision, which focuses on developing
+large-scale models through pretraining and in-context learning. This paradigm
+shift reduces the reliance on task-specific models, yielding a powerful unified
+model to deal with various tasks. However, these advances have predominantly
+concentrated on high-level vision tasks, with less attention paid to low-level
+vision tasks. To address this issue, we propose a universal model for general
+image processing that covers image restoration, image enhancement, image
+feature extraction tasks, \textit{etc}. Our proposed framework, named
+PromptGIP, unifies these diverse image processing tasks within a universal
+framework. Inspired by NLP question answering (QA) techniques, we employ a
+visual prompting question answering paradigm. Specifically, we treat the
+input-output image pair as a structured question-answer sentence, thereby
+reprogramming the image processing task as a prompting QA problem. PromptGIP
+can undertake diverse \textbf{cross-domain} tasks using provided visual
+prompts, eliminating the need for task-specific finetuning. Our methodology
+offers a universal and adaptive solution to general image processing. While
+PromptGIP has demonstrated a certain degree of out-of-domain task
+generalization capability, further research is expected to fully explore its
+more powerful emergent generalization.
+"
+In-Context Pretraining: Language Modeling Beyond Document Boundaries,Weijia Shi,http://arxiv.org/pdf/2310.10638v3.pdf,2023-10-16,"['cs.cl', 'cs.ai', 'cs.lg']",2310.10638v3.pdf,"  Large language models (LMs) are currently trained to predict tokens given
+document prefixes, enabling them to directly perform long-form generation and
+prompting-style tasks which can be reduced to document completion. Existing
+pretraining pipelines train LMs by concatenating random sets of short documents
+to create input contexts but the prior documents provide no signal for
+predicting the next document. We instead present In-Context Pretraining, a new
+approach where language models are pretrained on a sequence of related
+documents, thereby explicitly encouraging them to read and reason across
+document boundaries. We can do In-Context Pretraining by simply changing the
+document ordering so that each context contains related documents, and directly
+applying existing pretraining pipelines. However, this document sorting problem
+is challenging. There are billions of documents and we would like the sort to
+maximize contextual similarity for every document without repeating any data.
+To do this, we introduce approximate algorithms for finding related documents
+with efficient nearest neighbor search and constructing coherent input contexts
+with a graph traversal algorithm. Our experiments show In-Context Pretraining
+offers a simple and scalable approach to significantly enhance LMs'performance:
+we see notable improvements in tasks that require more complex contextual
+reasoning, including in-context learning (+8%), reading comprehension (+15%),
+faithfulness to previous contexts (+16%), long-context reasoning (+5%), and
+retrieval augmentation (+9%).
+"
+IDEAL: Influence-Driven Selective Annotations Empower In-Context  Learners in Large Language Models,Shaokun Zhang,http://arxiv.org/pdf/2310.10873v1.pdf,2023-10-16,['cs.cl'],2310.10873v1.pdf,"  In-context learning is a promising paradigm that utilizes in-context examples
+as prompts for the predictions of large language models. These prompts are
+crucial for achieving strong performance. However, since the prompts need to be
+sampled from a large volume of annotated examples, finding the right prompt may
+result in high annotation costs. To address this challenge, this paper
+introduces an influence-driven selective annotation method that aims to
+minimize annotation costs while improving the quality of in-context examples.
+The essence of our method is to select a pivotal subset from a large-scale
+unlabeled data pool to annotate for the subsequent sampling of prompts.
+Specifically, a directed graph is first constructed to represent unlabeled
+data. Afterward, the influence of candidate unlabeled subsets is quantified
+with a diffusion process. A simple yet effective greedy algorithm for unlabeled
+data selection is lastly introduced. It iteratively selects the data if it
+provides a maximum marginal gain with respect to quantified influence. Compared
+with previous efforts on selective annotations, our influence-driven method
+works in an end-to-end manner, avoids an intractable explicit balance between
+data diversity and representativeness, and enjoys theoretical support.
+Experiments confirm the superiority of the proposed method on various
+benchmarks, achieving better performance under lower time consumption during
+subset selection. The project page is available at
+https://skzhang1.github.io/IDEAL/.
+"
+Eureka: Human-Level Reward Design via Coding Large Language Models,Yecheng Jason Ma,http://arxiv.org/pdf/2310.12931v1.pdf,2023-10-19,"['cs.ro', 'cs.ai', 'cs.lg']",2310.12931v1.pdf,"  Large Language Models (LLMs) have excelled as high-level semantic planners
+for sequential decision-making tasks. However, harnessing them to learn complex
+low-level manipulation tasks, such as dexterous pen spinning, remains an open
+problem. We bridge this fundamental gap and present Eureka, a human-level
+reward design algorithm powered by LLMs. Eureka exploits the remarkable
+zero-shot generation, code-writing, and in-context improvement capabilities of
+state-of-the-art LLMs, such as GPT-4, to perform evolutionary optimization over
+reward code. The resulting rewards can then be used to acquire complex skills
+via reinforcement learning. Without any task-specific prompting or pre-defined
+reward templates, Eureka generates reward functions that outperform expert
+human-engineered rewards. In a diverse suite of 29 open-source RL environments
+that include 10 distinct robot morphologies, Eureka outperforms human experts
+on 83% of the tasks, leading to an average normalized improvement of 52%. The
+generality of Eureka also enables a new gradient-free in-context learning
+approach to reinforcement learning from human feedback (RLHF), readily
+incorporating human inputs to improve the quality and the safety of the
+generated rewards without model updating. Finally, using Eureka rewards in a
+curriculum learning setting, we demonstrate for the first time, a simulated
+Shadow Hand capable of performing pen spinning tricks, adeptly manipulating a
+pen in circles at rapid speed.
+"
+Self-prompted Chain-of-Thought on Large Language Models for Open-domain  Multi-hop Reasoning,Jinyuan Wang,http://arxiv.org/pdf/2310.13552v2.pdf,2023-10-20,"['cs.cl', 'cs.ai']",2310.13552v2.pdf,"  In open-domain question-answering (ODQA), most existing questions require
+single-hop reasoning on commonsense. To further extend this task, we officially
+introduce open-domain multi-hop reasoning (ODMR) by answering multi-hop
+questions with explicit reasoning steps in open-domain setting. Recently, large
+language models (LLMs) have found significant utility in facilitating ODQA
+without external corpus. Furthermore, chain-of-thought (CoT) prompting boosts
+the reasoning capability of LLMs to a greater extent with manual or automated
+paradigms. However, existing automated methods lack of quality assurance, while
+manual approaches suffer from limited scalability and poor diversity, hindering
+the capabilities of LLMs. In this paper, we propose Self-prompted
+Chain-of-Thought (SP-CoT), an automated framework to mass-produce high quality
+CoTs of LLMs, by LLMs and for LLMs. SP-CoT introduces an automated generation
+pipeline of high quality ODMR datasets, an adaptive sampler for in-context CoT
+selection and self-prompted inference via in-context learning. Extensive
+experiments on four multi-hop question-answering benchmarks show that our
+proposed SP-CoT not only significantly surpasses the previous SOTA methods on
+large-scale (175B) LLMs, but also nearly doubles the zero-shot performance of
+small-scale (13B) LLMs. Further analysis reveals the remarkable capability of
+SP-CoT to elicit direct and concise intermediate reasoning steps by recalling
+$\sim$50\% of intermediate answers on MuSiQue-Ans dataset.
+"
+Explainable Depression Symptom Detection in Social Media,Eliseo Bao Souto,http://arxiv.org/pdf/2310.13664v2.pdf,2023-10-20,['cs.cl'],2310.13664v2.pdf,"  Users of social platforms often perceive these sites as supportive spaces to
+post about their mental health issues. Those conversations contain important
+traces about individuals' health risks. Recently, researchers have exploited
+this online information to construct mental health detection models, which aim
+to identify users at risk on platforms like Twitter, Reddit or Facebook. Most
+of these models are centred on achieving good classification results, ignoring
+the explainability and interpretability of the decisions. Recent research has
+pointed out the importance of using clinical markers, such as the use of
+symptoms, to improve trust in the computational models by health professionals.
+In this paper, we propose using transformer-based architectures to detect and
+explain the appearance of depressive symptom markers in the users' writings. We
+present two approaches: i) train a model to classify, and another one to
+explain the classifier's decision separately and ii) unify the two tasks
+simultaneously using a single model. Additionally, for this latter manner, we
+also investigated the performance of recent conversational LLMs when using
+in-context learning. Our natural language explanations enable clinicians to
+interpret the models' decisions based on validated symptoms, enhancing trust in
+the automated process. We evaluate our approach using recent symptom-based
+datasets, employing both offline and expert-in-the-loop metrics to assess the
+quality of the explanations generated by our models. The experimental results
+show that it is possible to achieve good classification results while
+generating interpretable symptom-based explanations.
+"
+Ensemble-Instruct: Generating Instruction-Tuning Data with a  Heterogeneous Mixture of LMs,Young-Suk Lee,http://arxiv.org/pdf/2310.13961v1.pdf,2023-10-21,"['cs.cl', 'cs.ai']",2310.13961v1.pdf,"  Using in-context learning (ICL) for data generation, techniques such as
+Self-Instruct (Wang et al., 2023) or the follow-up Alpaca (Taori et al., 2023)
+can train strong conversational agents with only a small amount of human
+supervision. One limitation of these approaches is that they resort to very
+large language models (around 175B parameters) that are also proprietary and
+non-public. Here we explore the application of such techniques to language
+models that are much smaller (around 10B--40B parameters) and have permissive
+licenses. We find the Self-Instruct approach to be less effective at these
+sizes and propose new ICL methods that draw on two main ideas: (a)
+Categorization and simplification of the ICL templates to make prompt learning
+easier for the LM, and (b) Ensembling over multiple LM outputs to help select
+high-quality synthetic examples. Our algorithm leverages the 175 Self-Instruct
+seed tasks and employs separate pipelines for instructions that require an
+input and instructions that do not. Empirical investigations with different LMs
+show that: (1) Our proposed method yields higher-quality instruction tuning
+data than Self-Instruct, (2) It improves performances of both vanilla and
+instruction-tuned LMs by significant margins, and (3) Smaller instruction-tuned
+LMs generate more useful outputs than their larger un-tuned counterparts. Our
+codebase is available at https://github.com/IBM/ensemble-instruct.
+"
+Investigating the Fairness of Large Language Models for Predictions on  Tabular Data,Yanchen Liu,http://arxiv.org/pdf/2310.14607v1.pdf,2023-10-23,"['cs.cl', 'cs.lg']",2310.14607v1.pdf,"  Recent literature has suggested the potential of using large language models
+(LLMs) to make predictions for tabular tasks. However, LLMs have been shown to
+exhibit harmful social biases that reflect the stereotypes and inequalities
+present in the society. To this end, as well as the widespread use of tabular
+data in many high-stake applications, it is imperative to explore the following
+questions: what sources of information do LLMs draw upon when making
+predictions for tabular tasks; whether and to what extent are LLM predictions
+for tabular tasks influenced by social biases and stereotypes; and what are the
+consequential implications for fairness? Through a series of experiments, we
+delve into these questions and show that LLMs tend to inherit social biases
+from their training data which significantly impact their fairness in tabular
+prediction tasks. Furthermore, our investigations show that in the context of
+bias mitigation, though in-context learning and fine-tuning have a moderate
+effect, the fairness metric gap between different subgroups is still larger
+than that in traditional machine learning models, such as Random Forest and
+shallow Neural Networks. This observation emphasizes that the social biases are
+inherent within the LLMs themselves and inherited from their pre-training
+corpus, not only from the downstream task datasets. Besides, we demonstrate
+that label-flipping of in-context examples can significantly reduce biases,
+further highlighting the presence of inherent bias within LLMs.
+"
+Large Language Models are Visual Reasoning Coordinators,Liangyu Chen,http://arxiv.org/pdf/2310.15166v1.pdf,2023-10-23,"['cs.cv', 'cs.cl']",2310.15166v1.pdf,"  Visual reasoning requires multimodal perception and commonsense cognition of
+the world. Recently, multiple vision-language models (VLMs) have been proposed
+with excellent commonsense reasoning ability in various domains. However, how
+to harness the collective power of these complementary VLMs is rarely explored.
+Existing methods like ensemble still struggle to aggregate these models with
+the desired higher-order communications. In this work, we propose Cola, a novel
+paradigm that coordinates multiple VLMs for visual reasoning. Our key insight
+is that a large language model (LLM) can efficiently coordinate multiple VLMs
+by facilitating natural language communication that leverages their distinct
+and complementary capabilities. Extensive experiments demonstrate that our
+instruction tuning variant, Cola-FT, achieves state-of-the-art performance on
+visual question answering (VQA), outside knowledge VQA, visual entailment, and
+visual spatial reasoning tasks. Moreover, we show that our in-context learning
+variant, Cola-Zero, exhibits competitive performance in zero and few-shot
+settings, without finetuning. Through systematic ablation studies and
+visualizations, we validate that a coordinator LLM indeed comprehends the
+instruction prompts as well as the separate functionalities of VLMs; it then
+coordinates them to enable impressive visual reasoning capabilities.
+"
+Function Vectors in Large Language Models,Eric Todd,http://arxiv.org/pdf/2310.15213v1.pdf,2023-10-23,"['cs.cl', 'cs.lg']",2310.15213v1.pdf,"  We report the presence of a simple neural mechanism that represents an
+input-output function as a vector within autoregressive transformer language
+models (LMs). Using causal mediation analysis on a diverse range of
+in-context-learning (ICL) tasks, we find that a small number attention heads
+transport a compact representation of the demonstrated task, which we call a
+function vector (FV). FVs are robust to changes in context, i.e., they trigger
+execution of the task on inputs such as zero-shot and natural text settings
+that do not resemble the ICL contexts from which they are collected. We test
+FVs across a range of tasks, models, and layers and find strong causal effects
+across settings in middle layers. We investigate the internal structure of FVs
+and find while that they often contain information that encodes the output
+space of the function, this information alone is not sufficient to reconstruct
+an FV. Finally, we test semantic vector composition in FVs, and find that to
+some extent they can be summed to create vectors that trigger new complex
+tasks. Taken together, our findings suggest that LLMs contain internal
+abstractions of general-purpose functions that can be invoked in a variety of
+contexts.
+"
+TCRA-LLM: Token Compression Retrieval Augmented Large Language Model for  Inference Cost Reduction,Junyi Liu,http://arxiv.org/pdf/2310.15556v2.pdf,2023-10-24,"['cs.cl', 'cs.ir']",2310.15556v2.pdf,"  Since ChatGPT released its API for public use, the number of applications
+built on top of commercial large language models (LLMs) increase exponentially.
+One popular usage of such models is leveraging its in-context learning ability
+and generating responses given user queries leveraging knowledge obtained by
+retrieval augmentation. One problem of deploying commercial retrieval-augmented
+LLMs is the cost due to the additionally retrieved context that largely
+increases the input token size of the LLMs. To mitigate this, we propose a
+token compression scheme that includes two methods: summarization compression
+and semantic compression. The first method applies a T5-based model that is
+fine-tuned by datasets generated using self-instruct containing samples with
+varying lengths and reduce token size by doing summarization. The second method
+further compresses the token size by removing words with lower impact on the
+semantic. In order to adequately evaluate the effectiveness of the proposed
+methods, we propose and utilize a dataset called Food-Recommendation DB (FRDB)
+focusing on food recommendation for women around pregnancy period or infants.
+Our summarization compression can reduce 65% of the retrieval token size with
+further 0.3% improvement on the accuracy; semantic compression provides a more
+flexible way to trade-off the token size with performance, for which we can
+reduce the token size by 20% with only 1.6% of accuracy drop.
+"
+Testing the Limits: Unusual Text Inputs Generation for Mobile App Crash  Detection with Large Language Model,Zhe Liu,http://arxiv.org/pdf/2310.15657v1.pdf,2023-10-24,['cs.se'],2310.15657v1.pdf,"  Mobile applications have become a ubiquitous part of our daily life,
+providing users with access to various services and utilities. Text input, as
+an important interaction channel between users and applications, plays an
+important role in core functionality such as search queries, authentication,
+messaging, etc. However, certain special text (e.g., -18 for Font Size) can
+cause the app to crash, and generating diversified unusual inputs for fully
+testing the app is highly demanded. Nevertheless, this is also challenging due
+to the combination of explosion dilemma, high context sensitivity, and complex
+constraint relations. This paper proposes InputBlaster which leverages the LLM
+to automatically generate unusual text inputs for mobile app crash detection.
+It formulates the unusual inputs generation problem as a task of producing a
+set of test generators, each of which can yield a batch of unusual text inputs
+under the same mutation rule. In detail, InputBlaster leverages LLM to produce
+the test generators together with the mutation rules serving as the reasoning
+chain, and utilizes the in-context learning schema to demonstrate the LLM with
+examples for boosting the performance. InputBlaster is evaluated on 36 text
+input widgets with cash bugs involving 31 popular Android apps, and results
+show that it achieves 78% bug detection rate, with 136% higher than the best
+baseline. Besides, we integrate it with the automated GUI testing tool and
+detect 37 unseen crashes in real-world apps from Google Play.
+"
+ExPT: Synthetic Pretraining for Few-Shot Experimental Design,Tung Nguyen,http://arxiv.org/pdf/2310.19961v1.pdf,2023-10-30,"['cs.lg', 'cs.ai']",2310.19961v1.pdf,"  Experimental design is a fundamental problem in many science and engineering
+fields. In this problem, sample efficiency is crucial due to the time, money,
+and safety costs of real-world design evaluations. Existing approaches either
+rely on active data collection or access to large, labeled datasets of past
+experiments, making them impractical in many real-world scenarios. In this
+work, we address the more challenging yet realistic setting of few-shot
+experimental design, where only a few labeled data points of input designs and
+their corresponding values are available. We approach this problem as a
+conditional generation task, where a model conditions on a few labeled examples
+and the desired output to generate an optimal input design. To this end, we
+introduce Experiment Pretrained Transformers (ExPT), a foundation model for
+few-shot experimental design that employs a novel combination of synthetic
+pretraining with in-context learning. In ExPT, we only assume knowledge of a
+finite collection of unlabelled data points from the input domain and pretrain
+a transformer neural network to optimize diverse synthetic functions defined
+over this domain. Unsupervised pretraining allows ExPT to adapt to any design
+task at test time in an in-context fashion by conditioning on a few labeled
+data points from the target task and generating the candidate optima. We
+evaluate ExPT on few-shot experimental design in challenging domains and
+demonstrate its superior generality and performance compared to existing
+methods. The source code is available at https://github.com/tung-nd/ExPT.git.
+"
+Unleashing the Creative Mind: Language Model As Hierarchical Policy For  Improved Exploration on Challenging Problem Solving,Zhan Ling,http://arxiv.org/pdf/2311.00694v1.pdf,2023-11-01,"['cs.ai', 'cs.cl']",2311.00694v1.pdf,"  Large Language Models (LLMs) have achieved tremendous progress, yet they
+still often struggle with challenging reasoning problems. Current approaches
+address this challenge by sampling or searching detailed and low-level
+reasoning chains. However, these methods are still limited in their exploration
+capabilities, making it challenging for correct solutions to stand out in the
+huge solution space. In this work, we unleash LLMs' creative potential for
+exploring multiple diverse problem solving strategies by framing an LLM as a
+hierarchical policy via in-context learning. This policy comprises of a
+visionary leader that proposes multiple diverse high-level problem-solving
+tactics as hints, accompanied by a follower that executes detailed
+problem-solving processes following each of the high-level instruction. The
+follower uses each of the leader's directives as a guide and samples multiple
+reasoning chains to tackle the problem, generating a solution group for each
+leader proposal. Additionally, we propose an effective and efficient
+tournament-based approach to select among these explored solution groups to
+reach the final answer. Our approach produces meaningful and inspiring hints,
+enhances problem-solving strategy exploration, and improves the final answer
+accuracy on challenging problems in the MATH dataset. Code will be released at
+https://github.com/lz1oceani/LLM-As-Hierarchical-Policy.
+"
+Sentiment Analysis through LLM Negotiations,Xiaofei Sun,http://arxiv.org/pdf/2311.01876v1.pdf,2023-11-03,['cs.cl'],2311.01876v1.pdf,"  A standard paradigm for sentiment analysis is to rely on a singular LLM and
+makes the decision in a single round under the framework of in-context
+learning. This framework suffers the key disadvantage that the single-turn
+output generated by a single LLM might not deliver the perfect decision, just
+as humans sometimes need multiple attempts to get things right. This is
+especially true for the task of sentiment analysis where deep reasoning is
+required to address the complex linguistic phenomenon (e.g., clause
+composition, irony, etc) in the input.
+  To address this issue, this paper introduces a multi-LLM negotiation
+framework for sentiment analysis. The framework consists of a reasoning-infused
+generator to provide decision along with rationale, a explanation-deriving
+discriminator to evaluate the credibility of the generator. The generator and
+the discriminator iterate until a consensus is reached. The proposed framework
+naturally addressed the aforementioned challenge, as we are able to take the
+complementary abilities of two LLMs, have them use rationale to persuade each
+other for correction.
+  Experiments on a wide range of sentiment analysis benchmarks (SST-2, Movie
+Review, Twitter, yelp, amazon, IMDB) demonstrate the effectiveness of proposed
+approach: it consistently yields better performances than the ICL baseline
+across all benchmarks, and even superior performances to supervised baselines
+on the Twitter and movie review datasets.
+"
+ChEF: A Comprehensive Evaluation Framework for Standardized Assessment  of Multimodal Large Language Models,Zhelun Shi,http://arxiv.org/pdf/2311.02692v1.pdf,2023-11-05,['cs.cv'],2311.02692v1.pdf,"  Multimodal Large Language Models (MLLMs) have shown impressive abilities in
+interacting with visual content with myriad potential downstream tasks.
+However, even though a list of benchmarks has been proposed, the capabilities
+and limitations of MLLMs are still not comprehensively understood, due to a
+lack of a standardized and holistic evaluation framework. To this end, we
+present the first Comprehensive Evaluation Framework (ChEF) that can
+holistically profile each MLLM and fairly compare different MLLMs. First, we
+structure ChEF as four modular components, i.e., Scenario as scalable
+multimodal datasets, Instruction as flexible instruction retrieving formulae,
+Inferencer as reliable question answering strategies, and Metric as indicative
+task-specific score functions. Based on them, ChEF facilitates versatile
+evaluations in a standardized framework, and new evaluations can be built by
+designing new Recipes (systematic selection of these four components). Notably,
+current MLLM benchmarks can be readily summarized as recipes of ChEF. Second,
+we introduce 6 new recipes to quantify competent MLLMs' desired capabilities
+(or called desiderata, i.e., calibration, in-context learning, instruction
+following, language performance, hallucination, and robustness) as reliable
+agents that can perform real-world multimodal interactions. Third, we conduct a
+large-scale evaluation of 9 prominent MLLMs on 9 scenarios and 6 desiderata.
+Our evaluation summarized over 20 valuable observations concerning the
+generalizability of MLLMs across various scenarios and the composite capability
+of MLLMs required for multimodal interactions. We will publicly release all the
+detailed implementations for further analysis, as well as an easy-to-use
+modular toolkit for the integration of new recipes and models, so that ChEF can
+be a growing evaluation framework for the MLLM community.
+"
+Kinematic-aware Prompting for Generalizable Articulated Object  Manipulation with LLMs,Wenke Xia,http://arxiv.org/pdf/2311.02847v2.pdf,2023-11-06,"['cs.ro', 'cs.ai']",2311.02847v2.pdf,"  Generalizable articulated object manipulation is essential for home-assistant
+robots. Recent efforts focus on imitation learning from demonstrations or
+reinforcement learning in simulation, however, due to the prohibitive costs of
+real-world data collection and precise object simulation, it still remains
+challenging for these works to achieve broad adaptability across diverse
+articulated objects. Recently, many works have tried to utilize the strong
+in-context learning ability of Large Language Models (LLMs) to achieve
+generalizable robotic manipulation, but most of these researches focus on
+high-level task planning, sidelining low-level robotic control. In this work,
+building on the idea that the kinematic structure of the object determines how
+we can manipulate it, we propose a kinematic-aware prompting framework that
+prompts LLMs with kinematic knowledge of objects to generate low-level motion
+trajectory waypoints, supporting various object manipulation. To effectively
+prompt LLMs with the kinematic structure of different objects, we design a
+unified kinematic knowledge parser, which represents various articulated
+objects as a unified textual description containing kinematic joints and
+contact location. Building upon this unified description, a kinematic-aware
+planner model is proposed to generate precise 3D manipulation waypoints via a
+designed kinematic-aware chain-of-thoughts prompting method. Our evaluation
+spanned 48 instances across 16 distinct categories, revealing that our
+framework not only outperforms traditional methods on 8 seen categories but
+also shows a powerful zero-shot capability for 8 unseen articulated object
+categories. Moreover, the real-world experiments on 7 different object
+categories prove our framework's adaptability in practical scenarios. Code is
+released at
+\href{https://github.com/GeWu-Lab/LLM_articulated_object_manipulation/tree/main}{here}.
+"
+In-Context Learning for Knowledge Base Question Answering for Unmanned  Systems based on Large Language Models,Yunlong Chen,http://arxiv.org/pdf/2311.02956v1.pdf,2023-11-06,"['cs.cl', 'cs.ai', 'i.2.7']",2311.02956v1.pdf,"  Knowledge Base Question Answering (KBQA) aims to answer factoid questions
+based on knowledge bases. However, generating the most appropriate knowledge
+base query code based on Natural Language Questions (NLQ) poses a significant
+challenge in KBQA. In this work, we focus on the CCKS2023 Competition of
+Question Answering with Knowledge Graph Inference for Unmanned Systems.
+Inspired by the recent success of large language models (LLMs) like ChatGPT and
+GPT-3 in many QA tasks, we propose a ChatGPT-based Cypher Query Language (CQL)
+generation framework to generate the most appropriate CQL based on the given
+NLQ. Our generative framework contains six parts: an auxiliary model predicting
+the syntax-related information of CQL based on the given NLQ, a proper noun
+matcher extracting proper nouns from the given NLQ, a demonstration example
+selector retrieving similar examples of the input sample, a prompt constructor
+designing the input template of ChatGPT, a ChatGPT-based generation model
+generating the CQL, and an ensemble model to obtain the final answers from
+diversified outputs. With our ChatGPT-based CQL generation framework, we
+achieved the second place in the CCKS 2023 Question Answering with Knowledge
+Graph Inference for Unmanned Systems competition, achieving an F1-score of
+0.92676.
+"
+Retrieval-Augmented Code Generation for Universal Information Extraction,Yucan Guo,http://arxiv.org/pdf/2311.02962v1.pdf,2023-11-06,"['cs.ai', 'cs.cl', 'cs.ir']",2311.02962v1.pdf,"  Information Extraction (IE) aims to extract structural knowledge (e.g.,
+entities, relations, events) from natural language texts, which brings
+challenges to existing methods due to task-specific schemas and complex text
+expressions. Code, as a typical kind of formalized language, is capable of
+describing structural knowledge under various schemas in a universal way. On
+the other hand, Large Language Models (LLMs) trained on both codes and texts
+have demonstrated powerful capabilities of transforming texts into codes, which
+provides a feasible solution to IE tasks. Therefore, in this paper, we propose
+a universal retrieval-augmented code generation framework based on LLMs, called
+Code4UIE, for IE tasks. Specifically, Code4UIE adopts Python classes to define
+task-specific schemas of various structural knowledge in a universal way. By so
+doing, extracting knowledge under these schemas can be transformed into
+generating codes that instantiate the predefined Python classes with the
+information in texts. To generate these codes more precisely, Code4UIE adopts
+the in-context learning mechanism to instruct LLMs with examples. In order to
+obtain appropriate examples for different tasks, Code4UIE explores several
+example retrieval strategies, which can retrieve examples semantically similar
+to the given texts. Extensive experiments on five representative IE tasks
+across nine datasets demonstrate the effectiveness of the Code4UIE framework.
+"
+Unified Low-Resource Sequence Labeling by Sample-Aware Dynamic Sparse  Finetuning,Sarkar Snigdha Sarathi Das,http://arxiv.org/pdf/2311.03748v1.pdf,2023-11-07,['cs.cl'],2311.03748v1.pdf,"  Unified Sequence Labeling that articulates different sequence labeling
+problems such as Named Entity Recognition, Relation Extraction, Semantic Role
+Labeling, etc. in a generalized sequence-to-sequence format opens up the
+opportunity to make the maximum utilization of large language model knowledge
+toward structured prediction. Unfortunately, this requires formatting them into
+specialized augmented format unknown to the base pretrained language model
+(PLMs) necessitating finetuning to the target format. This significantly bounds
+its usefulness in data-limited settings where finetuning large models cannot
+properly generalize to the target format. To address this challenge and
+leverage PLM knowledge effectively, we propose FISH-DIP, a sample-aware dynamic
+sparse finetuning strategy that selectively focuses on a fraction of
+parameters, informed by feedback from highly regressing examples, during the
+fine-tuning process. By leveraging the dynamism of sparsity, our approach
+mitigates the impact of well-learned samples and prioritizes underperforming
+instances for improvement in generalization. Across five tasks of sequence
+labeling, we demonstrate that FISH-DIP can smoothly optimize the model in low
+resource settings offering upto 40% performance improvements over full
+fine-tuning depending on target evaluation settings. Also, compared to
+in-context learning and other parameter-efficient fine-tuning approaches,
+FISH-DIP performs comparably or better, notably in extreme low-resource
+settings.
+"
+UL2: Unifying Language Learning Paradigms,Yi Tay,http://arxiv.org/pdf/2205.05131v3.pdf,2022-05-10,['cs.cl'],2205.05131v3.pdf,"  Existing pre-trained models are generally geared towards a particular class
+of problems. To date, there seems to be still no consensus on what the right
+architecture and pre-training setup should be. This paper presents a unified
+framework for pre-training models that are universally effective across
+datasets and setups. We begin by disentangling architectural archetypes with
+pre-training objectives -- two concepts that are commonly conflated. Next, we
+present a generalized & unified perspective for self-supervision in NLP and
+show how different pre-training objectives can be cast as one another and how
+interpolating between different objectives can be effective. We then propose
+Mixture-of-Denoisers (MoD), a pre-training objective that combines diverse
+pre-training paradigms together. We furthermore introduce a notion of mode
+switching, wherein downstream fine-tuning is associated with specific
+pre-training schemes. We conduct extensive ablative experiments to compare
+multiple pre-training objectives and find that our method pushes the
+Pareto-frontier by outperforming T5 & GPT-like models across multiple diverse
+setups. By scaling our model up to 20B parameters, we achieve SOTA performance
+on 50 well-established supervised finetuning based NLP tasks. Our model also
+achieve strong results at in-context learning, outperforming 175B GPT-3 on
+zero-shot SuperGLUE and tripling the performance of T5-XXL on one-shot
+summarization. On 0-shot MMLU, UL2 20B outperforms T0 and T5 models. UL2 20B
+also works well with chain-of-thought prompting and reasoning, making it an
+appealing choice for research into reasoning at a small to medium scale of 20B
+parameters. Finally, we apply FLAN instruction tuning to the UL2 20B model,
+achieving MMLU and Big-Bench scores competitive to FLAN-PaLM 62B. We release
+Flax-based T5X checkpoints for the UL2 20B & Flan-UL2 20B.
+"
+Human-Timescale Adaptation in an Open-Ended Task Space, Adaptive Agent Team,http://arxiv.org/pdf/2301.07608v1.pdf,2023-01-18,"['cs.lg', 'cs.ai', 'cs.ne']",2301.07608v1.pdf,"  Foundation models have shown impressive adaptation and scalability in
+supervised and self-supervised learning problems, but so far these successes
+have not fully translated to reinforcement learning (RL). In this work, we
+demonstrate that training an RL agent at scale leads to a general in-context
+learning algorithm that can adapt to open-ended novel embodied 3D problems as
+quickly as humans. In a vast space of held-out environment dynamics, our
+adaptive agent (AdA) displays on-the-fly hypothesis-driven exploration,
+efficient exploitation of acquired knowledge, and can successfully be prompted
+with first-person demonstrations. Adaptation emerges from three ingredients:
+(1) meta-reinforcement learning across a vast, smooth and diverse task
+distribution, (2) a policy parameterised as a large-scale attention-based
+memory architecture, and (3) an effective automated curriculum that prioritises
+tasks at the frontier of an agent's capabilities. We demonstrate characteristic
+scaling laws with respect to network size, memory length, and richness of the
+training task distribution. We believe our results lay the foundation for
+increasingly general and adaptive RL agents that perform well across
+ever-larger open-ended domains.
+"
+DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4,Zhengliang Liu,http://arxiv.org/pdf/2303.11032v1.pdf,2023-03-20,"['cs.cl', 'cs.cy']",2303.11032v1.pdf,"  The digitization of healthcare has facilitated the sharing and re-using of
+medical data but has also raised concerns about confidentiality and privacy.
+HIPAA (Health Insurance Portability and Accountability Act) mandates removing
+re-identifying information before the dissemination of medical records. Thus,
+effective and efficient solutions for de-identifying medical data, especially
+those in free-text forms, are highly needed. While various computer-assisted
+de-identification methods, including both rule-based and learning-based, have
+been developed and used in prior practice, such solutions still lack
+generalizability or need to be fine-tuned according to different scenarios,
+significantly imposing restrictions in wider use. The advancement of large
+language models (LLM), such as ChatGPT and GPT-4, have shown great potential in
+processing text data in the medical domain with zero-shot in-context learning,
+especially in the task of privacy protection, as these models can identify
+confidential information by their powerful named entity recognition (NER)
+capability. In this work, we developed a novel GPT4-enabled de-identification
+framework (""DeID-GPT"") to automatically identify and remove the identifying
+information. Compared to existing commonly used medical text data
+de-identification methods, our developed DeID-GPT showed the highest accuracy
+and remarkable reliability in masking private information from the unstructured
+medical text while preserving the original structure and meaning of the text.
+This study is one of the earliest to utilize ChatGPT and GPT-4 for medical text
+data processing and de-identification, which provides insights for further
+research and solution development on the use of LLMs such as ChatGPT/GPT-4 in
+healthcare. Codes and benchmarking data information are available at
+https://github.com/yhydhx/ChatGPT-API.
+"
+TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with  Millions of APIs,Yaobo Liang,http://arxiv.org/pdf/2303.16434v1.pdf,2023-03-29,"['cs.ai', 'cs.cl']",2303.16434v1.pdf,"  Artificial Intelligence (AI) has made incredible progress recently. On the
+one hand, advanced foundation models like ChatGPT can offer powerful
+conversation, in-context learning and code generation abilities on a broad
+range of open-domain tasks. They can also generate high-level solution outlines
+for domain-specific tasks based on the common sense knowledge they have
+acquired. However, they still face difficulties with some specialized tasks
+because they lack enough domain-specific data during pre-training or they often
+have errors in their neural network computations on those tasks that need
+accurate executions. On the other hand, there are also many existing models and
+systems (symbolic-based or neural-based) that can do some domain-specific tasks
+very well. However, due to the different implementation or working mechanisms,
+they are not easily accessible or compatible with foundation models. Therefore,
+there is a clear and pressing need for a mechanism that can leverage foundation
+models to propose task solution outlines and then automatically match some of
+the sub-tasks in the outlines to the off-the-shelf models and systems with
+special functionalities to complete them. Inspired by this, we introduce
+TaskMatrix.AI as a new AI ecosystem that connects foundation models with
+millions of APIs for task completion. Unlike most previous work that aimed to
+improve a single AI model, TaskMatrix.AI focuses more on using existing
+foundation models (as a brain-like central system) and APIs of other AI models
+and systems (as sub-task solvers) to achieve diversified tasks in both digital
+and physical domains. As a position paper, we will present our vision of how to
+build such an ecosystem, explain each key component, and use study cases to
+illustrate both the feasibility of this vision and the main challenges we need
+to address next.
+"
+Subject-driven Text-to-Image Generation via Apprenticeship Learning,Wenhu Chen,http://arxiv.org/pdf/2304.00186v5.pdf,2023-04-01,"['cs.cv', 'cs.ai']",2304.00186v5.pdf,"  Recent text-to-image generation models like DreamBooth have made remarkable
+progress in generating highly customized images of a target subject, by
+fine-tuning an ``expert model'' for a given subject from a few examples.
+However, this process is expensive, since a new expert model must be learned
+for each subject. In this paper, we present SuTI, a Subject-driven
+Text-to-Image generator that replaces subject-specific fine tuning with
+in-context learning. Given a few demonstrations of a new subject, SuTI can
+instantly generate novel renditions of the subject in different scenes, without
+any subject-specific optimization. SuTI is powered by apprenticeship learning,
+where a single apprentice model is learned from data generated by a massive
+number of subject-specific expert models. Specifically, we mine millions of
+image clusters from the Internet, each centered around a specific visual
+subject. We adopt these clusters to train a massive number of expert models,
+each specializing in a different subject. The apprentice model SuTI then learns
+to imitate the behavior of these fine-tuned experts. SuTI can generate
+high-quality and customized subject-specific images 20x faster than
+optimization-based SoTA methods. On the challenging DreamBench and
+DreamBench-v2, our human evaluation shows that SuTI significantly outperforms
+existing models like InstructPix2Pix, Textual Inversion, Imagic, Prompt2Prompt,
+Re-Imagen and DreamBooth, especially on the subject and text alignment aspects.
+"
+Large Language Models are Edge-Case Fuzzers: Testing Deep Learning  Libraries via FuzzGPT,Yinlin Deng,http://arxiv.org/pdf/2304.02014v1.pdf,2023-04-04,['cs.se'],2304.02014v1.pdf,"  Deep Learning (DL) library bugs affect downstream DL applications,
+emphasizing the need for reliable systems. Generating valid input programs for
+fuzzing DL libraries is challenging due to the need for satisfying both
+language syntax/semantics and constraints for constructing valid computational
+graphs. Recently, the TitanFuzz work demonstrates that modern Large Language
+Models (LLMs) can be directly leveraged to implicitly learn all the constraints
+to generate valid DL programs for fuzzing. However, LLMs tend to generate
+ordinary programs following similar patterns seen in their massive training
+corpora, while fuzzing favors unusual inputs that cover edge cases or are
+unlikely to be manually produced.
+  To fill this gap, this paper proposes FuzzGPT, the first technique to prime
+LLMs to synthesize unusual programs for fuzzing. FuzzGPT is built on the
+well-known hypothesis that historical bug-triggering programs may include
+rare/valuable code ingredients important for bug finding. Traditional
+techniques leveraging such historical information require intensive human
+efforts to design dedicated generators and ensure the validity of generated
+programs. FuzzGPT demonstrates that this process can be fully automated via the
+intrinsic capabilities of LLMs (including fine-tuning and in-context learning),
+while being generalizable and applicable to challenging domains. While FuzzGPT
+can be applied with different LLMs, this paper focuses on the powerful
+GPT-style models: Codex and CodeGen. Moreover, FuzzGPT also shows the potential
+of directly leveraging the instruct-following capability of the recent ChatGPT
+for effective fuzzing. Evaluation on two popular DL libraries (PyTorch and
+TensorFlow) shows that FuzzGPT can substantially outperform TitanFuzz,
+detecting 76 bugs, with 49 already confirmed as previously unknown bugs,
+including 11 high-priority bugs or security vulnerabilities.
+"
+ImpressionGPT: An Iterative Optimizing Framework for Radiology Report  Summarization with ChatGPT,Chong Ma,http://arxiv.org/pdf/2304.08448v2.pdf,2023-04-17,"['cs.cl', 'cs.ai']",2304.08448v2.pdf,"  The 'Impression' section of a radiology report is a critical basis for
+communication between radiologists and other physicians, and it is typically
+written by radiologists based on the 'Findings' section. However, writing
+numerous impressions can be laborious and error-prone for radiologists.
+Although recent studies have achieved promising results in automatic impression
+generation using large-scale medical text data for pre-training and fine-tuning
+pre-trained language models, such models often require substantial amounts of
+medical text data and have poor generalization performance. While large
+language models (LLMs) like ChatGPT have shown strong generalization
+capabilities and performance, their performance in specific domains, such as
+radiology, remains under-investigated and potentially limited. To address this
+limitation, we propose ImpressionGPT, which leverages the in-context learning
+capability of LLMs by constructing dynamic contexts using domain-specific,
+individualized data. This dynamic prompt approach enables the model to learn
+contextual knowledge from semantically similar examples from existing data.
+Additionally, we design an iterative optimization algorithm that performs
+automatic evaluation on the generated impression results and composes the
+corresponding instruction prompts to further optimize the model. The proposed
+ImpressionGPT model achieves state-of-the-art performance on both MIMIC-CXR and
+OpenI datasets without requiring additional training data or fine-tuning the
+LLMs. This work presents a paradigm for localizing LLMs that can be applied in
+a wide range of similar application scenarios, bridging the gap between
+general-purpose LLMs and the specific language processing needs of various
+domains.
+"
+NaturalSpeech 2: Latent Diffusion Models are Natural and Zero-Shot  Speech and Singing Synthesizers,Kai Shen,http://arxiv.org/pdf/2304.09116v3.pdf,2023-04-18,"['eess.as', 'cs.ai', 'cs.cl', 'cs.lg', 'cs.sd']",2304.09116v3.pdf,"  Scaling text-to-speech (TTS) to large-scale, multi-speaker, and in-the-wild
+datasets is important to capture the diversity in human speech such as speaker
+identities, prosodies, and styles (e.g., singing). Current large TTS systems
+usually quantize speech into discrete tokens and use language models to
+generate these tokens one by one, which suffer from unstable prosody, word
+skipping/repeating issue, and poor voice quality. In this paper, we develop
+NaturalSpeech 2, a TTS system that leverages a neural audio codec with residual
+vector quantizers to get the quantized latent vectors and uses a diffusion
+model to generate these latent vectors conditioned on text input. To enhance
+the zero-shot capability that is important to achieve diverse speech synthesis,
+we design a speech prompting mechanism to facilitate in-context learning in the
+diffusion model and the duration/pitch predictor. We scale NaturalSpeech 2 to
+large-scale datasets with 44K hours of speech and singing data and evaluate its
+voice quality on unseen speakers. NaturalSpeech 2 outperforms previous TTS
+systems by a large margin in terms of prosody/timbre similarity, robustness,
+and voice quality in a zero-shot setting, and performs novel zero-shot singing
+synthesis with only a speech prompt. Audio samples are available at
+https://speechresearch.github.io/naturalspeech2.
+"
+Improving Language Model Negotiation with Self-Play and In-Context  Learning from AI Feedback,Yao Fu,http://arxiv.org/pdf/2305.10142v1.pdf,2023-05-17,['cs.cl'],2305.10142v1.pdf,"  We study whether multiple large language models (LLMs) can autonomously
+improve each other in a negotiation game by playing, reflecting, and
+criticizing. We are interested in this question because if LLMs were able to
+improve each other, it would imply the possibility of creating strong AI agents
+with minimal human intervention. We ask two LLMs to negotiate with each other,
+playing the roles of a buyer and a seller, respectively. They aim to reach a
+deal with the buyer targeting a lower price and the seller a higher one. A
+third language model, playing the critic, provides feedback to a player to
+improve the player's negotiation strategies. We let the two agents play
+multiple rounds, using previous negotiation history and AI feedback as
+in-context demonstrations to improve the model's negotiation strategy
+iteratively. We use different LLMs (GPT and Claude) for different roles and use
+the deal price as the evaluation metric. Our experiments reveal multiple
+intriguing findings: (1) Only a subset of the language models we consider can
+self-play and improve the deal price from AI feedback, weaker models either do
+not understand the game's rules or cannot incorporate AI feedback for further
+improvement. (2) Models' abilities to learn from the feedback differ when
+playing different roles. For example, it is harder for Claude-instant to
+improve as the buyer than as the seller. (3) When unrolling the game to
+multiple rounds, stronger agents can consistently improve their performance by
+meaningfully using previous experiences and iterative AI feedback, yet have a
+higher risk of breaking the deal. We hope our work provides insightful initial
+explorations of having models autonomously improve each other with game playing
+and AI feedback.
+"
+XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented  Languages,Sebastian Ruder,http://arxiv.org/pdf/2305.11938v2.pdf,2023-05-19,['cs.cl'],2305.11938v2.pdf,"  Data scarcity is a crucial issue for the development of highly multilingual
+NLP systems. Yet for many under-represented languages (ULs) -- languages for
+which NLP re-search is particularly far behind in meeting user needs -- it is
+feasible to annotate small amounts of data. Motivated by this, we propose
+XTREME-UP, a benchmark defined by: its focus on the scarce-data scenario rather
+than zero-shot; its focus on user-centric tasks -- tasks with broad adoption by
+speakers of high-resource languages; and its focus on under-represented
+languages where this scarce-data scenario tends to be most realistic. XTREME-UP
+evaluates the capabilities of language models across 88 under-represented
+languages over 9 key user-centric technologies including ASR, OCR, MT, and
+information access tasks that are of general utility. We create new datasets
+for OCR, autocomplete, semantic parsing, and transliteration, and build on and
+refine existing datasets for other tasks. XTREME-UP provides methodology for
+evaluating many modeling scenarios including text-only, multi-modal (vision,
+audio, and text),supervised parameter tuning, and in-context learning. We
+evaluate commonly used models on the benchmark. We release all code and scripts
+to train and evaluate models
+"
+Memory-Efficient Fine-Tuning of Compressed Large Language Models via  sub-4-bit Integer Quantization,Jeonghoon Kim,http://arxiv.org/pdf/2305.14152v2.pdf,2023-05-23,"['cs.lg', 'cs.ai']",2305.14152v2.pdf,"  Large language models (LLMs) face the challenges in fine-tuning and
+deployment due to their high memory demands and computational costs. While
+parameter-efficient fine-tuning (PEFT) methods aim to reduce the memory usage
+of the optimizer state during fine-tuning, the inherent size of pre-trained LLM
+weights continues to be a pressing concern. Even though quantization techniques
+are widely proposed to ease memory demands and accelerate LLM inference, most
+of these techniques are geared towards the deployment phase. To bridge this
+gap, this paper presents Parameter-Efficient and Quantization-aware Adaptation
+(PEQA) - a simple yet effective method that combines the advantages of PEFT
+with quantized LLMs. By updating solely the quantization scales, PEQA can be
+directly applied to quantized LLMs, ensuring seamless task transitions.
+Parallel to existing PEFT methods, PEQA significantly reduces the memory
+overhead associated with the optimizer state. Furthermore, it leverages the
+advantages of quantization to substantially reduce model sizes. Even after
+fine-tuning, the quantization structure of a PEQA-tuned LLM remains intact,
+allowing for accelerated inference on the deployment stage. We employ
+PEQA-tuning for task-specific adaptation on LLMs with up to 65 billion
+parameters. To assess the logical reasoning and language comprehension of
+PEQA-tuned LLMs, we fine-tune low-bit quantized LLMs using a instruction
+dataset. Our results show that even when LLMs are quantized to below 4-bit
+precision, their capabilities in language modeling, few-shot in-context
+learning, and comprehension can be resiliently restored to (or even improved
+over) their full-precision original performances with PEQA.
+"
+PaLI-X: On Scaling up a Multilingual Vision and Language Model,Xi Chen,http://arxiv.org/pdf/2305.18565v1.pdf,2023-05-29,"['cs.cv', 'cs.cl', 'cs.lg']",2305.18565v1.pdf,"  We present the training recipe and results of scaling up PaLI-X, a
+multilingual vision and language model, both in terms of size of the components
+and the breadth of its training task mixture. Our model achieves new levels of
+performance on a wide-range of varied and complex tasks, including multiple
+image-based captioning and question-answering tasks, image-based document
+understanding and few-shot (in-context) learning, as well as object detection,
+video question answering, and video captioning. PaLI-X advances the
+state-of-the-art on most vision-and-language benchmarks considered (25+ of
+them). Finally, we observe emerging capabilities, such as complex counting and
+multilingual object detection, tasks that are not explicitly in the training
+mix.
+"
+"Revisiting Out-of-distribution Robustness in NLP: Benchmark, Analysis,  and LLMs Evaluations",Lifan Yuan,http://arxiv.org/pdf/2306.04618v2.pdf,2023-06-07,"['cs.cl', 'cs.cr', 'cs.lg']",2306.04618v2.pdf,"  This paper reexamines the research on out-of-distribution (OOD) robustness in
+the field of NLP. We find that the distribution shift settings in previous
+studies commonly lack adequate challenges, hindering the accurate evaluation of
+OOD robustness. To address these issues, we propose a benchmark construction
+protocol that ensures clear differentiation and challenging distribution
+shifts. Then we introduce BOSS, a Benchmark suite for Out-of-distribution
+robustneSS evaluation covering 5 tasks and 20 datasets. Based on BOSS, we
+conduct a series of experiments on pre-trained language models for analysis and
+evaluation of OOD robustness. First, for vanilla fine-tuning, we examine the
+relationship between in-distribution (ID) and OOD performance. We identify
+three typical types that unveil the inner learning mechanism, which could
+potentially facilitate the forecasting of OOD robustness, correlating with the
+advancements on ID datasets. Then, we evaluate 5 classic methods on BOSS and
+find that, despite exhibiting some effectiveness in specific cases, they do not
+offer significant improvement compared to vanilla fine-tuning. Further, we
+evaluate 5 LLMs with various adaptation paradigms and find that when sufficient
+ID data is available, fine-tuning domain-specific models outperform LLMs on ID
+examples significantly. However, in the case of OOD instances, prioritizing
+LLMs with in-context learning yields better results. We identify that both
+fine-tuned small models and LLMs face challenges in effectively addressing
+downstream tasks. The code is public at
+\url{https://github.com/lifan-yuan/OOD_NLP}.
+"
+Transformers as Statisticians: Provable In-Context Learning with  In-Context Algorithm Selection,Yu Bai,http://arxiv.org/pdf/2306.04637v2.pdf,2023-06-07,"['cs.lg', 'cs.ai', 'cs.cl', 'math.st', 'stat.ml', 'stat.th']",2306.04637v2.pdf,"  Neural sequence models based on the transformer architecture have
+demonstrated remarkable \emph{in-context learning} (ICL) abilities, where they
+can perform new tasks when prompted with training and test examples, without
+any parameter update to the model. This work first provides a comprehensive
+statistical theory for transformers to perform ICL. Concretely, we show that
+transformers can implement a broad class of standard machine learning
+algorithms in context, such as least squares, ridge regression, Lasso, learning
+generalized linear models, and gradient descent on two-layer neural networks,
+with near-optimal predictive power on various in-context data distributions.
+Using an efficient implementation of in-context gradient descent as the
+underlying mechanism, our transformer constructions admit mild size bounds, and
+can be learned with polynomially many pretraining sequences.
+  Building on these ``base'' ICL algorithms, intriguingly, we show that
+transformers can implement more complex ICL procedures involving
+\emph{in-context algorithm selection}, akin to what a statistician can do in
+real life -- A \emph{single} transformer can adaptively select different base
+ICL algorithms -- or even perform qualitatively different tasks -- on different
+input sequences, without any explicit prompting of the right algorithm or task.
+We both establish this in theory by explicit constructions, and also observe
+this phenomenon experimentally. In theory, we construct two general mechanisms
+for algorithm selection with concrete examples: pre-ICL testing, and post-ICL
+validation. As an example, we use the post-ICL validation mechanism to
+construct a transformer that can perform nearly Bayes-optimal ICL on a
+challenging task -- noisy linear models with mixed noise levels.
+Experimentally, we demonstrate the strong in-context algorithm selection
+capabilities of standard transformer architectures.
+"
+Instruction Tuned Models are Quick Learners,Himanshu Gupta,http://arxiv.org/pdf/2306.05539v1.pdf,2023-05-17,['cs.cl'],2306.05539v1.pdf,"  Instruction tuning of language models has demonstrated the ability to enhance
+model generalization to unseen tasks via in-context learning using a few
+examples. However, typical supervised learning still requires a plethora of
+downstream training data for finetuning. Often in real-world situations, there
+is a scarcity of data available for finetuning, falling somewhere between few
+shot inference and fully supervised finetuning. In this work, we demonstrate
+the sample efficiency of instruction tuned models over various tasks by
+estimating the minimal downstream training data required by them to perform
+transfer learning and match the performance of state-of-the-art (SOTA)
+supervised models. We conduct experiments on 119 tasks from Super Natural
+Instructions (SuperNI) in both the single task learning (STL) and multi task
+learning (MTL) settings. Our findings reveal that, in the STL setting,
+instruction tuned models equipped with 25% of the downstream train data surpass
+the SOTA performance on the downstream tasks. In the MTL setting, an
+instruction tuned model trained on only 6% of downstream training data achieve
+SOTA, while using 100% of the training data results in a 3.69% points
+improvement (ROUGE-L 74.68) over the previous SOTA. We conduct an analysis on
+T5 vs Tk-Instruct by developing several baselines to demonstrate that
+instruction tuning aids in increasing both sample efficiency and transfer
+learning. Additionally, we observe a consistent ~4% performance increase in
+both settings when pre-finetuning is performed with instructions. Finally, we
+conduct a categorical study and find that contrary to previous results, tasks
+in the question rewriting and title generation categories suffer from
+instruction tuning.
+"
+Synapse: Trajectory-as-Exemplar Prompting with Memory for Computer  Control,Longtao Zheng,http://arxiv.org/pdf/2306.07863v2.pdf,2023-06-13,['cs.ai'],2306.07863v2.pdf,"  Building agents using large language models (LLMs) to control computers is an
+emerging research field, where the agent perceives computer states and performs
+actions to accomplish complex tasks. Previous computer agents have demonstrated
+the benefits of in-context learning (ICL); however, their performance is
+hindered by several issues. First, the limited context length of LLMs and
+complex computer states restrict the number of exemplars, as a single webpage
+can consume the entire context. Second, the exemplars in current methods, such
+as high-level plans and multi-choice questions, cannot represent complete
+trajectories, leading to suboptimal performance in tasks that require many
+steps or repeated actions. Third, existing computer agents rely on
+task-specific exemplars and overlook the similarity among tasks, resulting in
+poor generalization to novel tasks. To address these challenges, we introduce
+Synapse, featuring three key components: i) state abstraction, which filters
+out task-irrelevant information from raw states, allowing more exemplars within
+the limited context, ii) trajectory-as-exemplar prompting, which prompts the
+LLM with complete trajectories of the abstracted states and actions for
+improved multi-step decision-making, and iii) exemplar memory, which stores the
+embeddings of exemplars and retrieves them via similarity search for
+generalization to novel tasks. We evaluate Synapse on MiniWoB++, a standard
+task suite, and Mind2Web, a real-world website benchmark. In MiniWoB++, Synapse
+achieves a 99.2% average success rate (a 10% relative improvement) across 64
+tasks using demonstrations from only 48 tasks. Notably, Synapse is the first
+ICL method to solve the book-flight task in MiniWoB++. Synapse also exhibits a
+53% relative improvement in average step success rate over the previous
+state-of-the-art prompting scheme in Mind2Web.
+"
+Language to Rewards for Robotic Skill Synthesis,Wenhao Yu,http://arxiv.org/pdf/2306.08647v2.pdf,2023-06-14,"['cs.ro', 'cs.ai', 'cs.lg']",2306.08647v2.pdf,"  Large language models (LLMs) have demonstrated exciting progress in acquiring
+diverse new capabilities through in-context learning, ranging from logical
+reasoning to code-writing. Robotics researchers have also explored using LLMs
+to advance the capabilities of robotic control. However, since low-level robot
+actions are hardware-dependent and underrepresented in LLM training corpora,
+existing efforts in applying LLMs to robotics have largely treated LLMs as
+semantic planners or relied on human-engineered control primitives to interface
+with the robot. On the other hand, reward functions are shown to be flexible
+representations that can be optimized for control policies to achieve diverse
+tasks, while their semantic richness makes them suitable to be specified by
+LLMs. In this work, we introduce a new paradigm that harnesses this realization
+by utilizing LLMs to define reward parameters that can be optimized and
+accomplish variety of robotic tasks. Using reward as the intermediate interface
+generated by LLMs, we can effectively bridge the gap between high-level
+language instructions or corrections to low-level robot actions. Meanwhile,
+combining this with a real-time optimizer, MuJoCo MPC, empowers an interactive
+behavior creation experience where users can immediately observe the results
+and provide feedback to the system. To systematically evaluate the performance
+of our proposed method, we designed a total of 17 tasks for a simulated
+quadruped robot and a dexterous manipulator robot. We demonstrate that our
+proposed method reliably tackles 90% of the designed tasks, while a baseline
+using primitive skills as the interface with Code-as-policies achieves 50% of
+the tasks. We further validated our method on a real robot arm where complex
+manipulation skills such as non-prehensile pushing emerge through our
+interactive system.
+"
+Trained Transformers Learn Linear Models In-Context,Ruiqi Zhang,http://arxiv.org/pdf/2306.09927v3.pdf,2023-06-16,"['stat.ml', 'cs.ai', 'cs.cl', 'cs.lg']",2306.09927v3.pdf,"  Attention-based neural networks such as transformers have demonstrated a
+remarkable ability to exhibit in-context learning (ICL): Given a short prompt
+sequence of tokens from an unseen task, they can formulate relevant per-token
+and next-token predictions without any parameter updates. By embedding a
+sequence of labeled training data and unlabeled test data as a prompt, this
+allows for transformers to behave like supervised learning algorithms. Indeed,
+recent work has shown that when training transformer architectures over random
+instances of linear regression problems, these models' predictions mimic those
+of ordinary least squares.
+  Towards understanding the mechanisms underlying this phenomenon, we
+investigate the dynamics of ICL in transformers with a single linear
+self-attention layer trained by gradient flow on linear regression tasks. We
+show that despite non-convexity, gradient flow with a suitable random
+initialization finds a global minimum of the objective function. At this global
+minimum, when given a test prompt of labeled examples from a new prediction
+task, the transformer achieves prediction error competitive with the best
+linear predictor over the test prompt distribution. We additionally
+characterize the robustness of the trained transformer to a variety of
+distribution shifts and show that although a number of shifts are tolerated,
+shifts in the covariate distribution of the prompts are not. Motivated by this,
+we consider a generalized ICL setting where the covariate distributions can
+vary across prompts. We show that although gradient flow succeeds at finding a
+global minimum in this setting, the trained transformer is still brittle under
+mild covariate shifts. We complement this finding with experiments on large,
+nonlinear transformer architectures which we show are more robust under
+covariate shifts.
+"
+HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide  Resolution,Eric Nguyen,http://arxiv.org/pdf/2306.15794v1.pdf,2023-06-27,"['cs.lg', 'q-bio.gn']",2306.15794v1.pdf,"  Genomic (DNA) sequences encode an enormous amount of information for gene
+regulation and protein synthesis. Similar to natural language models,
+researchers have proposed foundation models in genomics to learn generalizable
+features from unlabeled genome data that can then be fine-tuned for downstream
+tasks such as identifying regulatory elements. Due to the quadratic scaling of
+attention, previous Transformer-based genomic models have used 512 to 4k tokens
+as context (<0.001% of the human genome), significantly limiting the modeling
+of long-range interactions in DNA. In addition, these methods rely on
+tokenizers to aggregate meaningful DNA units, losing single nucleotide
+resolution where subtle genetic variations can completely alter protein
+function via single nucleotide polymorphisms (SNPs). Recently, Hyena, a large
+language model based on implicit convolutions was shown to match attention in
+quality while allowing longer context lengths and lower time complexity.
+Leveraging Hyenas new long-range capabilities, we present HyenaDNA, a genomic
+foundation model pretrained on the human reference genome with context lengths
+of up to 1 million tokens at the single nucleotide-level, an up to 500x
+increase over previous dense attention-based models. HyenaDNA scales
+sub-quadratically in sequence length (training up to 160x faster than
+Transformer), uses single nucleotide tokens, and has full global context at
+each layer. We explore what longer context enables - including the first use of
+in-context learning in genomics for simple adaptation to novel tasks without
+updating pretrained model weights. On fine-tuned benchmarks from the Nucleotide
+Transformer, HyenaDNA reaches state-of-the-art (SotA) on 12 of 17 datasets
+using a model with orders of magnitude less parameters and pretraining data. On
+the GenomicBenchmarks, HyenaDNA surpasses SotA on all 8 datasets on average by
++9 accuracy points.
+"
+Generative Type Inference for Python,Yun Peng,http://arxiv.org/pdf/2307.09163v1.pdf,2023-07-18,['cs.se'],2307.09163v1.pdf,"  Python is a popular dynamic programming language, evidenced by its ranking as
+the second most commonly used language on GitHub. However, its dynamic type
+system can lead to potential type errors, leading researchers to explore
+automatic type inference approaches for Python programs. The rule-based type
+inference approaches can ensure the accuracy of predicted variable types, but
+they suffer from low coverage problems. Supervised type inference approaches,
+while feature-agnostic, require large, high-quality annotated datasets and are
+limited to pre-defined types. As zero-shot approaches, the cloze-style
+approaches reformulate the type inference problem into a fill-in-the-blank
+problem. However, their performance is limited.
+  This paper introduces TypeGen, a few-shot generative type inference approach
+that incorporates static domain knowledge from static analysis. TypeGen creates
+chain-of-thought (COT) prompts by translating the type inference steps of
+static analysis into prompts based on the type dependency graphs (TDGs),
+enabling language models to learn from how static analysis infers types. By
+combining COT prompts with code slices and type hints, TypeGen constructs
+example prompts from human annotations. TypeGen only requires very few
+annotated examples to teach language models to generate similar COT prompts via
+in-context learning. Moreover, TypeGen enhances the interpretability of results
+through the use of the input-explanation-output strategy. Experiments show that
+TypeGen outperforms the best baseline Type4Py by 10.0% for argument type
+prediction and 22.5% in return value type prediction in terms of top-1 Exact
+Match by using only five examples. Furthermore, TypeGen achieves substantial
+improvements of 27% to 84% compared to the zero-shot performance of large
+language models with parameter sizes ranging from 1.3B to 175B in terms of
+top-1 Exact Match.
+"
+Hypothesis Search: Inductive Reasoning with Language Models,Ruocheng Wang,http://arxiv.org/pdf/2309.05660v1.pdf,2023-09-11,"['cs.lg', 'cs.ai', 'cs.cl']",2309.05660v1.pdf,"  Inductive reasoning is a core problem-solving capacity: humans can identify
+underlying principles from a few examples, which can then be robustly
+generalized to novel scenarios. Recent work has evaluated large language models
+(LLMs) on inductive reasoning tasks by directly prompting them yielding ""in
+context learning."" This can work well for straightforward inductive tasks, but
+performs very poorly on more complex tasks such as the Abstraction and
+Reasoning Corpus (ARC). In this work, we propose to improve the inductive
+reasoning ability of LLMs by generating explicit hypotheses at multiple levels
+of abstraction: we prompt the LLM to propose multiple abstract hypotheses about
+the problem, in natural language, then implement the natural language
+hypotheses as concrete Python programs. These programs can be directly verified
+by running on the observed examples and generalized to novel inputs. Because of
+the prohibitive cost of generation with state-of-the-art LLMs, we consider a
+middle step to filter the set of hypotheses that will be implemented into
+programs: we either ask the LLM to summarize into a smaller set of hypotheses,
+or ask human annotators to select a subset of the hypotheses. We verify our
+pipeline's effectiveness on the ARC visual inductive reasoning benchmark, its
+variant 1D-ARC, and string transformation dataset SyGuS. On a random 40-problem
+subset of ARC, our automated pipeline using LLM summaries achieves 27.5%
+accuracy, significantly outperforming the direct prompting baseline (accuracy
+of 12.5%). With the minimal human input of selecting from LLM-generated
+candidates, the performance is boosted to 37.5%. (And we argue this is a lower
+bound on the performance of our approach without filtering.) Our ablation
+studies show that abstract hypothesis generation and concrete program
+representations are both beneficial for LLMs to perform inductive reasoning
+tasks.
+"
+How FaR Are Large Language Models From Agents with Theory-of-Mind?,Pei Zhou,http://arxiv.org/pdf/2310.03051v1.pdf,2023-10-04,"['cs.cl', 'cs.ai']",2310.03051v1.pdf,"  ""Thinking is for Doing."" Humans can infer other people's mental states from
+observations--an ability called Theory-of-Mind (ToM)--and subsequently act
+pragmatically on those inferences. Existing question answering benchmarks such
+as ToMi ask models questions to make inferences about beliefs of characters in
+a story, but do not test whether models can then use these inferences to guide
+their actions. We propose a new evaluation paradigm for large language models
+(LLMs): Thinking for Doing (T4D), which requires models to connect inferences
+about others' mental states to actions in social scenarios. Experiments on T4D
+demonstrate that LLMs such as GPT-4 and PaLM 2 seemingly excel at tracking
+characters' beliefs in stories, but they struggle to translate this capability
+into strategic action. Our analysis reveals the core challenge for LLMs lies in
+identifying the implicit inferences about mental states without being
+explicitly asked about as in ToMi, that lead to choosing the correct action in
+T4D. To bridge this gap, we introduce a zero-shot prompting framework, Foresee
+and Reflect (FaR), which provides a reasoning structure that encourages LLMs to
+anticipate future challenges and reason about potential actions. FaR boosts
+GPT-4's performance from 50% to 71% on T4D, outperforming other prompting
+methods such as Chain-of-Thought and Self-Ask. Moreover, FaR generalizes to
+diverse out-of-distribution story structures and scenarios that also require
+ToM inferences to choose an action, consistently outperforming other methods
+including few-shot in-context learning.
+"
+Entity Matching using Large Language Models,Ralph Peeters,http://arxiv.org/pdf/2310.11244v1.pdf,2023-10-17,"['cs.cl', 'cs.lg']",2310.11244v1.pdf,"  Entity Matching is the task of deciding whether two entity descriptions refer
+to the same real-world entity. Entity Matching is a central step in most data
+integration pipelines and an enabler for many e-commerce applications which
+require to match products offers from different vendors. State-of-the-art
+entity matching methods often rely on pre-trained language models (PLMs) such
+as BERT or RoBERTa. Two major drawbacks of these models for entity matching are
+that (i) the models require significant amounts of task-specific training data
+and (ii) the fine-tuned models are not robust concerning out-of-distribution
+entities. In this paper, we investigate using large language models (LLMs) for
+entity matching as a less domain-specific training data reliant and more robust
+alternative to PLM-based matchers. Our study covers hosted LLMs, such as GPT3.5
+and GPT4, as well as open source LLMs based on Llama2 which can be run locally.
+We evaluate these models in a zero-shot scenario as well as a scenario where
+task-specific training data is available. We compare different prompt designs
+as well as the prompt sensitivity of the models in the zero-shot scenario. We
+investigate (i) the selection of in-context demonstrations, (ii) the generation
+of matching rules, as well as (iii) fine-tuning GPT3.5 in the second scenario
+using the same pool of training data across the different approaches. Our
+experiments show that GPT4 without any task-specific training data outperforms
+fine-tuned PLMs (RoBERTa and Ditto) on three out of five benchmark datasets
+reaching F1 scores around 90%. The experiments with in-context learning and
+rule generation show that all models beside of GPT4 benefit from these
+techniques (on average 5.9% and 2.2% F1), while GPT4 does not need such
+additional guidance in most cases...
+"
+CycleAlign: Iterative Distillation from Black-box LLM to White-box  Models for Better Human Alignment,Jixiang Hong,http://arxiv.org/pdf/2310.16271v1.pdf,2023-10-25,"['cs.cl', 'cs.ai']",2310.16271v1.pdf,"  Language models trained on large-scale corpus often generate content that is
+harmful, toxic, or contrary to human preferences, making their alignment with
+human values a critical concern. Reinforcement learning from human feedback
+(RLHF) with algorithms like PPO is a prevalent approach for alignment but is
+often complex, unstable, and resource-intensive. Recently, ranking-based
+alignment methods have emerged, offering stability and effectiveness by
+replacing the RL framework with supervised fine-tuning, but they are costly due
+to the need for annotated data. Considering that existing large language models
+(LLMs) like ChatGPT are already relatively well-aligned and cost-friendly,
+researchers have begun to align the language model with human preference from
+AI feedback. The common practices, which unidirectionally distill the
+instruction-following responses from LLMs, are constrained by their bottleneck.
+Thus we introduce CycleAlign to distill alignment capabilities from
+parameter-invisible LLMs (black-box) to a parameter-visible model (white-box)
+in an iterative manner. With in-context learning (ICL) as the core of the
+cycle, the black-box models are able to rank the model-generated responses
+guided by human-craft instruction and demonstrations about their preferences.
+During iterative interaction, the white-box models also have a judgment about
+responses generated by them. Consequently, the agreement ranking could be
+viewed as a pseudo label to dynamically update the in-context demonstrations
+and improve the preference ranking ability of black-box models. Through
+multiple interactions, the CycleAlign framework could align the white-box model
+with the black-box model effectively in a low-resource way. Empirical results
+illustrate that the model fine-tuned by CycleAlign remarkably exceeds existing
+methods, and achieves the state-of-the-art performance in alignment with human
+value.
+"
+Transformers are Efficient In-Context Estimators for Wireless  Communication,Vicram Rajagopalan,http://arxiv.org/pdf/2311.00226v1.pdf,2023-11-01,"['eess.sp', 'cs.lg']",2311.00226v1.pdf,"  Pre-trained transformers can perform in-context learning, where they adapt to
+a new task using only a small number of prompts without any explicit model
+optimization. Inspired by this attribute, we propose a novel approach, called
+in-context estimation, for the canonical communication problem of estimating
+transmitted symbols from received symbols. A communication channel is
+essentially a noisy function that maps transmitted symbols to received symbols,
+and this function can be represented by an unknown parameter whose statistics
+depend on an (also unknown) latent context. Conventional approaches ignore this
+hierarchical structure and simply attempt to use known transmissions, called
+pilots, to perform a least-squares estimate of the channel parameter, which is
+then used to estimate successive, unknown transmitted symbols. We make the
+basic connection that transformers show excellent contextual sequence
+completion with a few prompts, and so they should be able to implicitly
+determine the latent context from pilot symbols to perform end-to-end
+in-context estimation of transmitted symbols. Furthermore, the transformer
+should use information efficiently, i.e., it should utilize any pilots received
+to attain the best possible symbol estimates. Through extensive simulations, we
+show that in-context estimation not only significantly outperforms standard
+approaches, but also achieves the same performance as an estimator with perfect
+knowledge of the latent context within a few context examples. Thus, we make a
+strong case that transformers are efficient in-context estimators in the
+communication setting.
+"
+Multimodal Prompt Learning for Product Title Generation with Extremely  Limited Labels,Bang Yang,http://arxiv.org/pdf/2307.01969v1.pdf,2023-07-05,['cs.cv'],2307.01969v1.pdf,"  Generating an informative and attractive title for the product is a crucial
+task for e-commerce. Most existing works follow the standard multimodal natural
+language generation approaches, e.g., image captioning, and employ the large
+scale of human-labelled datasets to train desirable models. However, for novel
+products, especially in a different domain, there are few existing labelled
+data. In this paper, we propose a prompt-based approach, i.e., the Multimodal
+Prompt Learning framework, to accurately and efficiently generate titles for
+novel products with limited labels. We observe that the core challenges of
+novel product title generation are the understanding of novel product
+characteristics and the generation of titles in a novel writing style. To this
+end, we build a set of multimodal prompts from different modalities to preserve
+the corresponding characteristics and writing styles of novel products. As a
+result, with extremely limited labels for training, the proposed method can
+retrieve the multimodal prompts to generate desirable titles for novel
+products. The experiments and analyses are conducted on five novel product
+categories under both the in-domain and out-of-domain experimental settings.
+The results show that, with only 1% of downstream labelled data for training,
+our proposed approach achieves the best few-shot results and even achieves
+competitive results with fully-supervised methods trained on 100% of training
+data; With the full labelled data for training, our method achieves
+state-of-the-art results.
+"
+Few-shot Joint Multimodal Aspect-Sentiment Analysis Based on Generative  Multimodal Prompt,Xiaocui Yang,http://arxiv.org/pdf/2305.10169v2.pdf,2023-05-17,['cs.mm'],2305.10169v2.pdf,"  We have witnessed the rapid proliferation of multimodal data on numerous
+social media platforms. Conventional studies typically require massive labeled
+data to train models for Multimodal Aspect-Based Sentiment Analysis (MABSA).
+However, collecting and annotating fine-grained multimodal data for MABSA is
+tough. To alleviate the above issue, we perform three MABSA-related tasks with
+quite a small number of labeled multimodal samples. We first build diverse and
+comprehensive multimodal few-shot datasets according to the data distribution.
+To capture the specific prompt for each aspect term in a few-shot scenario, we
+propose a novel Generative Multimodal Prompt (GMP) model for MABSA, which
+includes the Multimodal Encoder module and the N-Stream Decoders module. We
+further introduce a subtask to predict the number of aspect terms in each
+instance to construct the multimodal prompt. Extensive experiments on two
+datasets demonstrate that our approach outperforms strong baselines on two
+MABSA-related tasks in the few-shot setting.
+"
+VIMA: General Robot Manipulation with Multimodal Prompts,Yunfan Jiang,http://arxiv.org/pdf/2210.03094v2.pdf,2022-10-06,"['cs.ro', 'cs.ai', 'cs.lg']",2210.03094v2.pdf,"  Prompt-based learning has emerged as a successful paradigm in natural
+language processing, where a single general-purpose language model can be
+instructed to perform any task specified by input prompts. Yet task
+specification in robotics comes in various forms, such as imitating one-shot
+demonstrations, following language instructions, and reaching visual goals.
+They are often considered different tasks and tackled by specialized models. We
+show that a wide spectrum of robot manipulation tasks can be expressed with
+multimodal prompts, interleaving textual and visual tokens. Accordingly, we
+develop a new simulation benchmark that consists of thousands of
+procedurally-generated tabletop tasks with multimodal prompts, 600K+ expert
+trajectories for imitation learning, and a four-level evaluation protocol for
+systematic generalization. We design a transformer-based robot agent, VIMA,
+that processes these prompts and outputs motor actions autoregressively. VIMA
+features a recipe that achieves strong model scalability and data efficiency.
+It outperforms alternative designs in the hardest zero-shot generalization
+setting by up to $2.9\times$ task success rate given the same training data.
+With $10\times$ less training data, VIMA still performs $2.7\times$ better than
+the best competing variant. Code and video demos are available at
+https://vimalabs.github.io/
+"
+Delving into Multimodal Prompting for Fine-grained Visual Classification,Xin Jiang,http://arxiv.org/pdf/2309.08912v1.pdf,2023-09-16,"['cs.cv', 'cs.mm']",2309.08912v1.pdf,"  Fine-grained visual classification (FGVC) involves categorizing fine
+subdivisions within a broader category, which poses challenges due to subtle
+inter-class discrepancies and large intra-class variations. However, prevailing
+approaches primarily focus on uni-modal visual concepts. Recent advancements in
+pre-trained vision-language models have demonstrated remarkable performance in
+various high-level vision tasks, yet the applicability of such models to FGVC
+tasks remains uncertain. In this paper, we aim to fully exploit the
+capabilities of cross-modal description to tackle FGVC tasks and propose a
+novel multimodal prompting solution, denoted as MP-FGVC, based on the
+contrastive language-image pertaining (CLIP) model. Our MP-FGVC comprises a
+multimodal prompts scheme and a multimodal adaptation scheme. The former
+includes Subcategory-specific Vision Prompt (SsVP) and Discrepancy-aware Text
+Prompt (DaTP), which explicitly highlights the subcategory-specific
+discrepancies from the perspectives of both vision and language. The latter
+aligns the vision and text prompting elements in a common semantic space,
+facilitating cross-modal collaborative reasoning through a Vision-Language
+Fusion Module (VLFM) for further improvement on FGVC. Moreover, we tailor a
+two-stage optimization strategy for MP-FGVC to fully leverage the pre-trained
+CLIP model and expedite efficient adaptation for FGVC. Extensive experiments
+conducted on four FGVC datasets demonstrate the effectiveness of our MP-FGVC.
+"
+Multimodal Prompt Transformer with Hybrid Contrastive Learning for  Emotion Recognition in Conversation,Shihao Zou,http://arxiv.org/pdf/2310.04456v1.pdf,2023-10-04,"['cs.cl', 'cs.sd', 'eess.as']",2310.04456v1.pdf,"  Emotion Recognition in Conversation (ERC) plays an important role in driving
+the development of human-machine interaction. Emotions can exist in multiple
+modalities, and multimodal ERC mainly faces two problems: (1) the noise problem
+in the cross-modal information fusion process, and (2) the prediction problem
+of less sample emotion labels that are semantically similar but different
+categories. To address these issues and fully utilize the features of each
+modality, we adopted the following strategies: first, deep emotion cues
+extraction was performed on modalities with strong representation ability, and
+feature filters were designed as multimodal prompt information for modalities
+with weak representation ability. Then, we designed a Multimodal Prompt
+Transformer (MPT) to perform cross-modal information fusion. MPT embeds
+multimodal fusion information into each attention layer of the Transformer,
+allowing prompt information to participate in encoding textual features and
+being fused with multi-level textual information to obtain better multimodal
+fusion features. Finally, we used the Hybrid Contrastive Learning (HCL)
+strategy to optimize the model's ability to handle labels with few samples.
+This strategy uses unsupervised contrastive learning to improve the
+representation ability of multimodal fusion and supervised contrastive learning
+to mine the information of labels with few samples. Experimental results show
+that our proposed model outperforms state-of-the-art models in ERC on two
+benchmark datasets.
+"
+2nd Place Winning Solution for the CVPR2023 Visual Anomaly and Novelty  Detection Challenge: Multimodal Prompting for Data-centric Anomaly Detection,Yunkang Cao,http://arxiv.org/pdf/2306.09067v2.pdf,2023-06-15,['cs.cv'],2306.09067v2.pdf,"  This technical report introduces the winning solution of the team Segment Any
+Anomaly for the CVPR2023 Visual Anomaly and Novelty Detection (VAND) challenge.
+Going beyond uni-modal prompt, e.g., language prompt, we present a novel
+framework, i.e., Segment Any Anomaly + (SAA$+$), for zero-shot anomaly
+segmentation with multi-modal prompts for the regularization of cascaded modern
+foundation models. Inspired by the great zero-shot generalization ability of
+foundation models like Segment Anything, we first explore their assembly (SAA)
+to leverage diverse multi-modal prior knowledge for anomaly localization.
+Subsequently, we further introduce multimodal prompts (SAA$+$) derived from
+domain expert knowledge and target image context to enable the non-parameter
+adaptation of foundation models to anomaly segmentation. The proposed SAA$+$
+model achieves state-of-the-art performance on several anomaly segmentation
+benchmarks, including VisA and MVTec-AD, in the zero-shot setting. We will
+release the code of our winning solution for the CVPR2023 VAN.
+"
+Multimodal Prompt Retrieval for Generative Visual Question Answering,Timothy Ossowski,http://arxiv.org/pdf/2306.17675v1.pdf,2023-06-30,"['cs.cv', 'cs.ai']",2306.17675v1.pdf,"  Recent years have witnessed impressive results of pre-trained vision-language
+models on knowledge-intensive tasks such as visual question answering (VQA).
+Despite the recent advances in VQA, existing methods mainly adopt a
+discriminative formulation that predicts answers within a pre-defined label
+set, leading to easy overfitting on low-resource domains with limited labeled
+data (e.g., medicine) and poor generalization under domain shift to another
+dataset. To tackle this limitation, we propose a novel generative model
+enhanced by multimodal prompt retrieval (MPR) that integrates retrieved prompts
+and multimodal features to generate answers in free text. Our generative model
+enables rapid zero-shot dataset adaptation to unseen data distributions and
+open-set answer labels across datasets. Our experiments on medical VQA tasks
+show that MPR outperforms its non-retrieval counterpart by up to 30% accuracy
+points in a few-shot domain adaptation setting.
+"
+Zero-Shot and Few-Shot Video Question Answering with Multi-Modal Prompts,Deniz Engin,http://arxiv.org/pdf/2309.15915v1.pdf,2023-09-27,['cs.cv'],2309.15915v1.pdf,"  Recent vision-language models are driven by large-scale pretrained models.
+However, adapting pretrained models on limited data presents challenges such as
+overfitting, catastrophic forgetting, and the cross-modal gap between vision
+and language. We introduce a parameter-efficient method to address these
+challenges, combining multimodal prompt learning and a transformer-based
+mapping network, while keeping the pretrained models frozen. Our experiments on
+several video question answering benchmarks demonstrate the superiority of our
+approach in terms of performance and parameter efficiency on both zero-shot and
+few-shot settings. Our code is available at https://engindeniz.github.io/vitis.
+"
+Vita-CLIP: Video and text adaptive CLIP via Multimodal Prompting,Syed Talal Wasim,http://arxiv.org/pdf/2304.03307v1.pdf,2023-04-06,"['cs.cv', 'eess.iv']",2304.03307v1.pdf,"  Adopting contrastive image-text pretrained models like CLIP towards video
+classification has gained attention due to its cost-effectiveness and
+competitive performance. However, recent works in this area face a trade-off.
+Finetuning the pretrained model to achieve strong supervised performance
+results in low zero-shot generalization. Similarly, freezing the backbone to
+retain zero-shot capability causes significant drop in supervised accuracy.
+Because of this, recent works in literature typically train separate models for
+supervised and zero-shot action recognition. In this work, we propose a
+multimodal prompt learning scheme that works to balance the supervised and
+zero-shot performance under a single unified training. Our prompting approach
+on the vision side caters for three aspects: 1) Global video-level prompts to
+model the data distribution; 2) Local frame-level prompts to provide per-frame
+discriminative conditioning; and 3) a summary prompt to extract a condensed
+video representation. Additionally, we define a prompting scheme on the text
+side to augment the textual context. Through this prompting scheme, we can
+achieve state-of-the-art zero-shot performance on Kinetics-600, HMDB51 and
+UCF101 while remaining competitive in the supervised setting. By keeping the
+pretrained backbone frozen, we optimize a much lower number of parameters and
+retain the existing general representation which helps achieve the strong
+zero-shot performance. Our codes/models are released at
+https://github.com/TalalWasim/Vita-CLIP.
+"
+Similarity-Aware Multimodal Prompt Learning for Fake News Detection,Ye Jiang,http://arxiv.org/pdf/2304.04187v3.pdf,2023-04-09,['cs.cl'],2304.04187v3.pdf,"  The standard paradigm for fake news detection mainly utilizes text
+information to model the truthfulness of news. However, the discourse of online
+fake news is typically subtle and it requires expert knowledge to use textual
+information to debunk fake news. Recently, studies focusing on multimodal fake
+news detection have outperformed text-only methods. Recent approaches utilizing
+the pre-trained model to extract unimodal features, or fine-tuning the
+pre-trained model directly, have become a new paradigm for detecting fake news.
+Again, this paradigm either requires a large number of training instances, or
+updates the entire set of pre-trained model parameters, making real-world fake
+news detection impractical. Furthermore, traditional multimodal methods fuse
+the cross-modal features directly without considering that the uncorrelated
+semantic representation might inject noise into the multimodal features. This
+paper proposes a Similarity-Aware Multimodal Prompt Learning (SAMPLE)
+framework. First, we incorporate prompt learning into multimodal fake news
+detection. Prompt learning, which only tunes prompts with a frozen language
+model, can reduce memory usage significantly and achieve comparable
+performances, compared with fine-tuning. We analyse three prompt templates with
+a soft verbalizer to detect fake news. In addition, we introduce the
+similarity-aware fusing method to adaptively fuse the intensity of multimodal
+representation and mitigate the noise injection via uncorrelated cross-modal
+features. For evaluation, SAMPLE surpasses the F1 and the accuracies of
+previous works on two benchmark multimodal datasets, demonstrating the
+effectiveness of the proposed method in detecting fake news. In addition,
+SAMPLE also is superior to other approaches regardless of few-shot and
+data-rich settings.
+"
+Draw Your Art Dream: Diverse Digital Art Synthesis with Multimodal  Guided Diffusion,Nisha Huang,http://arxiv.org/pdf/2209.13360v2.pdf,2022-09-27,['cs.cv'],2209.13360v2.pdf,"  Digital art synthesis is receiving increasing attention in the multimedia
+community because of engaging the public with art effectively. Current digital
+art synthesis methods usually use single-modality inputs as guidance, thereby
+limiting the expressiveness of the model and the diversity of generated
+results. To solve this problem, we propose the multimodal guided artwork
+diffusion (MGAD) model, which is a diffusion-based digital artwork generation
+approach that utilizes multimodal prompts as guidance to control the
+classifier-free diffusion model. Additionally, the contrastive language-image
+pretraining (CLIP) model is used to unify text and image modalities. Extensive
+experimental results on the quality and quantity of the generated digital art
+paintings confirm the effectiveness of the combination of the diffusion model
+and multimodal guidance. Code is available at
+https://github.com/haha-lisa/MGAD-multimodal-guided-artwork-diffusion.
+"
+Multimodal Prompting with Missing Modalities for Visual Recognition,Yi-Lun Lee,http://arxiv.org/pdf/2303.03369v2.pdf,2023-03-06,['cs.cv'],2303.03369v2.pdf,"  In this paper, we tackle two challenges in multimodal learning for visual
+recognition: 1) when missing-modality occurs either during training or testing
+in real-world situations; and 2) when the computation resources are not
+available to finetune on heavy transformer models. To this end, we propose to
+utilize prompt learning and mitigate the above two challenges together.
+Specifically, our modality-missing-aware prompts can be plugged into multimodal
+transformers to handle general missing-modality cases, while only requiring
+less than 1% learnable parameters compared to training the entire model. We
+further explore the effect of different prompt configurations and analyze the
+robustness to missing modality. Extensive experiments are conducted to show the
+effectiveness of our prompt learning framework that improves the performance
+under various missing-modality cases, while alleviating the requirement of
+heavy model re-training. Code is available.
+"
+Audio Visual Language Maps for Robot Navigation,Chenguang Huang,http://arxiv.org/pdf/2303.07522v2.pdf,2023-03-13,"['cs.ro', 'cs.ai', 'cs.cl', 'cs.cv', 'cs.lg']",2303.07522v2.pdf,"  While interacting in the world is a multi-sensory experience, many robots
+continue to predominantly rely on visual perception to map and navigate in
+their environments. In this work, we propose Audio-Visual-Language Maps
+(AVLMaps), a unified 3D spatial map representation for storing cross-modal
+information from audio, visual, and language cues. AVLMaps integrate the
+open-vocabulary capabilities of multimodal foundation models pre-trained on
+Internet-scale data by fusing their features into a centralized 3D voxel grid.
+In the context of navigation, we show that AVLMaps enable robot systems to
+index goals in the map based on multimodal queries, e.g., textual descriptions,
+images, or audio snippets of landmarks. In particular, the addition of audio
+information enables robots to more reliably disambiguate goal locations.
+Extensive experiments in simulation show that AVLMaps enable zero-shot
+multimodal goal navigation from multimodal prompts and provide 50% better
+recall in ambiguous scenarios. These capabilities extend to mobile robots in
+the real world - navigating to landmarks referring to visual, audio, and
+spatial concepts. Videos and code are available at: https://avlmaps.github.io.
+"
+Multitask Multimodal Prompted Training for Interactive Embodied Task  Completion,Georgios Pantazopoulos,http://arxiv.org/pdf/2311.04067v1.pdf,2023-11-07,"['cs.lg', 'cs.ai', 'cs.cv']",2311.04067v1.pdf,"  Interactive and embodied tasks pose at least two fundamental challenges to
+existing Vision & Language (VL) models, including 1) grounding language in
+trajectories of actions and observations, and 2) referential disambiguation. To
+tackle these challenges, we propose an Embodied MultiModal Agent (EMMA): a
+unified encoder-decoder model that reasons over images and trajectories, and
+casts action prediction as multimodal text generation. By unifying all tasks as
+text generation, EMMA learns a language of actions which facilitates transfer
+across tasks. Different to previous modular approaches with independently
+trained components, we use a single multitask model where each task contributes
+to goal completion. EMMA performs on par with similar models on several VL
+benchmarks and sets a new state-of-the-art performance (36.81% success rate) on
+the Dialog-guided Task Completion (DTC), a benchmark to evaluate dialog-guided
+agents in the Alexa Arena
+"
+MaPLe: Multi-modal Prompt Learning,Muhammad Uzair Khattak,http://arxiv.org/pdf/2210.03117v3.pdf,2022-10-06,['cs.cv'],2210.03117v3.pdf,"  Pre-trained vision-language (V-L) models such as CLIP have shown excellent
+generalization ability to downstream tasks. However, they are sensitive to the
+choice of input text prompts and require careful selection of prompt templates
+to perform well. Inspired by the Natural Language Processing (NLP) literature,
+recent CLIP adaptation approaches learn prompts as the textual inputs to
+fine-tune CLIP for downstream tasks. We note that using prompting to adapt
+representations in a single branch of CLIP (language or vision) is sub-optimal
+since it does not allow the flexibility to dynamically adjust both
+representation spaces on a downstream task. In this work, we propose
+Multi-modal Prompt Learning (MaPLe) for both vision and language branches to
+improve alignment between the vision and language representations. Our design
+promotes strong coupling between the vision-language prompts to ensure mutual
+synergy and discourages learning independent uni-modal solutions. Further, we
+learn separate prompts across different early stages to progressively model the
+stage-wise feature relationships to allow rich context learning. We evaluate
+the effectiveness of our approach on three representative tasks of
+generalization to novel classes, new target datasets and unseen domain shifts.
+Compared with the state-of-the-art method Co-CoOp, MaPLe exhibits favorable
+performance and achieves an absolute gain of 3.45% on novel classes and 2.72%
+on overall harmonic-mean, averaged over 11 diverse image recognition datasets.
+Our code and pre-trained models are available at
+https://github.com/muzairkhattak/multimodal-prompt-learning.
+"
+Few-shot Multimodal Sentiment Analysis based on Multimodal Probabilistic  Fusion Prompts,Xiaocui Yang,http://arxiv.org/pdf/2211.06607v2.pdf,2022-11-12,"['cs.cl', 'cs.mm']",2211.06607v2.pdf,"  Multimodal sentiment analysis has gained significant attention due to the
+proliferation of multimodal content on social media. However, existing studies
+in this area rely heavily on large-scale supervised data, which is
+time-consuming and labor-intensive to collect. Thus, there is a need to address
+the challenge of few-shot multimodal sentiment analysis. To tackle this
+problem, we propose a novel method called Multimodal Probabilistic Fusion
+Prompts (MultiPoint) that leverages diverse cues from different modalities for
+multimodal sentiment detection in the few-shot scenario. Specifically, we start
+by introducing a Consistently Distributed Sampling approach called CDS, which
+ensures that the few-shot dataset has the same category distribution as the
+full dataset. Unlike previous approaches primarily using prompts based on the
+text modality, we design unified multimodal prompts to reduce discrepancies
+between different modalities and dynamically incorporate multimodal
+demonstrations into the context of each multimodal instance. To enhance the
+model's robustness, we introduce a probabilistic fusion method to fuse output
+predictions from multiple diverse prompts for each input. Our extensive
+experiments on six datasets demonstrate the effectiveness of our approach.
+First, our method outperforms strong baselines in the multimodal few-shot
+setting. Furthermore, under the same amount of data (1% of the full dataset),
+our CDS-based experimental results significantly outperform those based on
+previously sampled datasets constructed from the same number of instances of
+each class.
+"
+Multimodal Garment Designer: Human-Centric Latent Diffusion Models for  Fashion Image Editing,Alberto Baldrati,http://arxiv.org/pdf/2304.02051v2.pdf,2023-04-04,"['cs.cv', 'cs.ai', 'cs.mm']",2304.02051v2.pdf,"  Fashion illustration is used by designers to communicate their vision and to
+bring the design idea from conceptualization to realization, showing how
+clothes interact with the human body. In this context, computer vision can thus
+be used to improve the fashion design process. Differently from previous works
+that mainly focused on the virtual try-on of garments, we propose the task of
+multimodal-conditioned fashion image editing, guiding the generation of
+human-centric fashion images by following multimodal prompts, such as text,
+human body poses, and garment sketches. We tackle this problem by proposing a
+new architecture based on latent diffusion models, an approach that has not
+been used before in the fashion domain. Given the lack of existing datasets
+suitable for the task, we also extend two existing fashion datasets, namely
+Dress Code and VITON-HD, with multimodal annotations collected in a
+semi-automatic manner. Experimental results on these new datasets demonstrate
+the effectiveness of our proposal, both in terms of realism and coherence with
+the given multimodal inputs. Source code and collected multimodal annotations
+are publicly available at:
+https://github.com/aimagelab/multimodal-garment-designer.
+"
+Parameter-efficient Tuning of Large-scale Multimodal Foundation Model,Haixin Wang,http://arxiv.org/pdf/2305.08381v3.pdf,2023-05-15,['cs.cv'],2305.08381v3.pdf,"  Driven by the progress of large-scale pre-training, parameter-efficient
+transfer learning has gained immense popularity across different subfields of
+Artificial Intelligence. The core is to adapt the model to downstream tasks
+with only a small set of parameters. Recently, researchers have leveraged such
+proven techniques in multimodal tasks and achieve promising results. However,
+two critical issues remain unresolved: how to further reduce the complexity
+with lightweight design and how to boost alignment between modalities under
+extremely low parameters. In this paper, we propose A graceful prompt framework
+for cross-modal transfer (Aurora) to overcome these challenges. Considering the
+redundancy in existing architectures, we first utilize the mode approximation
+to generate 0.1M trainable parameters to implement the multimodal prompt
+tuning, which explores the low intrinsic dimension with only 0.04% parameters
+of the pre-trained model. Then, for better modality alignment, we propose the
+Informative Context Enhancement and Gated Query Transformation module under
+extremely few parameters scenes. A thorough evaluation on six cross-modal
+benchmarks shows that it not only outperforms the state-of-the-art but even
+outperforms the full fine-tuning approach. Our code is available at:
+https://github.com/WillDreamer/Aurora.
+"
+RM-PRT: Realistic Robotic Manipulation Simulator and Benchmark with  Progressive Reasoning Tasks,Pengzhen Ren,http://arxiv.org/pdf/2306.11335v2.pdf,2023-06-20,"['cs.ro', 'cs.ai', 'cs.cv', 'cs.lg']",2306.11335v2.pdf,"  Recently, the advent of pre-trained large-scale language models (LLMs) like
+ChatGPT and GPT-4 have significantly advanced the machine's natural language
+understanding capabilities. This breakthrough has allowed us to seamlessly
+integrate these open-source LLMs into a unified robot simulator environment to
+help robots accurately understand and execute human natural language
+instructions. To this end, in this work, we introduce a realistic robotic
+manipulation simulator and build a Robotic Manipulation with Progressive
+Reasoning Tasks (RM-PRT) benchmark on this basis. Specifically, the RM-PRT
+benchmark builds a new high-fidelity digital twin scene based on Unreal Engine
+5, which includes 782 categories, 2023 objects, and 15K natural language
+instructions generated by ChatGPT for a detailed evaluation of robot
+manipulation. We propose a general pipeline for the RM-PRT benchmark that takes
+as input multimodal prompts containing natural language instructions and
+automatically outputs actions containing the movement and position transitions.
+We set four natural language understanding tasks with progressive reasoning
+levels and evaluate the robot's ability to understand natural language
+instructions in two modes of adsorption and grasping. In addition, we also
+conduct a comprehensive analysis and comparison of the differences and
+advantages of 10 different LLMs in instruction understanding and generation
+quality. We hope the new simulator and benchmark will facilitate future
+research on language-guided robotic manipulation. Project website:
+https://necolizer.github.io/RM-PRT/ .
+"
+Reframing Instructional Prompts to GPTk's Language,Swaroop Mishra,http://arxiv.org/pdf/2109.07830v3.pdf,2021-09-16,"['cs.cl', 'cs.ai', 'cs.lg']",2109.07830v3.pdf,"  What kinds of instructional prompts are easier to follow for Language Models
+(LMs)? We study this question by conducting extensive empirical analysis that
+shed light on important features of successful instructional prompts.
+Specifically, we study several classes of reframing techniques for manual
+reformulation of prompts into more effective ones. Some examples include
+decomposing a complex task instruction into multiple simpler tasks or itemizing
+instructions into sequential steps. Our experiments compare the zero-shot and
+few-shot performance of LMs prompted with reframed instructions on 12 NLP tasks
+across 6 categories. Compared with original instructions, our reframed
+instructions lead to significant improvements across LMs with different sizes.
+For example, the same reframed prompts boost few-shot performance of
+GPT3-series and GPT2-series by 12.5% and 6.7% respectively averaged over all
+tasks. Furthermore, reframed instructions reduce the number of examples
+required to prompt LMs in the few-shot setting. We hope these
+empirically-driven techniques will pave the way towards more effective future
+prompting algorithms.
+"
+Prompt-Based Learning for Thread Structure Prediction in Cybersecurity  Forums,Kazuaki Kashihara,http://arxiv.org/pdf/2303.05400v1.pdf,2023-03-05,"['cs.cl', 'cs.ai', 'cs.cr']",2303.05400v1.pdf,"  With recent trends indicating cyber crimes increasing in both frequency and
+cost, it is imperative to develop new methods that leverage data-rich hacker
+forums to assist in combating ever evolving cyber threats. Defining
+interactions within these forums is critical as it facilitates identifying
+highly skilled users, which can improve prediction of novel threats and future
+cyber attacks. We propose a method called Next Paragraph Prediction with
+Instructional Prompting (NPP-IP) to predict thread structures while grounded on
+the context around posts. This is the first time to apply an instructional
+prompting approach to the cybersecurity domain. We evaluate our NPP-IP with the
+Reddit dataset and Hacker Forums dataset that has posts and thread structures
+of real hacker forums' threads, and compare our method's performance with
+existing methods. The experimental evaluation shows that our proposed method
+can predict the thread structure significantly better than existing methods
+allowing for better social network prediction based on forum interactions.
+"
+Red Teaming Language Model Detectors with Language Models,Zhouxing Shi,http://arxiv.org/pdf/2305.19713v2.pdf,2023-05-31,"['cs.cl', 'cs.lg']",2305.19713v2.pdf,"  The prevalence and strong capability of large language models (LLMs) present
+significant safety and ethical risks if exploited by malicious users. To
+prevent the potentially deceptive usage of LLMs, recent works have proposed
+algorithms to detect LLM-generated text and protect LLMs. In this paper, we
+investigate the robustness and reliability of these LLM detectors under
+adversarial attacks. We study two types of attack strategies: 1) replacing
+certain words in an LLM's output with their synonyms given the context; 2)
+automatically searching for an instructional prompt to alter the writing style
+of the generation. In both strategies, we leverage an auxiliary LLM to generate
+the word replacements or the instructional prompt. Different from previous
+works, we consider a challenging setting where the auxiliary LLM can also be
+protected by a detector. Experiments reveal that our attacks effectively
+compromise the performance of all detectors in the study with plausible
+generations, underscoring the urgent need to improve the robustness of
+LLM-generated text detection systems.
+"
+Large Language Models Encode Clinical Knowledge,Karan Singhal,http://arxiv.org/pdf/2212.13138v1.pdf,2022-12-26,['cs.cl'],2212.13138v1.pdf,"  Large language models (LLMs) have demonstrated impressive capabilities in
+natural language understanding and generation, but the quality bar for medical
+and clinical applications is high. Today, attempts to assess models' clinical
+knowledge typically rely on automated evaluations on limited benchmarks. There
+is no standard to evaluate model predictions and reasoning across a breadth of
+tasks. To address this, we present MultiMedQA, a benchmark combining six
+existing open question answering datasets spanning professional medical exams,
+research, and consumer queries; and HealthSearchQA, a new free-response dataset
+of medical questions searched online. We propose a framework for human
+evaluation of model answers along multiple axes including factuality,
+precision, possible harm, and bias. In addition, we evaluate PaLM (a
+540-billion parameter LLM) and its instruction-tuned variant, Flan-PaLM, on
+MultiMedQA. Using a combination of prompting strategies, Flan-PaLM achieves
+state-of-the-art accuracy on every MultiMedQA multiple-choice dataset (MedQA,
+MedMCQA, PubMedQA, MMLU clinical topics), including 67.6% accuracy on MedQA (US
+Medical License Exam questions), surpassing prior state-of-the-art by over 17%.
+However, human evaluation reveals key gaps in Flan-PaLM responses. To resolve
+this we introduce instruction prompt tuning, a parameter-efficient approach for
+aligning LLMs to new domains using a few exemplars. The resulting model,
+Med-PaLM, performs encouragingly, but remains inferior to clinicians. We show
+that comprehension, recall of knowledge, and medical reasoning improve with
+model scale and instruction prompt tuning, suggesting the potential utility of
+LLMs in medicine. Our human evaluations reveal important limitations of today's
+models, reinforcing the importance of both evaluation frameworks and method
+development in creating safe, helpful LLM models for clinical applications.
+"
+Layout and Task Aware Instruction Prompt for Zero-shot Document Image  Question Answering,Wenjin Wang,http://arxiv.org/pdf/2306.00526v4.pdf,2023-06-01,"['cs.cl', 'cs.ai', 'cs.cv']",2306.00526v4.pdf,"  Layout-aware pre-trained models has achieved significant progress on document
+image question answering. They introduce extra learnable modules into existing
+language models to capture layout information within document images from text
+bounding box coordinates obtained by OCR tools. However, extra modules
+necessitate pre-training on extensive document images. This prevents these
+methods from directly utilizing off-the-shelf instruction-tuning language
+foundation models, which have recently shown promising potential in zero-shot
+learning. Instead, in this paper, we find that instruction-tuning language
+models like Claude and ChatGPT can understand layout by spaces and line breaks.
+Based on this observation, we propose the LAyout and Task aware Instruction
+Prompt (LATIN-Prompt), which consists of layout-aware document content and
+task-aware instruction. Specifically, the former uses appropriate spaces and
+line breaks to recover the layout information among text segments obtained by
+OCR tools, and the latter ensures that generated answers adhere to formatting
+requirements. Moreover, we propose the LAyout and Task aware Instruction Tuning
+(LATIN-Tuning) to improve the performance of small instruction-tuning models
+like Alpaca. Experimental results show that LATIN-Prompt enables zero-shot
+performance of Claude and ChatGPT to be comparable to the fine-tuning
+performance of SOTAs on document image question answering, and LATIN-Tuning
+enhances the zero-shot performance of Alpaca significantly. For example,
+LATIN-Prompt improves the performance of Claude and ChatGPT on DocVQA by 263%
+and 20% respectively. LATIN-Tuning improves the performance of Alpaca on DocVQA
+by 87.7%. Quantitative and qualitative analyses demonstrate the effectiveness
+of LATIN-Prompt and LATIN-Tuning. We provide the code in supplementary and will
+release it to facilitate future research.
+"
+InstructUIE: Multi-task Instruction Tuning for Unified Information  Extraction,Xiao Wang,http://arxiv.org/pdf/2304.08085v1.pdf,2023-04-17,"['cs.cl', 'cs.ai']",2304.08085v1.pdf,"  Large language models have unlocked strong multi-task capabilities from
+reading instructive prompts. However, recent studies have shown that existing
+large models still have difficulty with information extraction tasks. For
+example, gpt-3.5-turbo achieved an F1 score of 18.22 on the Ontonotes dataset,
+which is significantly lower than the state-of-the-art performance. In this
+paper, we propose InstructUIE, a unified information extraction framework based
+on instruction tuning, which can uniformly model various information extraction
+tasks and capture the inter-task dependency. To validate the proposed method,
+we introduce IE INSTRUCTIONS, a benchmark of 32 diverse information extraction
+datasets in a unified text-to-text format with expert-written instructions.
+Experimental results demonstrate that our method achieves comparable
+performance to Bert in supervised settings and significantly outperforms the
+state-of-the-art and gpt3.5 in zero-shot settings.
+"
+Camoscio: an Italian Instruction-tuned LLaMA,Andrea Santilli,http://arxiv.org/pdf/2307.16456v1.pdf,2023-07-31,['cs.cl'],2307.16456v1.pdf,"  In recent years Large Language Models (LLMs) have increased the state of the
+art on several natural language processing tasks. However, their accessibility
+is often limited to paid API services, posing challenges for researchers in
+conducting extensive investigations. On the other hand, while some open-source
+models have been proposed by the community, they are typically multilingual and
+not specifically tailored for the Italian language. In an effort to democratize
+the available and open resources for the Italian language, in this paper we
+introduce Camoscio: a language model specifically tuned to follow users'
+prompts in Italian. Specifically, we finetuned the smallest variant of LLaMA
+(7b) with LoRA on a corpus of instruction prompts translated to Italian via
+ChatGPT. Results indicate that the model's zero-shot performance on various
+downstream tasks in Italian competes favorably with existing models
+specifically finetuned for those tasks. All the artifacts (code, dataset,
+model) are released to the community at the following url:
+https://github.com/teelinsan/camoscio
+"
+Self-Alignment with Instruction Backtranslation,Xian Li,http://arxiv.org/pdf/2308.06259v2.pdf,2023-08-11,['cs.cl'],2308.06259v2.pdf,"  We present a scalable method to build a high quality instruction following
+language model by automatically labelling human-written text with corresponding
+instructions. Our approach, named instruction backtranslation, starts with a
+language model finetuned on a small amount of seed data, and a given web
+corpus. The seed model is used to construct training examples by generating
+instruction prompts for web documents (self-augmentation), and then selecting
+high quality examples from among these candidates (self-curation). This data is
+then used to finetune a stronger model. Finetuning LLaMa on two iterations of
+our approach yields a model that outperforms all other LLaMa-based models on
+the Alpaca leaderboard not relying on distillation data, demonstrating highly
+effective self-alignment.
+"
+Discrete Prompt Compression with Reinforcement Learning,Hoyoun Jung,http://arxiv.org/pdf/2308.08758v1.pdf,2023-08-17,"['cs.cl', 'cs.ai']",2308.08758v1.pdf,"  Instruction-tuned Language Models (LMs) are widely used by users to address
+various problems with task-specific prompts. Constraints associated with the
+context window length and computational costs encourage the development of
+compressed prompts. Existing methods rely heavily on training embeddings, which
+are designed to accommodate multiple token meanings. This presents challenges
+in terms of interpretability, a fixed number of embedding tokens, reusability
+across different LMs, and inapplicability when interacting with black-box APIs.
+This study proposes prompt compression with reinforcement learning (PCRL), a
+novel discrete prompt compression method that addresses these issues. PCRL
+employs a computationally efficient policy network that directly edits prompts.
+The PCRL training approach can be flexibly applied to various types of LMs, as
+well as decoder-only and encoder-decoder architecture, and can be trained
+without gradient access to LMs or labeled data. PCRL achieves an average
+reduction of 24.6% in token count across various instruction prompts while
+preserving performance. Further, we demonstrate that the learned policy can be
+transferred to larger LMs, and through various analyses, we aid the
+understanding of token importance within prompts.
+"
+Casteist but Not Racist? Quantifying Disparities in Large Language Model  Bias between India and the West,Khyati Khandelwal,http://arxiv.org/pdf/2309.08573v1.pdf,2023-09-15,"['cs.cl', 'cs.cy']",2309.08573v1.pdf,"  Large Language Models (LLMs), now used daily by millions of users, can encode
+societal biases, exposing their users to representational harms. A large body
+of scholarship on LLM bias exists but it predominantly adopts a Western-centric
+frame and attends comparatively less to bias levels and potential harms in the
+Global South. In this paper, we quantify stereotypical bias in popular LLMs
+according to an Indian-centric frame and compare bias levels between the Indian
+and Western contexts. To do this, we develop a novel dataset which we call
+Indian-BhED (Indian Bias Evaluation Dataset), containing stereotypical and
+anti-stereotypical examples for caste and religion contexts. We find that the
+majority of LLMs tested are strongly biased towards stereotypes in the Indian
+context, especially as compared to the Western context. We finally investigate
+Instruction Prompting as a simple intervention to mitigate such bias and find
+that it significantly reduces both stereotypical and anti-stereotypical biases
+in the majority of cases for GPT-3.5. The findings of this work highlight the
+need for including more diverse voices when evaluating LLMs.
+"
+Harnessing Large Language Models' Empathetic Response Generation  Capabilities for Online Mental Health Counselling Support,Siyuan Brandon Loh,http://arxiv.org/pdf/2310.08017v1.pdf,2023-10-12,"['cs.cl', 'i.2']",2310.08017v1.pdf,"  Large Language Models (LLMs) have demonstrated remarkable performance across
+various information-seeking and reasoning tasks. These computational systems
+drive state-of-the-art dialogue systems, such as ChatGPT and Bard. They also
+carry substantial promise in meeting the growing demands of mental health care,
+albeit relatively unexplored. As such, this study sought to examine LLMs'
+capability to generate empathetic responses in conversations that emulate those
+in a mental health counselling setting. We selected five LLMs: version 3.5 and
+version 4 of the Generative Pre-training (GPT), Vicuna FastChat-T5, Pathways
+Language Model (PaLM) version 2, and Falcon-7B-Instruct. Based on a simple
+instructional prompt, these models responded to utterances derived from the
+EmpatheticDialogues (ED) dataset. Using three empathy-related metrics, we
+compared their responses to those from traditional response generation dialogue
+systems, which were fine-tuned on the ED dataset, along with human-generated
+responses. Notably, we discovered that responses from the LLMs were remarkably
+more empathetic in most scenarios. We position our findings in light of
+catapulting advancements in creating empathetic conversational systems.
+"
+Few-shot Instruction Prompts for Pretrained Language Models to Detect  Social Biases,Shrimai Prabhumoye,http://arxiv.org/pdf/2112.07868v2.pdf,2021-12-15,"['cs.cl', 'cs.ai']",2112.07868v2.pdf,"  Detecting social bias in text is challenging due to nuance, subjectivity, and
+difficulty in obtaining good quality labeled datasets at scale, especially
+given the evolving nature of social biases and society. To address these
+challenges, we propose a few-shot instruction-based method for prompting
+pre-trained language models (LMs). We select a few class-balanced exemplars
+from a small support repository that are closest to the query to be labeled in
+the embedding space. We then provide the LM with instruction that consists of
+this subset of labeled exemplars, the query text to be classified, a definition
+of bias, and prompt it to make a decision. We demonstrate that large LMs used
+in a few-shot context can detect different types of fine-grained biases with
+similar and sometimes superior accuracy to fine-tuned models. We observe that
+the largest 530B parameter model is significantly more effective in detecting
+social bias compared to smaller models (achieving at least 13% improvement in
+AUC metric compared to other models). It also maintains a high AUC (dropping
+less than 2%) when the labeled repository is reduced to as few as $100$
+samples. Large pretrained language models thus make it easier and quicker to
+build new bias detectors.
+"
+"GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large  Language Models",Archiki Prasad,http://arxiv.org/pdf/2203.07281v2.pdf,2022-03-14,"['cs.cl', 'cs.ai', 'cs.lg']",2203.07281v2.pdf,"  Providing natural language instructions in prompts is a useful new paradigm
+for improving task performance of large language models in a zero-shot setting.
+Recent work has aimed to improve such prompts via manual rewriting or
+gradient-based tuning. However, manual rewriting is time-consuming and requires
+subjective interpretation, while gradient-based tuning can be extremely
+computationally demanding for large models and may not be feasible for
+API-based models. In this work, we introduce Gradient-free Instructional Prompt
+Search (GrIPS), a gradient-free, edit-based search approach for improving task
+instructions for large language models. GrIPS takes in instructions designed
+for humans and automatically returns an improved, edited prompt, while allowing
+for API-based tuning. With InstructGPT models, GrIPS improves the average task
+performance by up to 4.30 percentage points on eight classification tasks from
+the Natural Instructions dataset (with similar improvements for OPT, BLOOM, and
+FLAN-T5). We see improvements for both instruction-only prompts and instruction
++ k-shot examples prompts. Notably, GrIPS outperforms manual rewriting and
+purely example-based prompts while controlling for the available compute and
+data budget. Further, performance of GrIPS is comparable to select
+gradient-based tuning approaches. Qualitatively, we show our edits can simplify
+instructions and at times make them incoherent but nonetheless improve
+accuracy. Our code is available at: https://github.com/archiki/GrIPS
+"
+LINGUIST: Language Model Instruction Tuning to Generate Annotated  Utterances for Intent Classification and Slot Tagging,Andy Rosenbaum,http://arxiv.org/pdf/2209.09900v1.pdf,2022-09-20,"['cs.cl', 'cs.ai', 'cs.lg']",2209.09900v1.pdf,"  We present LINGUIST, a method for generating annotated data for Intent
+Classification and Slot Tagging (IC+ST), via fine-tuning AlexaTM 5B, a
+5-billion-parameter multilingual sequence-to-sequence (seq2seq) model, on a
+flexible instruction prompt. In a 10-shot novel intent setting for the SNIPS
+dataset, LINGUIST surpasses state-of-the-art approaches (Back-Translation and
+Example Extrapolation) by a wide margin, showing absolute improvement for the
+target intents of +1.9 points on IC Recall and +2.5 points on ST F1 Score. In
+the zero-shot cross-lingual setting of the mATIS++ dataset, LINGUIST
+out-performs a strong baseline of Machine Translation with Slot Alignment by
++4.14 points absolute on ST F1 Score across 6 languages, while matching
+performance on IC. Finally, we verify our results on an internal large-scale
+multilingual dataset for conversational agent IC+ST and show significant
+improvements over a baseline which uses Back-Translation, Paraphrasing and Slot
+Catalog Resampling. To our knowledge, we are the first to demonstrate
+instruction fine-tuning of a large-scale seq2seq model to control the outputs
+of multilingual intent- and slot-labeled data generation.
+"
+InferFix: End-to-End Program Repair with LLMs,Matthew Jin,http://arxiv.org/pdf/2303.07263v1.pdf,2023-03-13,['cs.se'],2303.07263v1.pdf,"  Software development life cycle is profoundly influenced by bugs: their
+introduction, identification, and eventual resolution account for a significant
+portion of software cost. This has motivated software engineering researchers
+and practitioners to propose different approaches for automating the
+identification and repair of software defects. Large language models have been
+adapted to the program repair task through few-shot demonstration learning and
+instruction prompting, treating this as an infilling task. However, these
+models have only focused on learning general bug-fixing patterns for
+uncategorized bugs mined from public repositories. In this paper, we propose
+InferFix: a transformer-based program repair framework paired with a
+state-of-the-art static analyzer to fix critical security and performance bugs.
+InferFix combines a Retriever -- transformer encoder model pretrained via
+contrastive learning objective, which aims at searching for semantically
+equivalent bugs and corresponding fixes; and a Generator -- a large language
+model (Codex Cushman) finetuned on supervised bug-fix data with prompts
+augmented via bug type annotations and semantically similar fixes retrieved
+from an external non-parametric memory. To train and evaluate our approach, we
+curated InferredBugs, a novel, metadata-rich dataset of bugs extracted by
+executing the Infer static analyzer on the change histories of thousands of
+Java and C# repositories. Our evaluation demonstrates that InferFix outperforms
+strong LLM baselines, with a top-1 accuracy of 65.6% for generating fixes in C#
+and 76.8% in Java. We discuss the deployment of InferFix alongside Infer at
+Microsoft which offers an end-to-end solution for detection, classification,
+and localization of bugs, as well as fixing and validation of candidate
+patches, integrated in the continuous integration pipeline to automate the
+software development workflow.
+"
+Text-based Person Search without Parallel Image-Text Data,Yang Bai,http://arxiv.org/pdf/2305.12964v2.pdf,2023-05-22,['cs.cv'],2305.12964v2.pdf,"  Text-based person search (TBPS) aims to retrieve the images of the target
+person from a large image gallery based on a given natural language
+description. Existing methods are dominated by training models with parallel
+image-text pairs, which are very costly to collect. In this paper, we make the
+first attempt to explore TBPS without parallel image-text data ($\mu$-TBPS), in
+which only non-parallel images and texts, or even image-only data, can be
+adopted. Towards this end, we propose a two-stage framework,
+generation-then-retrieval (GTR), to first generate the corresponding pseudo
+text for each image and then perform the retrieval in a supervised manner. In
+the generation stage, we propose a fine-grained image captioning strategy to
+obtain an enriched description of the person image, which firstly utilizes a
+set of instruction prompts to activate the off-the-shelf pretrained
+vision-language model to capture and generate fine-grained person attributes,
+and then converts the extracted attributes into a textual description via the
+finetuned large language model or the hand-crafted template. In the retrieval
+stage, considering the noise interference of the generated texts for training
+model, we develop a confidence score-based training scheme by enabling more
+reliable texts to contribute more during the training. Experimental results on
+multiple TBPS benchmarks (i.e., CUHK-PEDES, ICFG-PEDES and RSTPReid) show that
+the proposed GTR can achieve a promising performance without relying on
+parallel image-text data.
+"
+EDM3: Event Detection as Multi-task Text Generation,Ujjwala Anantheswaran,http://arxiv.org/pdf/2305.16357v1.pdf,2023-05-25,['cs.cl'],2305.16357v1.pdf,"  Event detection refers to identifying event occurrences in a text and
+comprises of two subtasks; event identification and classification. We present
+EDM3, a novel approach for Event Detection that formulates three generative
+tasks: identification, classification, and combined detection. We show that
+EDM3 helps to learn transferable knowledge that can be leveraged to perform
+Event Detection and its subtasks concurrently, mitigating the error propagation
+inherent in pipelined approaches. Unlike previous dataset- or domain-specific
+approaches, EDM3 utilizes the existing knowledge of language models, allowing
+it to be trained over any classification schema. We evaluate EDM3 on multiple
+event detection datasets: RAMS, WikiEvents, MAVEN, and MLEE, showing that EDM3
+outperforms 1) single-task performance by 8.4% on average and 2) multi-task
+performance without instructional prompts by 2.4% on average. We obtain SOTA
+results on RAMS (71.3% vs. 65.1% F-1) and competitive performance on other
+datasets. We analyze our approach to demonstrate its efficacy in low-resource
+and multi-sentence settings. We also show the effectiveness of this approach on
+non-standard event configurations such as multi-word and multi-class event
+triggers. Overall, our results show that EDM3 is a promising approach for Event
+Detection that has the potential for real-world applications.
+"
+VAST: A Vision-Audio-Subtitle-Text Omni-Modality Foundation Model and  Dataset,Sihan Chen,http://arxiv.org/pdf/2305.18500v2.pdf,2023-05-29,"['cs.cv', 'cs.ai', 'cs.cl', 'cs.lg', 'eess.as']",2305.18500v2.pdf,"  Vision and text have been fully explored in contemporary video-text
+foundational models, while other modalities such as audio and subtitles in
+videos have not received sufficient attention. In this paper, we resort to
+establish connections between multi-modality video tracks, including Vision,
+Audio, and Subtitle, and Text by exploring an automatically generated
+large-scale omni-modality video caption dataset called VAST-27M. Specifically,
+we first collect 27 million open-domain video clips and separately train a
+vision and an audio captioner to generate vision and audio captions. Then, we
+employ an off-the-shelf Large Language Model (LLM) to integrate the generated
+captions, together with subtitles and instructional prompts into omni-modality
+captions. Based on the proposed VAST-27M dataset, we train an omni-modality
+video-text foundational model named VAST, which can perceive and process
+vision, audio, and subtitle modalities from video, and better support various
+tasks including vision-text, audio-text, and multi-modal video-text tasks
+(retrieval, captioning and QA). Extensive experiments have been conducted to
+demonstrate the effectiveness of our proposed VAST-27M corpus and VAST
+foundation model. VAST achieves 22 new state-of-the-art results on various
+cross-modality benchmarks. Code, model and dataset will be released at
+https://github.com/TXH-mercury/VAST.
+"
+Mondrian: Prompt Abstraction Attack Against Large Language Models for  Cheaper API Pricing,Wai Man Si,http://arxiv.org/pdf/2308.03558v1.pdf,2023-08-07,"['cs.cr', 'cs.cl']",2308.03558v1.pdf,"  The Machine Learning as a Service (MLaaS) market is rapidly expanding and
+becoming more mature. For example, OpenAI's ChatGPT is an advanced large
+language model (LLM) that generates responses for various queries with
+associated fees. Although these models can deliver satisfactory performance,
+they are far from perfect. Researchers have long studied the vulnerabilities
+and limitations of LLMs, such as adversarial attacks and model toxicity.
+Inevitably, commercial ML models are also not exempt from such issues, which
+can be problematic as MLaaS continues to grow. In this paper, we discover a new
+attack strategy against LLM APIs, namely the prompt abstraction attack.
+Specifically, we propose Mondrian, a simple and straightforward method that
+abstracts sentences, which can lower the cost of using LLM APIs. In this
+approach, the adversary first creates a pseudo API (with a lower established
+price) to serve as the proxy of the target API (with a higher established
+price). Next, the pseudo API leverages Mondrian to modify the user query,
+obtain the abstracted response from the target API, and forward it back to the
+end user. Our results show that Mondrian successfully reduces user queries'
+token length ranging from 13% to 23% across various tasks, including text
+classification, generation, and question answering. Meanwhile, these abstracted
+queries do not significantly affect the utility of task-specific and general
+language models like ChatGPT. Mondrian also reduces instruction prompts' token
+length by at least 11% without compromising output quality. As a result, the
+prompt abstraction attack enables the adversary to profit without bearing the
+cost of API development and deployment.
+"
+Neuro Symbolic Reasoning for Planning: Counterexample Guided Inductive  Synthesis using Large Language Models and Satisfiability Solving,Sumit Kumar Jha,http://arxiv.org/pdf/2309.16436v1.pdf,2023-09-28,"['cs.ai', 'cs.lo']",2309.16436v1.pdf,"  Generative large language models (LLMs) with instruct training such as GPT-4
+can follow human-provided instruction prompts and generate human-like responses
+to these prompts. Apart from natural language responses, they have also been
+found to be effective at generating formal artifacts such as code, plans, and
+logical specifications from natural language prompts. Despite their remarkably
+improved accuracy, these models are still known to produce factually incorrect
+or contextually inappropriate results despite their syntactic coherence - a
+phenomenon often referred to as hallucination. This limitation makes it
+difficult to use these models to synthesize formal artifacts that are used in
+safety-critical applications. Unlike tasks such as text summarization and
+question-answering, bugs in code, plan, and other formal artifacts produced by
+LLMs can be catastrophic. We posit that we can use the satisfiability modulo
+theory (SMT) solvers as deductive reasoning engines to analyze the generated
+solutions from the LLMs, produce counterexamples when the solutions are
+incorrect, and provide that feedback to the LLMs exploiting the dialog
+capability of instruct-trained LLMs. This interaction between inductive LLMs
+and deductive SMT solvers can iteratively steer the LLM to generate the correct
+response. In our experiments, we use planning over the domain of blocks as our
+synthesis task for evaluating our approach. We use GPT-4, GPT3.5 Turbo,
+Davinci, Curie, Babbage, and Ada as the LLMs and Z3 as the SMT solver. Our
+method allows the user to communicate the planning problem in natural language;
+even the formulation of queries to SMT solvers is automatically generated from
+natural language. Thus, the proposed technique can enable non-expert users to
+describe their problems in natural language, and the combination of LLMs and
+SMT solvers can produce provably correct solutions.
+"
+Benchmarking a foundation LLM on its ability to re-label structure names  in accordance with the AAPM TG-263 report,Jason Holmes,http://arxiv.org/pdf/2310.03874v1.pdf,2023-10-05,"['physics.med-ph', 'cs.cl']",2310.03874v1.pdf,"  Purpose: To introduce the concept of using large language models (LLMs) to
+re-label structure names in accordance with the American Association of
+Physicists in Medicine (AAPM) Task Group (TG)-263 standard, and to establish a
+benchmark for future studies to reference.
+  Methods and Materials: The Generative Pre-trained Transformer (GPT)-4
+application programming interface (API) was implemented as a Digital Imaging
+and Communications in Medicine (DICOM) storage server, which upon receiving a
+structure set DICOM file, prompts GPT-4 to re-label the structure names of both
+target volumes and normal tissues according to the AAPM TG-263. Three disease
+sites, prostate, head and neck, and thorax were selected for evaluation. For
+each disease site category, 150 patients were randomly selected for manually
+tuning the instructions prompt (in batches of 50) and 50 patients were randomly
+selected for evaluation. Structure names that were considered were those that
+were most likely to be relevant for studies utilizing structure contours for
+many patients.
+  Results: The overall re-labeling accuracy of both target volumes and normal
+tissues for prostate, head and neck, and thorax cases was 96.0%, 98.5%, and
+96.9% respectively. Re-labeling of target volumes was less accurate on average
+except for prostate - 100%, 93.1%, and 91.1% respectively.
+  Conclusions: Given the accuracy of GPT-4 in re-labeling structure names of
+both target volumes and normal tissues as presented in this work, LLMs are
+poised to be the preferred method for standardizing structure names in
+radiation oncology, especially considering the rapid advancements in LLM
+capabilities that are likely to continue.
+"
+What Makes Pre-trained Language Models Better Zero-shot Learners?,Jinghui Lu,http://arxiv.org/pdf/2209.15206v3.pdf,2022-09-30,"['cs.cl', 'cs.ai']",2209.15206v3.pdf,"  Current methods for prompt learning in zeroshot scenarios widely rely on a
+development set with sufficient human-annotated data to select the
+best-performing prompt template a posteriori. This is not ideal because in a
+realworld zero-shot scenario of practical relevance, no labelled data is
+available. Thus, we propose a simple yet effective method for screening
+reasonable prompt templates in zero-shot text classification: Perplexity
+Selection (Perplection). We hypothesize that language discrepancy can be used
+to measure the efficacy of prompt templates, and thereby develop a
+substantiated perplexity-based scheme allowing for forecasting the performance
+of prompt templates in advance. Experiments show that our method leads to
+improved prediction performance in a realistic zero-shot setting, eliminating
+the need for any labelled examples.
+"
+IIE-NLP-NUT at SemEval-2020 Task 4: Guiding PLM with Prompt Template  Reconstruction Strategy for ComVE,Luxi Xing,http://arxiv.org/pdf/2007.00924v1.pdf,2020-07-02,['cs.cl'],2007.00924v1.pdf,"  This paper introduces our systems for the first two subtasks of SemEval
+Task4: Commonsense Validation and Explanation. To clarify the intention for
+judgment and inject contrastive information for selection, we propose the input
+reconstruction strategy with prompt templates. Specifically, we formalize the
+subtasks into the multiple-choice question answering format and construct the
+input with the prompt templates, then, the final prediction of question
+answering is considered as the result of subtasks. Experimental results show
+that our approaches achieve significant performance compared with the baseline
+systems. Our approaches secure the third rank on both official test sets of the
+first two subtasks with an accuracy of 96.4 and an accuracy of 94.3
+respectively.
+"
+GraphPrompt: Biomedical Entity Normalization Using Graph-based Prompt  Templates,Jiayou Zhang,http://arxiv.org/pdf/2112.03002v1.pdf,2021-11-13,"['cs.cl', 'cs.ai']",2112.03002v1.pdf,"  Biomedical entity normalization unifies the language across biomedical
+experiments and studies, and further enables us to obtain a holistic view of
+life sciences. Current approaches mainly study the normalization of more
+standardized entities such as diseases and drugs, while disregarding the more
+ambiguous but crucial entities such as pathways, functions and cell types,
+hindering their real-world applications. To achieve biomedical entity
+normalization on these under-explored entities, we first introduce an
+expert-curated dataset OBO-syn encompassing 70 different types of entities and
+2 million curated entity-synonym pairs. To utilize the unique graph structure
+in this dataset, we propose GraphPrompt, a prompt-based learning approach that
+creates prompt templates according to the graphs. GraphPrompt obtained 41.0%
+and 29.9% improvement on zero-shot and few-shot settings respectively,
+indicating the effectiveness of these graph-based prompt templates. We envision
+that our method GraphPrompt and OBO-syn dataset can be broadly applied to
+graph-based NLP tasks, and serve as the basis for analyzing diverse and
+accumulating biomedical data.
+"
+CCPrompt: Counterfactual Contrastive Prompt-Tuning for Many-Class  Classification,Yang Li,http://arxiv.org/pdf/2211.05987v1.pdf,2022-11-11,['cs.cl'],2211.05987v1.pdf,"  With the success of the prompt-tuning paradigm in Natural Language Processing
+(NLP), various prompt templates have been proposed to further stimulate
+specific knowledge for serving downstream tasks, e.g., machine translation,
+text generation, relation extraction, and so on. Existing prompt templates are
+mainly shared among all training samples with the information of task
+description. However, training samples are quite diverse. The sharing task
+description is unable to stimulate the unique task-related information in each
+training sample, especially for tasks with the finite-label space. To exploit
+the unique task-related information, we imitate the human decision process
+which aims to find the contrastive attributes between the objective factual and
+their potential counterfactuals. Thus, we propose the \textbf{C}ounterfactual
+\textbf{C}ontrastive \textbf{Prompt}-Tuning (CCPrompt) approach for many-class
+classification, e.g., relation classification, topic classification, and entity
+typing. Compared with simple classification tasks, these tasks have more
+complex finite-label spaces and are more rigorous for prompts. First of all, we
+prune the finite label space to construct fact-counterfactual pairs. Then, we
+exploit the contrastive attributes by projecting training instances onto every
+fact-counterfactual pair. We further set up global prototypes corresponding
+with all contrastive attributes for selecting valid contrastive attributes as
+additional tokens in the prompt template. Finally, a simple Siamese
+representation learning is employed to enhance the robustness of the model. We
+conduct experiments on relation classification, topic classification, and
+entity typing tasks in both fully supervised setting and few-shot setting. The
+results indicate that our model outperforms former baselines.
+"
+Low-Resource Multi-Granularity Academic Function Recognition Based on  Multiple Prompt Knowledge,Jiawei Liu,http://arxiv.org/pdf/2305.03287v1.pdf,2023-05-05,"['cs.cl', 'cs.ai']",2305.03287v1.pdf,"  Fine-tuning pre-trained language models (PLMs), e.g., SciBERT, generally
+requires large numbers of annotated data to achieve state-of-the-art
+performance on a range of NLP tasks in the scientific domain. However,
+obtaining the fine-tune data for scientific NLP task is still challenging and
+expensive. Inspired by recent advancement in prompt learning, in this paper, we
+propose the Mix Prompt Tuning (MPT), which is a semi-supervised method to
+alleviate the dependence on annotated data and improve the performance of
+multi-granularity academic function recognition tasks with a small number of
+labeled examples. Specifically, the proposed method provides multi-perspective
+representations by combining manual prompt templates with automatically learned
+continuous prompt templates to help the given academic function recognition
+task take full advantage of knowledge in PLMs. Based on these prompt templates
+and the fine-tuned PLM, a large number of pseudo labels are assigned to the
+unlabeled examples. Finally, we fine-tune the PLM using the pseudo training
+set. We evaluate our method on three academic function recognition tasks of
+different granularity including the citation function, the abstract sentence
+function, and the keyword function, with datasets from computer science domain
+and biomedical domain. Extensive experiments demonstrate the effectiveness of
+our method and statistically significant improvements against strong baselines.
+In particular, it achieves an average increase of 5% in Macro-F1 score compared
+with fine-tuning, and 6% in Macro-F1 score compared with other semi-supervised
+method under low-resource settings. In addition, MPT is a general method that
+can be easily applied to other low-resource scientific classification tasks.
+"
+AutoCLIP: Auto-tuning Zero-Shot Classifiers for Vision-Language Models,Jan Hendrik Metzen,http://arxiv.org/pdf/2309.16414v2.pdf,2023-09-28,"['cs.cv', 'cs.ai', 'cs.lg']",2309.16414v2.pdf,"  Classifiers built upon vision-language models such as CLIP have shown
+remarkable zero-shot performance across a broad range of image classification
+tasks. Prior work has studied different ways of automatically creating
+descriptor sets for every class based on prompt templates, ranging from
+manually engineered templates over templates obtained from a large language
+model to templates built from random words and characters. Up until now,
+deriving zero-shot classifiers from the respective encoded class descriptors
+has remained nearly unchanged, i.e., classify to the class that maximizes
+cosine similarity between its averaged encoded class descriptors and the image
+encoding. However, weighing all class descriptors equally can be suboptimal
+when certain descriptors match visual clues on a given image better than
+others. In this work, we propose AutoCLIP, a method for auto-tuning zero-shot
+classifiers. AutoCLIP tunes per-image weights to each prompt template at
+inference time, based on statistics of class descriptor-image similarities.
+AutoCLIP is fully unsupervised, has very low computational overhead, and can be
+easily implemented in few lines of code. We show that AutoCLIP outperforms
+baselines across a broad range of vision-language models, datasets, and prompt
+templates consistently and by up to 3 percent point accuracy.
+"
+Position-based Prompting for Health Outcome Generation,M. Abaho,http://arxiv.org/pdf/2204.03489v1.pdf,2022-03-30,"['cs.cl', 'cs.lg']",2204.03489v1.pdf,"  Probing Pre-trained Language Models (PLMs) using prompts has indirectly
+implied that language models (LMs) can be treated as knowledge bases. To this
+end, this phenomena has been effective especially when these LMs are fine-tuned
+towards not just data of a specific domain, but also to the style or linguistic
+pattern of the prompts themselves. We observe that, satisfying a particular
+linguistic pattern in prompts is an unsustainable constraint that unnecessarily
+lengthens the probing task, especially because, they are often manually
+designed and the range of possible prompt template patterns can vary depending
+on the prompting objective and domain. We therefore explore an idea of using a
+position-attention mechanism to capture positional information of each word in
+a prompt relative to the mask to be filled, hence avoiding the need to
+re-construct prompts when the prompts linguistic pattern changes. Using our
+approach, we demonstrate the ability of eliciting answers to rare prompt
+templates (in a case study on health outcome generation) such as Postfix and
+Mixed patterns whose missing information is respectively at the start and in
+multiple random places of the prompt. More so, using various biomedical PLMs,
+our approach consistently outperforms a baseline in which the default mask
+language model (MLM) representation is used to predict masked tokens.
+"
+Prompting Large Language Models With the Socratic Method,Edward Y. Chang,http://arxiv.org/pdf/2303.08769v2.pdf,2023-02-17,"['cs.lg', 'i.2.7']",2303.08769v2.pdf,"  This paper presents a systematic approach to using the Socratic method in
+developing prompt templates that effectively interact with large language
+models, including GPT-3. Various methods are examined, and those that yield
+precise answers and justifications while fostering creativity and imagination
+to enhance creative writing are identified. Techniques such as {\em
+definition}, {\em elenchus}, {\em dialectic}, {\em maieutics}, {\em
+generalization}, and {\em counterfactual reasoning} are discussed for their
+application in engineering prompt templates and their connections to inductive,
+deductive, and abductive reasoning. Through examples, the effectiveness of
+these dialogue and reasoning methods is demonstrated. An interesting
+observation is made that when the task's goal and user intent are conveyed to
+GPT-3 via ChatGPT before the start of a dialogue, the large language model
+seems to connect to the external context expressed in the intent and perform
+more effectively.
+"
+Prompt Learning for News Recommendation,Zizhuo Zhang,http://arxiv.org/pdf/2304.05263v1.pdf,2023-04-11,"['cs.ir', 'cs.ai', 'h.3.3']",2304.05263v1.pdf,"  Some recent \textit{news recommendation} (NR) methods introduce a Pre-trained
+Language Model (PLM) to encode news representation by following the vanilla
+pre-train and fine-tune paradigm with carefully-designed
+recommendation-specific neural networks and objective functions. Due to the
+inconsistent task objective with that of PLM, we argue that their modeling
+paradigm has not well exploited the abundant semantic information and
+linguistic knowledge embedded in the pre-training process. Recently, the
+pre-train, prompt, and predict paradigm, called \textit{prompt learning}, has
+achieved many successes in natural language processing domain. In this paper,
+we make the first trial of this new paradigm to develop a \textit{Prompt
+Learning for News Recommendation} (Prompt4NR) framework, which transforms the
+task of predicting whether a user would click a candidate news as a cloze-style
+mask-prediction task. Specifically, we design a series of prompt templates,
+including discrete, continuous, and hybrid templates, and construct their
+corresponding answer spaces to examine the proposed Prompt4NR framework.
+Furthermore, we use the prompt ensembling to integrate predictions from
+multiple prompt templates. Extensive experiments on the MIND dataset validate
+the effectiveness of our Prompt4NR with a set of new benchmark results.
+"
+Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot  Classification,Han Wang,http://arxiv.org/pdf/2204.06305v2.pdf,2022-04-13,"['cs.cl', 'cs.ai', 'cs.lg']",2204.06305v2.pdf,"  Prompt-based learning (i.e., prompting) is an emerging paradigm for
+exploiting knowledge learned by a pretrained language model. In this paper, we
+propose Automatic Multi-Label Prompting (AMuLaP), a simple yet effective method
+to automatically select label mappings for few-shot text classification with
+prompting. Our method exploits one-to-many label mappings and a
+statistics-based algorithm to select label mappings given a prompt template.
+Our experiments demonstrate that AMuLaP achieves competitive performance on the
+GLUE benchmark without human effort or external resources.
+"
+CoCoMo: Computational Consciousness Modeling for Generative and Ethical  AI,Edward Y. Chang,http://arxiv.org/pdf/2304.02438v2.pdf,2023-03-17,"['cs.oh', 'i.2.7']",2304.02438v2.pdf,"  The CoCoMo model proposes a computational solution to the challenge of
+incorporating ethical and emotional intelligence considerations into AI
+systems, with the aim of creating AI agents that combine knowledge with
+compassion. To achieve this goal, CoCoMo prioritizes fairness, beneficence,
+non-maleficence, empathy, adaptability, transparency, and critical and
+exploratory thinking abilities. The model employs consciousness modeling,
+reinforcement learning, and prompt template formulation to support these
+desired traits. By incorporating ethical and emotional intelligence
+considerations, a generative AI model can potentially lead to improved
+fairness, reduced toxicity, and increased reliability.
+"
+PromptNER: Prompt Locating and Typing for Named Entity Recognition,Yongliang Shen,http://arxiv.org/pdf/2305.17104v1.pdf,2023-05-26,['cs.cl'],2305.17104v1.pdf,"  Prompt learning is a new paradigm for utilizing pre-trained language models
+and has achieved great success in many tasks. To adopt prompt learning in the
+NER task, two kinds of methods have been explored from a pair of symmetric
+perspectives, populating the template by enumerating spans to predict their
+entity types or constructing type-specific prompts to locate entities. However,
+these methods not only require a multi-round prompting manner with a high time
+overhead and computational cost, but also require elaborate prompt templates,
+that are difficult to apply in practical scenarios. In this paper, we unify
+entity locating and entity typing into prompt learning, and design a dual-slot
+multi-prompt template with the position slot and type slot to prompt locating
+and typing respectively. Multiple prompts can be input to the model
+simultaneously, and then the model extracts all entities by parallel
+predictions on the slots. To assign labels for the slots during training, we
+design a dynamic template filling mechanism that uses the extended bipartite
+graph matching between prompts and the ground-truth entities. We conduct
+experiments in various settings, including resource-rich flat and nested NER
+datasets and low-resource in-domain and cross-domain datasets. Experimental
+results show that the proposed model achieves a significant performance
+improvement, especially in the cross-domain few-shot setting, which outperforms
+the state-of-the-art model by +7.7% on average.
+"
+Large Language and Text-to-3D Models for Engineering Design Optimization,Thiago Rios,http://arxiv.org/pdf/2307.01230v1.pdf,2023-07-03,"['cs.cl', 'cs.lg', 'cs.ne']",2307.01230v1.pdf,"  The current advances in generative AI for learning large neural network
+models with the capability to produce essays, images, music and even 3D assets
+from text prompts create opportunities for a manifold of disciplines. In the
+present paper, we study the potential of deep text-to-3D models in the
+engineering domain, with focus on the chances and challenges when integrating
+and interacting with 3D assets in computational simulation-based design
+optimization. In contrast to traditional design optimization of 3D geometries
+that often searches for the optimum designs using numerical representations,
+such as B-Spline surface or deformation parameters in vehicle aerodynamic
+optimization, natural language challenges the optimization framework by
+requiring a different interpretation of variation operators while at the same
+time may ease and motivate the human user interaction. Here, we propose and
+realize a fully automated evolutionary design optimization framework using
+Shap-E, a recently published text-to-3D asset network by OpenAI, in the context
+of aerodynamic vehicle optimization. For representing text prompts in the
+evolutionary optimization, we evaluate (a) a bag-of-words approach based on
+prompt templates and Wordnet samples, and (b) a tokenisation approach based on
+prompt templates and the byte pair encoding method from GPT4. Our main findings
+from the optimizations indicate that, first, it is important to ensure that the
+designs generated from prompts are within the object class of application, i.e.
+diverse and novel designs need to be realistic, and, second, that more research
+is required to develop methods where the strength of text prompt variations and
+the resulting variations of the 3D designs share causal relations to some
+degree to improve the optimization.
+"
+Zero-shot information extraction from radiological reports using ChatGPT,Danqing Hu,http://arxiv.org/pdf/2309.01398v2.pdf,2023-09-04,['cs.cl'],2309.01398v2.pdf,"  Electronic health records contain an enormous amount of valuable information,
+but many are recorded in free text. Information extraction is the strategy to
+transform the sequence of characters into structured data, which can be
+employed for secondary analysis. However, the traditional information
+extraction components, such as named entity recognition and relation
+extraction, require annotated data to optimize the model parameters, which has
+become one of the major bottlenecks in building information extraction systems.
+With the large language models achieving good performances on various
+downstream NLP tasks without parameter tuning, it becomes possible to use large
+language models for zero-shot information extraction. In this study, we aim to
+explore whether the most popular large language model, ChatGPT, can extract
+useful information from the radiological reports. We first design the prompt
+template for the interested information in the CT reports. Then, we generate
+the prompts by combining the prompt template with the CT reports as the inputs
+of ChatGPT to obtain the responses. A post-processing module is developed to
+transform the responses into structured extraction results. We conducted the
+experiments with 847 CT reports collected from Peking University Cancer
+Hospital. The experimental results indicate that ChatGPT can achieve
+competitive performances for some extraction tasks compared with the baseline
+information extraction system, but some limitations need to be further
+improved.
+"
+Can Language Models be Biomedical Knowledge Bases?,Mujeen Sung,http://arxiv.org/pdf/2109.07154v1.pdf,2021-09-15,['cs.cl'],2109.07154v1.pdf,"  Pre-trained language models (LMs) have become ubiquitous in solving various
+natural language processing (NLP) tasks. There has been increasing interest in
+what knowledge these LMs contain and how we can extract that knowledge,
+treating LMs as knowledge bases (KBs). While there has been much work on
+probing LMs in the general domain, there has been little attention to whether
+these powerful LMs can be used as domain-specific KBs. To this end, we create
+the BioLAMA benchmark, which is comprised of 49K biomedical factual knowledge
+triples for probing biomedical LMs. We find that biomedical LMs with recently
+proposed probing methods can achieve up to 18.51% Acc@5 on retrieving
+biomedical knowledge. Although this seems promising given the task difficulty,
+our detailed analyses reveal that most predictions are highly correlated with
+prompt templates without any subjects, hence producing similar results on each
+relation and hindering their capabilities to be used as domain-specific KBs. We
+hope that BioLAMA can serve as a challenging benchmark for biomedical factual
+probing.
+"
+HealthPrompt: A Zero-shot Learning Paradigm for Clinical Natural  Language Processing,Sonish Sivarajkumar,http://arxiv.org/pdf/2203.05061v1.pdf,2022-03-09,"['cs.cl', 'cs.ai', 'cs.ir']",2203.05061v1.pdf,"  Deep learning algorithms are dependent on the availability of large-scale
+annotated clinical text datasets. The lack of such publicly available datasets
+is the biggest bottleneck for the development of clinical Natural Language
+Processing(NLP) systems. Zero-Shot Learning(ZSL) refers to the use of deep
+learning models to classify instances from new classes of which no training
+data have been seen before. Prompt-based learning is an emerging ZSL technique
+where we define task-based templates for NLP tasks. We developed a novel
+prompt-based clinical NLP framework called HealthPrompt and applied the
+paradigm of prompt-based learning on clinical texts. In this technique, rather
+than fine-tuning a Pre-trained Language Model(PLM), the task definitions are
+tuned by defining a prompt template. We performed an in-depth analysis of
+HealthPrompt on six different PLMs in a no-data setting. Our experiments prove
+that prompts effectively capture the context of clinical texts and perform
+remarkably well without any training data.
+"
+RelationPrompt: Leveraging Prompts to Generate Synthetic Data for  Zero-Shot Relation Triplet Extraction,Yew Ken Chia,http://arxiv.org/pdf/2203.09101v1.pdf,2022-03-17,['cs.cl'],2203.09101v1.pdf,"  Despite the importance of relation extraction in building and representing
+knowledge, less research is focused on generalizing to unseen relations types.
+We introduce the task setting of Zero-Shot Relation Triplet Extraction
+(ZeroRTE) to encourage further research in low-resource relation extraction
+methods. Given an input sentence, each extracted triplet consists of the head
+entity, relation label, and tail entity where the relation label is not seen at
+the training stage. To solve ZeroRTE, we propose to synthesize relation
+examples by prompting language models to generate structured texts. Concretely,
+we unify language model prompts and structured text approaches to design a
+structured prompt template for generating synthetic relation samples when
+conditioning on relation label prompts (RelationPrompt). To overcome the
+limitation for extracting multiple relation triplets in a sentence, we design a
+novel Triplet Search Decoding method. Experiments on FewRel and Wiki-ZSL
+datasets show the efficacy of RelationPrompt for the ZeroRTE task and zero-shot
+relation classification. Our code and data are available at
+github.com/declare-lab/RelationPrompt.
+"
+CUP: Curriculum Learning based Prompt Tuning for Implicit Event Argument  Extraction,Jiaju Lin,http://arxiv.org/pdf/2205.00498v2.pdf,2022-05-01,['cs.cl'],2205.00498v2.pdf,"  Implicit event argument extraction (EAE) aims to identify arguments that
+could scatter over the document. Most previous work focuses on learning the
+direct relations between arguments and the given trigger, while the implicit
+relations with long-range dependency are not well studied. Moreover, recent
+neural network based approaches rely on a large amount of labeled data for
+training, which is unavailable due to the high labelling cost. In this paper,
+we propose a Curriculum learning based Prompt tuning (CUP) approach, which
+resolves implicit EAE by four learning stages. The stages are defined according
+to the relations with the trigger node in a semantic graph, which well captures
+the long-range dependency between arguments and the trigger. In addition, we
+integrate a prompt-based encoder-decoder model to elicit related knowledge from
+pre-trained language models (PLMs) in each stage, where the prompt templates
+are adapted with the learning progress to enhance the reasoning for arguments.
+Experimental results on two well-known benchmark datasets show the great
+advantages of our proposed approach. In particular, we outperform the
+state-of-the-art models in both fully-supervised and low-data scenarios.
+"
+Let Me Check the Examples: Enhancing Demonstration Learning via Explicit  Imitation,Sirui Wang,http://arxiv.org/pdf/2209.00455v1.pdf,2022-08-31,"['cs.lg', 'cs.ai']",2209.00455v1.pdf,"  Demonstration learning aims to guide the prompt prediction via providing
+answered demonstrations in the few shot settings. Despite achieving promising
+results, existing work only concatenates the answered examples as
+demonstrations to the prompt template (including the raw context) without any
+additional operation, neglecting the prompt-demonstration dependencies.
+Besides, prior research found that randomly replacing the labels of
+demonstrations marginally hurts performance, illustrating that the model could
+not properly learn the knowledge brought by the demonstrations. Inspired by the
+human learning process, in this paper, we introduce Imitation DEMOnstration
+Learning (Imitation-Demo) to strengthen demonstration learning via explicitly
+imitating human review behaviour, which includes: (1) contrastive learning
+mechanism to concentrate on the similar demonstrations. (2) demonstration-label
+re-prediction method to consolidate known knowledge. Experiment results show
+that our proposed method achieves state-of-the-art performance on 11 out of 14
+classification corpora. Further studies also prove that Imitation-Demo
+strengthen the association between prompt and demonstrations, which could
+provide the basis for exploring how demonstration learning works.
+"
+A Few-shot Approach to Resume Information Extraction via Prompts,Chengguang Gan,http://arxiv.org/pdf/2209.09450v2.pdf,2022-09-20,['cs.cl'],2209.09450v2.pdf,"  Prompt learning's fine-tune performance on text classification tasks has
+attracted the NLP community. This paper applies it to resume information
+extraction, improving existing methods for this task. We created manual
+templates and verbalizers tailored to resume texts and compared the performance
+of Masked Language Model (MLM) and Seq2Seq PLMs. Also, we enhanced the
+verbalizer design for Knowledgeable Prompt-tuning, contributing to prompt
+template design across NLP tasks. We present the Manual Knowledgeable
+Verbalizer (MKV), a rule for constructing verbalizers for specific
+applications. Our tests show that MKV rules yield more effective, robust
+templates and verbalizers than existing methods. Our MKV approach resolved
+sample imbalance, surpassing current automatic prompt methods. This study
+underscores the value of tailored prompt learning for resume extraction,
+stressing the importance of custom-designed templates and verbalizers.
+"
+Distilling Task-specific Logical Rules from Large Pre-trained Models,Tao Chen,http://arxiv.org/pdf/2210.02768v1.pdf,2022-10-06,['cs.cl'],2210.02768v1.pdf,"  Logical rules, both transferable and explainable, are widely used as weakly
+supervised signals for many downstream tasks such as named entity tagging. To
+reduce the human effort of writing rules, previous researchers adopt an
+iterative approach to automatically learn logical rules from several seed
+rules. However, obtaining more seed rules can only be accomplished by extra
+human annotation with heavy costs. Limited by the size and quality of the seed
+rules, the model performance of previous systems is bounded. In this paper, we
+develop a novel framework STREAM to distill task-specific logical rules from
+large pre-trained models. Specifically, we borrow recent prompt-based language
+models as the knowledge expert to yield initial seed rules, and based on the
+formed high-quality instance pool that acts as an intermediary role, we keep
+teaching the expert to fit our task and learning task-specific logical rules.
+Experiments on three public named entity tagging benchmarks demonstrate the
+effectiveness of our proposed framework. With several predefined prompt
+templates, our system has gained significant improvements over previous
+state-of-the-art methods.
+"
+CLIP model is an Efficient Continual Learner,Vishal Thengane,http://arxiv.org/pdf/2210.03114v1.pdf,2022-10-06,['cs.cv'],2210.03114v1.pdf,"  The continual learning setting aims to learn new tasks over time without
+forgetting the previous ones. The literature reports several significant
+efforts to tackle this problem with limited or no access to previous task data.
+Among such efforts, typical solutions offer sophisticated techniques involving
+memory replay, knowledge distillation, model regularization, and dynamic
+network expansion. The resulting methods have a retraining cost at each
+learning task, dedicated memory requirements, and setting-specific design
+choices. In this work, we show that a frozen CLIP (Contrastive Language-Image
+Pretraining) model offers astounding continual learning performance without any
+fine-tuning (zero-shot evaluation). We evaluate CLIP under a variety of
+settings including class-incremental, domain-incremental and task-agnostic
+incremental learning on five popular benchmarks (ImageNet-100 & 1K, CORe50,
+CIFAR-100, and TinyImageNet). Without any bells and whistles, the CLIP model
+outperforms the state-of-the-art continual learning approaches in the majority
+of the settings. We show the effect on the CLIP model's performance by varying
+text inputs with simple prompt templates. To the best of our knowledge, this is
+the first work to report the CLIP zero-shot performance in a continual setting.
+We advocate the use of this strong yet embarrassingly simple baseline for
+future comparisons in the continual learning tasks.
+"
+A Unified Framework for Multi-intent Spoken Language Understanding with  prompting,Feifan Song,http://arxiv.org/pdf/2210.03337v1.pdf,2022-10-07,"['cs.cl', 'cs.ai']",2210.03337v1.pdf,"  Multi-intent Spoken Language Understanding has great potential for widespread
+implementation. Jointly modeling Intent Detection and Slot Filling in it
+provides a channel to exploit the correlation between intents and slots.
+However, current approaches are apt to formulate these two sub-tasks
+differently, which leads to two issues: 1) It hinders models from effective
+extraction of shared features. 2) Pretty complicated structures are involved to
+enhance expression ability while causing damage to the interpretability of
+frameworks. In this work, we describe a Prompt-based Spoken Language
+Understanding (PromptSLU) framework, to intuitively unify two sub-tasks into
+the same form by offering a common pre-trained Seq2Seq model. In detail, ID and
+SF are completed by concisely filling the utterance into task-specific prompt
+templates as input, and sharing output formats of key-value pairs sequence.
+Furthermore, variable intents are predicted first, then naturally embedded into
+prompts to guide slot-value pairs inference from a semantic perspective.
+Finally, we are inspired by prevalent multi-task learning to introduce an
+auxiliary sub-task, which helps to learn relationships among provided labels.
+Experiment results show that our framework outperforms several state-of-the-art
+baselines on two public datasets.
+"
+UniHD at TSAR-2022 Shared Task: Is Compute All We Need for Lexical  Simplification?,Dennis Aumiller,http://arxiv.org/pdf/2301.01764v2.pdf,2023-01-04,['cs.cl'],2301.01764v2.pdf,"  Previous state-of-the-art models for lexical simplification consist of
+complex pipelines with several components, each of which requires deep
+technical knowledge and fine-tuned interaction to achieve its full potential.
+As an alternative, we describe a frustratingly simple pipeline based on
+prompted GPT-3 responses, beating competing approaches by a wide margin in
+settings with few training instances. Our best-performing submission to the
+English language track of the TSAR-2022 shared task consists of an ``ensemble''
+of six different prompt templates with varying context levels. As a
+late-breaking result, we further detail a language transfer technique that
+allows simplification in languages other than English. Applied to the Spanish
+and Portuguese subset, we achieve state-of-the-art results with only minor
+modification to the original prompts. Aside from detailing the implementation
+and setup, we spend the remainder of this work discussing the particularities
+of prompting and implications for future work. Code for the experiments is
+available online at https://github.com/dennlinger/TSAR-2022-Shared-Task
+"
+Prompting Large Language Model for Machine Translation: A Case Study,Biao Zhang,http://arxiv.org/pdf/2301.07069v2.pdf,2023-01-17,"['cs.cl', 'cs.lg']",2301.07069v2.pdf,"  Research on prompting has shown excellent performance with little or even no
+supervised training across many tasks. However, prompting for machine
+translation is still under-explored in the literature. We fill this gap by
+offering a systematic study on prompting strategies for translation, examining
+various factors for prompt template and demonstration example selection. We
+further explore the use of monolingual data and the feasibility of
+cross-lingual, cross-domain, and sentence-to-document transfer learning in
+prompting. Extensive experiments with GLM-130B (Zeng et al., 2022) as the
+testbed show that 1) the number and the quality of prompt examples matter,
+where using suboptimal examples degenerates translation; 2) several features of
+prompt examples, such as semantic similarity, show significant Spearman
+correlation with their prompting performance; yet, none of the correlations are
+strong enough; 3) using pseudo parallel prompt examples constructed from
+monolingual data via zero-shot prompting could improve translation; and 4)
+improved performance is achievable by transferring knowledge from prompt
+examples selected in other settings. We finally provide an analysis on the
+model outputs and discuss several problems that prompting still suffers from.
+"
+Global Constraints with Prompting for Zero-Shot Event Argument  Classification,Zizheng Lin,http://arxiv.org/pdf/2302.04459v1.pdf,2023-02-09,['cs.cl'],2302.04459v1.pdf,"  Determining the role of event arguments is a crucial subtask of event
+extraction. Most previous supervised models leverage costly annotations, which
+is not practical for open-domain applications. In this work, we propose to use
+global constraints with prompting to effectively tackles event argument
+classification without any annotation and task-specific training. Specifically,
+given an event and its associated passage, the model first creates several new
+passages by prefix prompts and cloze prompts, where prefix prompts indicate
+event type and trigger span, and cloze prompts connect each candidate role with
+the target argument span. Then, a pre-trained language model scores the new
+passages, making the initial prediction. Our novel prompt templates can easily
+adapt to all events and argument types without manual effort. Next, the model
+regularizes the prediction by global constraints exploiting cross-task,
+cross-argument, and cross-event relations. Extensive experiments demonstrate
+our model's effectiveness: it outperforms the best zero-shot baselines by 12.5%
+and 10.9% F1 on ACE and ERE with given argument spans and by 4.3% and 3.3% F1,
+respectively, without given argument spans. We have made our code publicly
+available.
+"
+Large Language Models Are State-of-the-Art Evaluators of Translation  Quality,Tom Kocmi,http://arxiv.org/pdf/2302.14520v2.pdf,2023-02-28,['cs.cl'],2302.14520v2.pdf,"  We describe GEMBA, a GPT-based metric for assessment of translation quality,
+which works both with a reference translation and without. In our evaluation,
+we focus on zero-shot prompting, comparing four prompt variants in two modes,
+based on the availability of the reference. We investigate nine versions of GPT
+models, including ChatGPT and GPT-4. We show that our method for translation
+quality assessment only works with GPT~3.5 and larger models. Comparing to
+results from WMT22's Metrics shared task, our method achieves state-of-the-art
+accuracy in both modes when compared to MQM-based human labels. Our results are
+valid on the system level for all three WMT22 Metrics shared task language
+pairs, namely English into German, English into Russian, and Chinese into
+English. This provides a first glimpse into the usefulness of pre-trained,
+generative large language models for quality assessment of translations. We
+publicly release all our code and prompt templates used for the experiments
+described in this work, as well as all corresponding scoring results, to allow
+for external validation and reproducibility.
+"
+The Prompt Artists,Minsuk Chang,http://arxiv.org/pdf/2303.12253v1.pdf,2023-03-22,['cs.hc'],2303.12253v1.pdf,"  This paper examines the art practices, artwork, and motivations of prolific
+users of the latest generation of text-to-image models. Through interviews,
+observations, and a user survey, we present a sampling of the artistic styles
+and describe the developed community of practice around generative AI. We find
+that: 1) the text prompt and the resulting image can be considered collectively
+as an art piece prompts as art and 2) prompt templates (prompts with ``slots''
+for others to fill in with their own words) are developed to create generative
+art styles. We discover that the value placed by this community on unique
+outputs leads to artists seeking specialized vocabulary to produce distinctive
+art pieces (e.g., by reading architectural blogs to find phrases to describe
+images). We also find that some artists use ""glitches"" in the model that can be
+turned into artistic styles of their own right. From these findings, we outline
+specific implications for design regarding future prompting and image editing
+options.
+"
+WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation,Jongheon Jeong,http://arxiv.org/pdf/2303.14814v1.pdf,2023-03-26,"['cs.cv', 'cs.ai', 'cs.cl']",2303.14814v1.pdf,"  Visual anomaly classification and segmentation are vital for automating
+industrial quality inspection. The focus of prior research in the field has
+been on training custom models for each quality inspection task, which requires
+task-specific images and annotation. In this paper we move away from this
+regime, addressing zero-shot and few-normal-shot anomaly classification and
+segmentation. Recently CLIP, a vision-language model, has shown revolutionary
+generality with competitive zero-/few-shot performance in comparison to
+full-supervision. But CLIP falls short on anomaly classification and
+segmentation tasks. Hence, we propose window-based CLIP (WinCLIP) with (1) a
+compositional ensemble on state words and prompt templates and (2) efficient
+extraction and aggregation of window/patch/image-level features aligned with
+text. We also propose its few-normal-shot extension WinCLIP+, which uses
+complementary information from normal images. In MVTec-AD (and VisA), without
+further tuning, WinCLIP achieves 91.8%/85.1% (78.1%/79.6%) AUROC in zero-shot
+anomaly classification and segmentation while WinCLIP+ does 93.1%/95.2%
+(83.8%/96.4%) in 1-normal-shot, surpassing state-of-the-art by large margins.
+"
+MetricPrompt: Prompting Model as a Relevance Metric for Few-shot Text  Classification,Hongyuan Dong,http://arxiv.org/pdf/2306.08892v1.pdf,2023-06-15,['cs.cl'],2306.08892v1.pdf,"  Prompting methods have shown impressive performance in a variety of text
+mining tasks and applications, especially few-shot ones. Despite the promising
+prospects, the performance of prompting model largely depends on the design of
+prompt template and verbalizer. In this work, we propose MetricPrompt, which
+eases verbalizer design difficulty by reformulating few-shot text
+classification task into text pair relevance estimation task. MetricPrompt
+adopts prompting model as the relevance metric, further bridging the gap
+between Pre-trained Language Model's (PLM) pre-training objective and text
+classification task, making possible PLM's smooth adaption. Taking a training
+sample and a query one simultaneously, MetricPrompt captures cross-sample
+relevance information for accurate relevance estimation. We conduct experiments
+on three widely used text classification datasets across four few-shot
+settings. Results show that MetricPrompt outperforms manual verbalizer and
+other automatic verbalizer design methods across all few-shot settings,
+achieving new state-of-the-art (SOTA) performance.
+"
+TrustGPT: A Benchmark for Trustworthy and Responsible Large Language  Models,Yue Huang,http://arxiv.org/pdf/2306.11507v1.pdf,2023-06-20,"['cs.cl', 'cs.ai']",2306.11507v1.pdf,"  Large Language Models (LLMs) such as ChatGPT, have gained significant
+attention due to their impressive natural language processing capabilities. It
+is crucial to prioritize human-centered principles when utilizing these models.
+Safeguarding the ethical and moral compliance of LLMs is of utmost importance.
+However, individual ethical issues have not been well studied on the latest
+LLMs. Therefore, this study aims to address these gaps by introducing a new
+benchmark -- TrustGPT. TrustGPT provides a comprehensive evaluation of LLMs in
+three crucial areas: toxicity, bias, and value-alignment. Initially, TrustGPT
+examines toxicity in language models by employing toxic prompt templates
+derived from social norms. It then quantifies the extent of bias in models by
+measuring quantifiable toxicity values across different groups. Lastly,
+TrustGPT assesses the value of conversation generation models from both active
+value-alignment and passive value-alignment tasks. Through the implementation
+of TrustGPT, this research aims to enhance our understanding of the performance
+of conversation generation models and promote the development of language
+models that are more ethical and socially responsible.
+"
+DAPrompt: Deterministic Assumption Prompt Learning for Event Causality  Identification,Wei Xiang,http://arxiv.org/pdf/2307.09813v1.pdf,2023-07-19,['cs.cl'],2307.09813v1.pdf,"  Event Causality Identification (ECI) aims at determining whether there is a
+causal relation between two event mentions. Conventional prompt learning
+designs a prompt template to first predict an answer word and then maps it to
+the final decision. Unlike conventional prompts, we argue that predicting an
+answer word may not be a necessary prerequisite for the ECI task. Instead, we
+can first make a deterministic assumption on the existence of causal relation
+between two events and then evaluate its rationality to either accept or reject
+the assumption. The design motivation is to try the most utilization of the
+encyclopedia-like knowledge embedded in a pre-trained language model. In light
+of such considerations, we propose a deterministic assumption prompt learning
+model, called DAPrompt, for the ECI task. In particular, we design a simple
+deterministic assumption template concatenating with the input event pair,
+which includes two masks as predicted events' tokens. We use the probabilities
+of predicted events to evaluate the assumption rationality for the final event
+causality decision. Experiments on the EventStoryLine corpus and
+Causal-TimeBank corpus validate our design objective in terms of significant
+performance improvements over the state-of-the-art algorithms.
+"
+DiffuGen: Adaptable Approach for Generating Labeled Image Datasets using  Stable Diffusion Models,Michael Shenoda,http://arxiv.org/pdf/2309.00248v1.pdf,2023-09-01,"['cs.cv', 'cs.ai']",2309.00248v1.pdf,"  Generating high-quality labeled image datasets is crucial for training
+accurate and robust machine learning models in the field of computer vision.
+However, the process of manually labeling real images is often time-consuming
+and costly. To address these challenges associated with dataset generation, we
+introduce ""DiffuGen,"" a simple and adaptable approach that harnesses the power
+of stable diffusion models to create labeled image datasets efficiently. By
+leveraging stable diffusion models, our approach not only ensures the quality
+of generated datasets but also provides a versatile solution for label
+generation. In this paper, we present the methodology behind DiffuGen, which
+combines the capabilities of diffusion models with two distinct labeling
+techniques: unsupervised and supervised. Distinctively, DiffuGen employs prompt
+templating for adaptable image generation and textual inversion to enhance
+diffusion model capabilities.
+"
+Mitigating Word Bias in Zero-shot Prompt-based Classifiers,Adian Liusie,http://arxiv.org/pdf/2309.04992v1.pdf,2023-09-10,['cs.cl'],2309.04992v1.pdf,"  Prompt-based classifiers are an attractive approach for zero-shot
+classification. However, the precise choice of the prompt template and label
+words can largely influence performance, with semantically equivalent settings
+often showing notable performance difference. This discrepancy can be partly
+attributed to word biases, where the classifier may be biased towards classes.
+To address this problem, it is possible to optimise classification thresholds
+on a labelled data set, however, this mitigates some of the advantages of
+prompt-based classifiers. This paper instead approaches this problem by
+examining the expected marginal probabilities of the classes. Here,
+probabilities are reweighted to have a uniform prior over classes, in an
+unsupervised fashion. Further, we draw a theoretical connection between the
+class priors and the language models' word prior, and offer the ability to set
+a threshold in a zero-resource fashion. We show that matching class priors
+correlates strongly with the oracle upper bound performance and demonstrate
+large consistent performance gains for prompt settings over a range of NLP
+tasks.
+"
+Prompt-Enhanced Self-supervised Representation Learning for Remote  Sensing Image Understanding,Mingming Zhang,http://arxiv.org/pdf/2310.00022v1.pdf,2023-09-28,['cs.cv'],2310.00022v1.pdf,"  Learning representations through self-supervision on a large-scale, unlabeled
+dataset has proven to be highly effective for understanding diverse images,
+such as those used in remote sensing image analysis. However, remote sensing
+images often have complex and densely populated scenes, with multiple land
+objects and no clear foreground objects. This intrinsic property can lead to
+false positive pairs in contrastive learning, or missing contextual information
+in reconstructive learning, which can limit the effectiveness of existing
+self-supervised learning methods. To address these problems, we propose a
+prompt-enhanced self-supervised representation learning method that uses a
+simple yet efficient pre-training pipeline. Our approach involves utilizing
+original image patches as a reconstructive prompt template, and designing a
+prompt-enhanced generative branch that provides contextual information through
+semantic consistency constraints. We collected a dataset of over 1.28 million
+remote sensing images that is comparable to the popular ImageNet dataset, but
+without specific temporal or geographical constraints. Our experiments show
+that our method outperforms fully supervised learning models and
+state-of-the-art self-supervised learning methods on various downstream tasks,
+including land cover classification, semantic segmentation, object detection,
+and instance segmentation. These results demonstrate that our approach learns
+impressive remote sensing representations with high generalization and
+transferability.
+"
+LLM4DV: Using Large Language Models for Hardware Test Stimuli Generation,Zixi Zhang,http://arxiv.org/pdf/2310.04535v1.pdf,2023-10-06,"['cs.lg', 'cs.ar']",2310.04535v1.pdf,"  Test stimuli generation has been a crucial but labor-intensive task in
+hardware design verification. In this paper, we revolutionize this process by
+harnessing the power of large language models (LLMs) and present a novel
+benchmarking framework, LLM4DV. This framework introduces a prompt template for
+interactively eliciting test stimuli from the LLM, along with four innovative
+prompting improvements to support the pipeline execution and further enhance
+its performance. We compare LLM4DV to traditional constrained-random testing
+(CRT), using three self-designed design-under-test (DUT) modules. Experiments
+demonstrate that LLM4DV excels in efficiently handling straightforward DUT
+scenarios, leveraging its ability to employ basic mathematical reasoning and
+pre-trained knowledge. While it exhibits reduced efficiency in complex task
+settings, it still outperforms CRT in relative terms. The proposed framework
+and the DUT modules used in our experiments will be open-sourced upon
+publication.
+"
+Estimating Uncertainty in Multimodal Foundation Models using Public  Internet Data,Shiladitya Dutta,http://arxiv.org/pdf/2310.09926v1.pdf,2023-10-15,['cs.ai'],2310.09926v1.pdf,"  Foundation models are trained on vast amounts of data at scale using
+self-supervised learning, enabling adaptation to a wide range of downstream
+tasks. At test time, these models exhibit zero-shot capabilities through which
+they can classify previously unseen (user-specified) categories. In this paper,
+we address the problem of quantifying uncertainty in these zero-shot
+predictions. We propose a heuristic approach for uncertainty estimation in
+zero-shot settings using conformal prediction with web data. Given a set of
+classes at test time, we conduct zero-shot classification with CLIP-style
+models using a prompt template, e.g., ""an image of a <category>"", and use the
+same template as a search query to source calibration data from the open web.
+Given a web-based calibration set, we apply conformal prediction with a novel
+conformity score that accounts for potential errors in retrieved web data. We
+evaluate the utility of our proposed method in Biomedical foundation models;
+our preliminary results show that web-based conformal prediction sets achieve
+the target coverage with satisfactory efficiency on a variety of biomedical
+datasets.
+"
+Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring  Fine-Grained Relevance Labels,Honglei Zhuang,http://arxiv.org/pdf/2310.14122v2.pdf,2023-10-21,['cs.ir'],2310.14122v2.pdf,"  Zero-shot text rankers powered by recent LLMs achieve remarkable ranking
+performance by simply prompting. Existing prompts for pointwise LLM rankers
+mostly ask the model to choose from binary relevance labels like ""Yes"" and
+""No"". However, the lack of intermediate relevance label options may cause the
+LLM to provide noisy or biased answers for documents that are partially
+relevant to the query. We propose to incorporate fine-grained relevance labels
+into the prompt for LLM rankers, enabling them to better differentiate among
+documents with different levels of relevance to the query and thus derive a
+more accurate ranking. We study two variants of the prompt template, coupled
+with different numbers of relevance levels. Our experiments on 8 BEIR data sets
+show that adding fine-grained relevance labels significantly improves the
+performance of LLM rankers.
+"
+"Large Language Models can Share Images, Too!",Young-Jun Lee,http://arxiv.org/pdf/2310.14804v1.pdf,2023-10-23,"['cs.cv', 'cs.ai', 'cs.cl']",2310.14804v1.pdf,"  This paper explores the image-sharing capability of Large Language Models
+(LLMs), such as InstructGPT, ChatGPT, and GPT-4, in a zero-shot setting,
+without the help of visual foundation models. Inspired by the two-stage process
+of image-sharing in human dialogues, we propose a two-stage framework that
+allows LLMs to predict potential image-sharing turns and generate related image
+descriptions using our effective restriction-based prompt template. With
+extensive experiments, we unlock the \textit{image-sharing} capability of LLMs
+in zero-shot prompting, with GPT-4 achieving the best performance.
+Additionally, we uncover the emergent \textit{image-sharing} ability in
+zero-shot prompting, demonstrating the effectiveness of restriction-based
+prompts in both stages of our framework. Based on this framework, we augment
+the PhotoChat dataset with images generated by Stable Diffusion at predicted
+turns, namely PhotoChat++. To our knowledge, this is the first study to assess
+the image-sharing ability of LLMs in a zero-shot setting without visual
+foundation models. The source code and the dataset will be released after
+publication.
+"
+KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization  for Relation Extraction,Xiang Chen,http://arxiv.org/pdf/2104.07650v7.pdf,2021-04-15,"['cs.cl', 'cs.ai', 'cs.ir', 'cs.lg']",2104.07650v7.pdf,"  Recently, prompt-tuning has achieved promising results for specific few-shot
+classification tasks. The core idea of prompt-tuning is to insert text pieces
+(i.e., templates) into the input and transform a classification task into a
+masked language modeling problem. However, for relation extraction, determining
+an appropriate prompt template requires domain expertise, and it is cumbersome
+and time-consuming to obtain a suitable label word. Furthermore, there exists
+abundant semantic and prior knowledge among the relation labels that cannot be
+ignored. To this end, we focus on incorporating knowledge among relation labels
+into prompt-tuning for relation extraction and propose a Knowledge-aware
+Prompt-tuning approach with synergistic optimization (KnowPrompt).
+Specifically, we inject latent knowledge contained in relation labels into
+prompt construction with learnable virtual type words and answer words. Then,
+we synergistically optimize their representation with structured constraints.
+Extensive experimental results on five datasets with standard and low-resource
+settings demonstrate the effectiveness of our approach. Our code and datasets
+are available in https://github.com/zjunlp/KnowPrompt for reproducibility.
+"
+Prompt-based Zero-shot Relation Extraction with Semantic Knowledge  Augmentation,Jiaying Gong,http://arxiv.org/pdf/2112.04539v2.pdf,2021-12-08,['cs.cl'],2112.04539v2.pdf,"  In relation triplet extraction (RTE), recognizing unseen (new) relations for
+which there are no training instances is a challenging task. Efforts have been
+made to recognize unseen relations based on question-answering models or
+relation descriptions. However, these approaches miss the semantic information
+about connections between seen and unseen relations. In this paper, We propose
+a prompt-based model with semantic knowledge augmentation (ZS-SKA) to recognize
+unseen relations under the zero-shot setting. We present a new word-level
+analogy-based sentence translation rule and generate augmented instances with
+unseen relations from instances with seen relations using that new rule. We
+design prompts with weighted virtual label construction based on an external
+knowledge graph to integrate semantic knowledge information learned from seen
+relations. Instead of using the actual label sets in the prompt template, we
+construct weighted virtual label words. We learn the representations of both
+seen and unseen relations with augmented instances and prompts. We then
+calculate the distance between the generated representations using prototypical
+networks to predict unseen relations. Extensive experiments conducted on three
+public datasets FewRel, Wiki-ZSL, and NYT, show that ZS-SKA outperforms
+state-of-the-art methods under the zero-shot scenarios. Our experimental
+results also demonstrate the effectiveness and robustness of ZS-SKA.
+"
+DynaMaR: Dynamic Prompt with Mask Token Representation,Xiaodi Sun,http://arxiv.org/pdf/2206.02982v1.pdf,2022-06-07,"['cs.cl', 'cs.lg']",2206.02982v1.pdf,"  Recent research has shown that large language models pretrained using
+unsupervised approaches can achieve significant performance improvement on many
+downstream tasks. Typically when adapting these language models to downstream
+tasks, like a classification or regression task, we employ a fine-tuning
+paradigm in which the sentence representation from the language model is input
+to a task-specific head; the model is then fine-tuned end-to-end. However, with
+the emergence of models like GPT-3, prompt-based fine-tuning has been proven to
+be a successful approach for few-shot tasks. Inspired by this work, we study
+discrete prompt technologies in practice. There are two issues that arise with
+the standard prompt approach. First, it can overfit on the prompt template.
+Second, it requires manual effort to formulate the downstream task as a
+language model problem. In this paper, we propose an improvement to
+prompt-based fine-tuning that addresses these two issues. We refer to our
+approach as DynaMaR -- Dynamic Prompt with Mask Token Representation. Results
+show that DynaMaR can achieve an average improvement of 10% in few-shot
+settings and improvement of 3.7% in data-rich settings over the standard
+fine-tuning approach on four e-commerce applications.
+"
+Rethinking the Event Coding Pipeline with Prompt Entailment,Clément Lefebvre,http://arxiv.org/pdf/2210.05257v2.pdf,2022-10-11,"['cs.cl', 'cs.hc', 'cs.lg']",2210.05257v2.pdf,"  For monitoring crises, political events are extracted from the news. The
+large amount of unstructured full-text event descriptions makes a case-by-case
+analysis unmanageable, particularly for low-resource humanitarian aid
+organizations. This creates a demand to classify events into event types, a
+task referred to as event coding. Typically, domain experts craft an event type
+ontology, annotators label a large dataset and technical experts develop a
+supervised coding system. In this work, we propose PR-ENT, a new event coding
+approach that is more flexible and resource-efficient, while maintaining
+competitive accuracy: first, we extend an event description such as ""Military
+injured two civilians'' by a template, e.g. ""People were [Z]"" and prompt a
+pre-trained (cloze) language model to fill the slot Z. Second, we select answer
+candidates Z* = {""injured'', ""hurt""...} by treating the event description as
+premise and the filled templates as hypothesis in a textual entailment task.
+This allows domain experts to draft the codebook directly as labeled prompts
+and interpretable answer candidates. This human-in-the-loop process is guided
+by our interactive codebook design tool. We evaluate PR-ENT in several
+robustness checks: perturbing the event description and prompt template,
+restricting the vocabulary and removing contextual information.
+"
+Visual Prompting for Adversarial Robustness,Aochuan Chen,http://arxiv.org/pdf/2210.06284v4.pdf,2022-10-12,"['cs.cv', 'cs.cr', 'cs.lg']",2210.06284v4.pdf,"  In this work, we leverage visual prompting (VP) to improve adversarial
+robustness of a fixed, pre-trained model at testing time. Compared to
+conventional adversarial defenses, VP allows us to design universal (i.e.,
+data-agnostic) input prompting templates, which have plug-and-play capabilities
+at testing time to achieve desired model performance without introducing much
+computation overhead. Although VP has been successfully applied to improving
+model generalization, it remains elusive whether and how it can be used to
+defend against adversarial attacks. We investigate this problem and show that
+the vanilla VP approach is not effective in adversarial defense since a
+universal input prompt lacks the capacity for robust learning against
+sample-specific adversarial perturbations. To circumvent it, we propose a new
+VP method, termed Class-wise Adversarial Visual Prompting (C-AVP), to generate
+class-wise visual prompts so as to not only leverage the strengths of ensemble
+prompts but also optimize their interrelations to improve model robustness. Our
+experiments show that C-AVP outperforms the conventional VP method, with 2.1X
+standard accuracy gain and 2X robust accuracy gain. Compared to classical
+test-time defenses, C-AVP also yields a 42X inference time speedup.
+"
+Continuous Prompt Tuning Based Textual Entailment Model for E-commerce  Entity Typing,Yibo Wang,http://arxiv.org/pdf/2211.02483v1.pdf,2022-11-04,['cs.cl'],2211.02483v1.pdf,"  The explosion of e-commerce has caused the need for processing and analysis
+of product titles, like entity typing in product titles. However, the rapid
+activity in e-commerce has led to the rapid emergence of new entities, which is
+difficult to be solved by general entity typing. Besides, product titles in
+e-commerce have very different language styles from text data in general
+domain. In order to handle new entities in product titles and address the
+special language styles problem of product titles in e-commerce domain, we
+propose our textual entailment model with continuous prompt tuning based
+hypotheses and fusion embeddings for e-commerce entity typing. First, we
+reformulate the entity typing task into a textual entailment problem to handle
+new entities that are not present during training. Second, we design a model to
+automatically generate textual entailment hypotheses using a continuous prompt
+tuning method, which can generate better textual entailment hypotheses without
+manual design. Third, we utilize the fusion embeddings of BERT embedding and
+CharacterBERT embedding with a two-layer MLP classifier to solve the problem
+that the language styles of product titles in e-commerce are different from
+that of general domain. To analyze the effect of each contribution, we compare
+the performance of entity typing and textual entailment model, and conduct
+ablation studies on continuous prompt tuning and fusion embeddings. We also
+evaluate the impact of different prompt template initialization for the
+continuous prompt tuning. We show our proposed model improves the average F1
+score by around 2% compared to the baseline BERT entity typing model.
+"
+Multi-label Few-shot ICD Coding as Autoregressive Generation with Prompt,Zhichao Yang,http://arxiv.org/pdf/2211.13813v2.pdf,2022-11-24,"['cs.cl', 'cs.ai']",2211.13813v2.pdf,"  Automatic International Classification of Diseases (ICD) coding aims to
+assign multiple ICD codes to a medical note with an average of 3,000+ tokens.
+This task is challenging due to the high-dimensional space of multi-label
+assignment (155,000+ ICD code candidates) and the long-tail challenge - Many
+ICD codes are infrequently assigned yet infrequent ICD codes are important
+clinically. This study addresses the long-tail challenge by transforming this
+multi-label classification task into an autoregressive generation task.
+Specifically, we first introduce a novel pretraining objective to generate free
+text diagnoses and procedure using the SOAP structure, the medical logic
+physicians use for note documentation. Second, instead of directly predicting
+the high dimensional space of ICD codes, our model generates the lower
+dimension of text descriptions, which then infer ICD codes. Third, we designed
+a novel prompt template for multi-label classification. We evaluate our
+Generation with Prompt model with the benchmark of all code assignment
+(MIMIC-III-full) and few shot ICD code assignment evaluation benchmark
+(MIMIC-III-few). Experiments on MIMIC-III-few show that our model performs with
+a marco F1 30.2, which substantially outperforms the previous MIMIC-III-full
+SOTA model (marco F1 4.3) and the model specifically designed for few/zero shot
+setting (marco F1 18.7). Finally, we design a novel ensemble learner, a cross
+attention reranker with prompts, to integrate previous SOTA and our best
+few-shot coding predictions. Experiments on MIMIC-III-full show that our
+ensemble learner substantially improves both macro and micro F1, from 10.4 to
+14.6 and from 58.2 to 59.1, respectively.
+"
+LabelPrompt: Effective Prompt-based Learning for Relation Classification,Wenjie Zhang,http://arxiv.org/pdf/2302.08068v2.pdf,2023-02-16,"['cs.cl', 'cs.ai', 'cs.ir', 'cs.lg']",2302.08068v2.pdf,"  Recently, prompt-based learning has gained popularity across many natural
+language processing (NLP) tasks by reformulating them into a cloze-style format
+to better align pre-trained language models (PLMs) with downstream tasks.
+However, applying this approach to relation classification poses unique
+challenges. Specifically, associating natural language words that fill the
+masked token with semantic relation labels (\textit{e.g.}
+\textit{``org:founded\_by}'') is difficult. To address this challenge, this
+paper presents a novel prompt-based learning method, namely LabelPrompt, for
+the relation classification task. Motivated by the intuition to ``GIVE MODEL
+CHOICES!'', we first define additional tokens to represent relation labels,
+which regard these tokens as the verbaliser with semantic initialisation and
+explicitly construct them with a prompt template method. Then, to mitigate
+inconsistency between predicted relations and given entities, we implement an
+entity-aware module with contrastive learning. Last, we conduct an attention
+query strategy within the self-attention layer to differentiates prompt tokens
+and sequence tokens. Together, these strategies enhance the adaptability of
+prompt-based learning, especially when only small labelled datasets is
+available. Comprehensive experiments on benchmark datasets demonstrate the
+superiority of our method, particularly in the few-shot scenario.
+"
+Adapting Prompt for Few-shot Table-to-Text Generation,Zhixin Guo,http://arxiv.org/pdf/2302.12468v2.pdf,2023-02-24,['cs.cl'],2302.12468v2.pdf,"  Pretrained language models (PLMs) have made remarkable progress in
+table-to-text generation tasks. However, the lack of domain-specific knowledge
+makes it challenging to bridge the topological gap between tabular data and
+text, especially in real-world applications with limited resources. To mitigate
+the limitation of insufficient labeled data, we propose a novel framework:
+Adapt-Prompt-to-Generate (AdaPTGen). The core insight of AdaPTGen is to adapt
+prompt templates of domain-specific knowledge into the model, which brings at
+least three benefits: (1) it injects representation of normal table-related
+descriptions to bridge the topological gap between tabular data and texts; (2)
+it enables us to use large amounts of unlabeled domain-specific knowledge
+fully, which can alleviate the PLMs' inherent shortcomings of lacking domain
+knowledge; (3) it allows us to design various tasks to explore the
+domain-specific knowledge. Extensive experiments and analyses are conducted on
+three open-domain few-shot natural language generation (NLG) data sets: Humans,
+Songs, and Books. Compared to previous state-of-the-art approaches, our model
+achieves superior performance in terms of both fluency and accuracy.
+"
+Model-tuning Via Prompts Makes NLP Models Adversarially Robust,Mrigank Raman,http://arxiv.org/pdf/2303.07320v1.pdf,2023-03-13,"['cs.cl', 'cs.lg']",2303.07320v1.pdf,"  In recent years, NLP practitioners have converged on the following practice:
+(i) import an off-the-shelf pretrained (masked) language model; (ii) append a
+multilayer perceptron atop the CLS token's hidden representation (with randomly
+initialized weights); and (iii) fine-tune the entire model on a downstream task
+(MLP). This procedure has produced massive gains on standard NLP benchmarks,
+but these models remain brittle, even to mild adversarial perturbations, such
+as word-level synonym substitutions. In this work, we demonstrate surprising
+gains in adversarial robustness enjoyed by Model-tuning Via Prompts (MVP), an
+alternative method of adapting to downstream tasks. Rather than modifying the
+model (by appending an MLP head), MVP instead modifies the input (by appending
+a prompt template). Across three classification datasets, MVP improves
+performance against adversarial word-level synonym substitutions by an average
+of 8% over standard methods and even outperforms adversarial training-based
+state-of-art defenses by 3.5%. By combining MVP with adversarial training, we
+achieve further improvements in robust accuracy while maintaining clean
+accuracy. Finally, we conduct ablations to investigate the mechanism underlying
+these gains. Notably, we find that the main causes of vulnerability of MLP can
+be attributed to the misalignment between pre-training and fine-tuning tasks,
+and the randomly initialized MLP parameters. Code is available at
+https://github.com/acmi-lab/mvp
+"
+"PromptAid: Prompt Exploration, Perturbation, Testing and Iteration using  Visual Analytics for Large Language Models",Aditi Mishra,http://arxiv.org/pdf/2304.01964v2.pdf,2023-04-04,['cs.hc'],2304.01964v2.pdf,"  Large Language Models (LLMs) have gained widespread popularity due to their
+ability to perform ad-hoc Natural Language Processing (NLP) tasks with a simple
+natural language prompt. Part of the appeal for LLMs is their approachability
+to the general public, including individuals with no prior technical experience
+in NLP techniques. However, natural language prompts can vary significantly in
+terms of their linguistic structure, context, and other semantics. Modifying
+one or more of these aspects can result in significant differences in task
+performance. Non-expert users may find it challenging to identify the changes
+needed to improve a prompt, especially when they lack domain-specific knowledge
+and lack appropriate feedback. To address this challenge, we present PromptAid,
+a visual analytics system designed to interactively create, refine, and test
+prompts through exploration, perturbation, testing, and iteration. PromptAid
+uses multiple, coordinated visualizations which allow users to improve prompts
+by using the three strategies: keyword perturbations, paraphrasing
+perturbations, and obtaining the best set of in-context few-shot examples.
+PromptAid was designed through an iterative prototyping process involving NLP
+experts and was evaluated through quantitative and qualitative assessments for
+LLMs. Our findings indicate that PromptAid helps users to iterate over prompt
+template alterations with less cognitive overhead, generate diverse prompts
+with help of recommendations, and analyze the performance of the generated
+prompts while surpassing existing state-of-the-art prompting interfaces in
+performance.
+"
+FashionSAP: Symbols and Attributes Prompt for Fine-grained Fashion  Vision-Language Pre-training,Yunpeng Han,http://arxiv.org/pdf/2304.05051v1.pdf,2023-04-11,"['cs.cv', 'cs.cl']",2304.05051v1.pdf,"  Fashion vision-language pre-training models have shown efficacy for a wide
+range of downstream tasks. However, general vision-language pre-training models
+pay less attention to fine-grained domain features, while these features are
+important in distinguishing the specific domain tasks from general tasks. We
+propose a method for fine-grained fashion vision-language pre-training based on
+fashion Symbols and Attributes Prompt (FashionSAP) to model fine-grained
+multi-modalities fashion attributes and characteristics. Firstly, we propose
+the fashion symbols, a novel abstract fashion concept layer, to represent
+different fashion items and to generalize various kinds of fine-grained fashion
+features, making modelling fine-grained attributes more effective. Secondly,
+the attributes prompt method is proposed to make the model learn specific
+attributes of fashion items explicitly. We design proper prompt templates
+according to the format of fashion data. Comprehensive experiments are
+conducted on two public fashion benchmarks, i.e., FashionGen and FashionIQ, and
+FashionSAP gets SOTA performances for four popular fashion tasks. The ablation
+study also shows the proposed abstract fashion symbols, and the attribute
+prompt method enables the model to acquire fine-grained semantics in the
+fashion domain effectively. The obvious performance gains from FashionSAP
+provide a new baseline for future fashion task research.
+"
+"A study on Prompt Design, Advantages and Limitations of ChatGPT for Deep  Learning Program Repair",Jialun Cao,http://arxiv.org/pdf/2304.08191v1.pdf,2023-04-17,['cs.se'],2304.08191v1.pdf,"  ChatGPT has revolutionized many research and industrial fields. ChatGPT has
+shown great potential in software engineering to boost various traditional
+tasks such as program repair, code understanding, and code generation. However,
+whether automatic program repair (APR) applies to deep learning (DL) programs
+is still unknown. DL programs, whose decision logic is not explicitly encoded
+in the source code, have posed unique challenges to APR. While to repair DL
+programs, an APR approach needs to not only parse the source code syntactically
+but also needs to understand the code intention. With the best prior work, the
+performance of fault localization is still far less than satisfactory (only
+about 30\%). Therefore, in this paper, we explore ChatGPT's capability for DL
+program repair by asking three research questions. (1) Can ChatGPT debug DL
+programs effectively? (2) How can ChatGPT's repair performance be improved by
+prompting? (3) In which way can dialogue help facilitate the repair? On top of
+that, we categorize the common aspects useful for prompt design for DL program
+repair. Also, we propose various prompt templates to facilitate the performance
+and summarize the advantages and disadvantages of ChatGPT's abilities such as
+detecting bad code smell, code refactoring, and detecting API
+misuse/deprecation.
+"
+Prompt-Learning for Cross-Lingual Relation Extraction,Chiaming Hsu,http://arxiv.org/pdf/2304.10354v1.pdf,2023-04-20,['cs.cl'],2304.10354v1.pdf,"  Relation Extraction (RE) is a crucial task in Information Extraction, which
+entails predicting relationships between entities within a given sentence.
+However, extending pre-trained RE models to other languages is challenging,
+particularly in real-world scenarios where Cross-Lingual Relation Extraction
+(XRE) is required. Despite recent advancements in Prompt-Learning, which
+involves transferring knowledge from Multilingual Pre-trained Language Models
+(PLMs) to diverse downstream tasks, there is limited research on the effective
+use of multilingual PLMs with prompts to improve XRE. In this paper, we present
+a novel XRE algorithm based on Prompt-Tuning, referred to as Prompt-XRE. To
+evaluate its effectiveness, we design and implement several prompt templates,
+including hard, soft, and hybrid prompts, and empirically test their
+performance on competitive multilingual PLMs, specifically mBART. Our extensive
+experiments, conducted on the low-resource ACE05 benchmark across multiple
+languages, demonstrate that our Prompt-XRE algorithm significantly outperforms
+both vanilla multilingual PLMs and other existing models, achieving
+state-of-the-art performance in XRE. To further show the generalization of our
+Prompt-XRE on larger data scales, we construct and release a new XRE dataset-
+WMT17-EnZh XRE, containing 0.9M English-Chinese pairs extracted from WMT 2017
+parallel corpus. Experiments on WMT17-EnZh XRE also show the effectiveness of
+our Prompt-XRE against other competitive baselines. The code and newly
+constructed dataset are freely available at
+\url{https://github.com/HSU-CHIA-MING/Prompt-XRE}.
+"
+CitePrompt: Using Prompts to Identify Citation Intent in Scientific  Papers,Avishek Lahiri,http://arxiv.org/pdf/2304.12730v2.pdf,2023-04-25,['cs.cl'],2304.12730v2.pdf,"  Citations in scientific papers not only help us trace the intellectual
+lineage but also are a useful indicator of the scientific significance of the
+work. Citation intents prove beneficial as they specify the role of the
+citation in a given context. In this paper, we present CitePrompt, a framework
+which uses the hitherto unexplored approach of prompt-based learning for
+citation intent classification. We argue that with the proper choice of the
+pretrained language model, the prompt template, and the prompt verbalizer, we
+can not only get results that are better than or comparable to those obtained
+with the state-of-the-art methods but also do it with much less exterior
+information about the scientific document. We report state-of-the-art results
+on the ACL-ARC dataset, and also show significant improvement on the SciCite
+dataset over all baseline models except one. As suitably large labelled
+datasets for citation intent classification can be quite hard to find, in a
+first, we propose the conversion of this task to the few-shot and zero-shot
+settings. For the ACL-ARC dataset, we report a 53.86% F1 score for the
+zero-shot setting, which improves to 63.61% and 66.99% for the 5-shot and
+10-shot settings, respectively.
+"
+Don't Stop Pretraining? Make Prompt-based Fine-tuning Powerful Learner,Zhengxiang Shi,http://arxiv.org/pdf/2305.01711v4.pdf,2023-05-02,['cs.cl'],2305.01711v4.pdf,"  Language models (LMs) trained on vast quantities of unlabelled data have
+greatly advanced the field of natural language processing (NLP). In this study,
+we re-visit the widely accepted notion in NLP that continued pre-training LMs
+on task-related texts improves the performance of fine-tuning (FT) in
+downstream tasks. Through experiments on eight single-sentence tasks and eight
+sentence-pair tasks in both semi-supervised and fully-supervised settings, we
+find that conventional continued pre-training does not consistently provide
+benefits and can even be detrimental for sentence-pair tasks or when
+prompt-based FT is used. To tackle these issues, we propose Prompt-based
+Continued Pre-training (PCP), which combines the idea of instruction tuning
+with conventional continued pre-training. Our approach aims to improve the
+performance of prompt-based FT by presenting both task-related texts and prompt
+templates to LMs through unsupervised pre-training objectives before
+fine-tuning for the target task. Our empirical evaluations on 21 benchmarks
+demonstrate that the PCP consistently improves the performance of
+state-of-the-art prompt-based FT approaches (up to 20.1% absolute) in both
+semi-supervised and fully-supervised settings, even with only hundreds of
+unlabelled examples. Additionally, prompt-based FT with the PCP outperforms
+state-of-the-art semi-supervised approaches with greater simplicity,
+eliminating the need for an iterative process and extra data augmentation. Our
+further analysis explores the performance lower bound of the PCP and reveals
+that the advantages of PCP persist across different sizes of models and
+datasets.
+"
+Large Language Models are Zero-Shot Rankers for Recommender Systems,Yupeng Hou,http://arxiv.org/pdf/2305.08845v1.pdf,2023-05-15,"['cs.ir', 'cs.cl']",2305.08845v1.pdf,"  Recently, large language models (LLMs) (e.g. GPT-4) have demonstrated
+impressive general-purpose task-solving abilities, including the potential to
+approach recommendation tasks. Along this line of research, this work aims to
+investigate the capacity of LLMs that act as the ranking model for recommender
+systems. To conduct our empirical study, we first formalize the recommendation
+problem as a conditional ranking task, considering sequential interaction
+histories as conditions and the items retrieved by the candidate generation
+model as candidates. We adopt a specific prompting approach to solving the
+ranking task by LLMs: we carefully design the prompting template by including
+the sequential interaction history, the candidate items, and the ranking
+instruction. We conduct extensive experiments on two widely-used datasets for
+recommender systems and derive several key findings for the use of LLMs in
+recommender systems. We show that LLMs have promising zero-shot ranking
+abilities, even competitive to or better than conventional recommendation
+models on candidates retrieved by multiple candidate generators. We also
+demonstrate that LLMs struggle to perceive the order of historical interactions
+and can be affected by biases like position bias, while these issues can be
+alleviated via specially designed prompting and bootstrapping strategies. The
+code to reproduce this work is available at
+https://github.com/RUCAIBox/LLMRank.
+"
+TEPrompt: Task Enlightenment Prompt Learning for Implicit Discourse  Relation Recognition,Wei Xiang,http://arxiv.org/pdf/2305.10866v1.pdf,2023-05-18,['cs.cl'],2305.10866v1.pdf,"  Implicit Discourse Relation Recognition (IDRR) aims at classifying the
+relation sense between two arguments without an explicit connective. Recently,
+the ConnPrompt~\cite{Wei.X:et.al:2022:COLING} has leveraged the powerful prompt
+learning for IDRR based on the fusion of multi-prompt decisions from three
+different yet much similar connective prediction templates. Instead of
+multi-prompt ensembling, we propose to design auxiliary tasks with enlightened
+prompt learning for the IDRR task. Although an auxiliary task is not used to
+directly output final prediction, we argue that during the joint training some
+of its learned features can be useful to boost the main task. In light of such
+motivations, we propose a task enlightenment prompt learning model, called
+TEPrompt, to fuse learned features from three related tasks for IDRR. In
+particular, the TEPrompt contains three tasks, viz., Discourse Relation
+Recognition (DRR), Sense Semantics Classification (SSC) and Annotated
+Connective Prediction (ACP), each with a unique prompt template and an answer
+space. In the training phase, we jointly train three prompt learning tasks with
+shared argument representation. In the testing phase, we only take the DRR
+output with fused features as the final IDRR decision. Experiments with the
+same conditions have shown that the proposed TEPrompt outperforms the
+ConnPrompt. This can be attributed to the promoted decision features and
+language models benefited from joint-training of auxiliary tasks.
+"
+Prompting ChatGPT in MNER: Enhanced Multimodal Named Entity Recognition  with Auxiliary Refined Knowledge,Jinyuan Li,http://arxiv.org/pdf/2305.12212v2.pdf,2023-05-20,['cs.cl'],2305.12212v2.pdf,"  Multimodal Named Entity Recognition (MNER) on social media aims to enhance
+textual entity prediction by incorporating image-based clues. Existing studies
+mainly focus on maximizing the utilization of pertinent image information or
+incorporating external knowledge from explicit knowledge bases. However, these
+methods either neglect the necessity of providing the model with external
+knowledge, or encounter issues of high redundancy in the retrieved knowledge.
+In this paper, we present PGIM -- a two-stage framework that aims to leverage
+ChatGPT as an implicit knowledge base and enable it to heuristically generate
+auxiliary knowledge for more efficient entity prediction. Specifically, PGIM
+contains a Multimodal Similar Example Awareness module that selects suitable
+examples from a small number of predefined artificial samples. These examples
+are then integrated into a formatted prompt template tailored to the MNER and
+guide ChatGPT to generate auxiliary refined knowledge. Finally, the acquired
+knowledge is integrated with the original text and fed into a downstream model
+for further processing. Extensive experiments show that PGIM outperforms
+state-of-the-art methods on two classic MNER datasets and exhibits a stronger
+robustness and generalization capability.
+"
+"Paradigm Shift in Sustainability Disclosure Analysis: Empowering  Stakeholders with CHATREPORT, a Language Model-Based Tool",Jingwei Ni,http://arxiv.org/pdf/2306.15518v1.pdf,2023-06-27,['cs.cl'],2306.15518v1.pdf,"  This paper introduces a novel approach to enhance Large Language Models
+(LLMs) with expert knowledge to automate the analysis of corporate
+sustainability reports by benchmarking them against the Task Force for
+Climate-Related Financial Disclosures (TCFD) recommendations. Corporate
+sustainability reports are crucial in assessing organizations' environmental
+and social risks and impacts. However, analyzing these reports' vast amounts of
+information makes human analysis often too costly. As a result, only a few
+entities worldwide have the resources to analyze these reports, which could
+lead to a lack of transparency. While AI-powered tools can automatically
+analyze the data, they are prone to inaccuracies as they lack domain-specific
+expertise. This paper introduces a novel approach to enhance LLMs with expert
+knowledge to automate the analysis of corporate sustainability reports. We
+christen our tool CHATREPORT, and apply it in a first use case to assess
+corporate climate risk disclosures following the TCFD recommendations.
+CHATREPORT results from collaborating with experts in climate science, finance,
+economic policy, and computer science, demonstrating how domain experts can be
+involved in developing AI tools. We make our prompt templates, generated data,
+and scores available to the public to encourage transparency.
+"
+TIAM -- A Metric for Evaluating Alignment in Text-to-Image Generation,Paul Grimal,http://arxiv.org/pdf/2307.05134v1.pdf,2023-07-11,"['cs.cv', 'cs.ai', 'cs.cl', 'cs.lg']",2307.05134v1.pdf,"  The progress in the generation of synthetic images has made it crucial to
+assess their quality. While several metrics have been proposed to assess the
+rendering of images, it is crucial for Text-to-Image (T2I) models, which
+generate images based on a prompt, to consider additional aspects such as to
+which extent the generated image matches the important content of the prompt.
+Moreover, although the generated images usually result from a random starting
+point, the influence of this one is generally not considered. In this article,
+we propose a new metric based on prompt templates to study the alignment
+between the content specified in the prompt and the corresponding generated
+images. It allows us to better characterize the alignment in terms of the type
+of the specified objects, their number, and their color. We conducted a study
+on several recent T2I models about various aspects. An additional interesting
+result we obtained with our approach is that image quality can vary drastically
+depending on the latent noise used as a seed for the images. We also quantify
+the influence of the number of concepts in the prompt, their order as well as
+their (color) attributes. Finally, our method allows us to identify some latent
+seeds that produce better images than others, opening novel directions of
+research on this understudied topic.
+"
+LLM-FuncMapper: Function Identification for Interpreting Complex Clauses  in Building Codes via LLM,Zhe Zheng,http://arxiv.org/pdf/2308.08728v1.pdf,2023-08-17,"['cs.ai', 'cs.cl']",2308.08728v1.pdf,"  As a vital stage of automated rule checking (ARC), rule interpretation of
+regulatory texts requires considerable effort. However, interpreting regulatory
+clauses with implicit properties or complex computational logic is still
+challenging due to the lack of domain knowledge and limited expressibility of
+conventional logic representations. Thus, LLM-FuncMapper, an approach to
+identifying predefined functions needed to interpret various regulatory clauses
+based on the large language model (LLM), is proposed. First, by systematically
+analysis of building codes, a series of atomic functions are defined to capture
+shared computational logics of implicit properties and complex constraints,
+creating a database of common blocks for interpreting regulatory clauses. Then,
+a prompt template with the chain of thought is developed and further enhanced
+with a classification-based tuning strategy, to enable common LLMs for
+effective function identification. Finally, the proposed approach is validated
+with statistical analysis, experiments, and proof of concept. Statistical
+analysis reveals a long-tail distribution and high expressibility of the
+developed function database, with which almost 100% of computer-processible
+clauses can be interpreted and represented as computer-executable codes.
+Experiments show that LLM-FuncMapper achieve promising results in identifying
+relevant predefined functions for rule interpretation. Further proof of concept
+in automated rule interpretation also demonstrates the possibility of
+LLM-FuncMapper in interpreting complex regulatory clauses. To the best of our
+knowledge, this study is the first attempt to introduce LLM for understanding
+and interpreting complex regulatory clauses, which may shed light on further
+adoption of LLM in the construction domain.
+"
+Prompt-Based Length Controlled Generation with Reinforcement Learning,Renlong Jie,http://arxiv.org/pdf/2308.12030v2.pdf,2023-08-23,"['cs.cl', 'cs.ai', 'cs.lg']",2308.12030v2.pdf,"  Large language models (LLMs) like ChatGPT and GPT-4 have attracted great
+attention given their surprising performance on a wide range of NLP tasks.
+Length controlled generation of LLMs emerges as an important topic, which
+enables users to fully leverage the capability of LLMs in more real-world
+scenarios like generating a proper answer or essay of a desired length. In
+addition, the autoregressive generation in LLMs is extremely time-consuming,
+while the ability of controlling this generated length can reduce the inference
+cost by limiting the length. Therefore, we propose a prompt-based length
+control method to achieve high-accuracy length controlled generation. In
+particular, we adopt reinforcement learning with the reward signal given by
+either trainable or rule-based reward models, which further enhances the
+length-control ability of LLMs by rewarding outputs that follows pre-defined
+control instruction. To enable rule-based inference, we also introduce standard
+prompt extractor to collect the standard control information from users' input.
+Experiments show that our method significantly improves the accuracy of
+prompt-based length control for summarization task on popular datasets like
+CNNDM and NYT. Both the standard prompt extractor and the RL-tuned model have
+show strong generalization ability to unseen control prompt templates.
+"
+LLM Powered Sim-to-real Transfer for Traffic Signal Control,Longchao Da,http://arxiv.org/pdf/2308.14284v3.pdf,2023-08-28,"['cs.ai', 'h.4.0']",2308.14284v3.pdf,"  Numerous solutions are proposed for the Traffic Signal Control (TSC) tasks
+aiming to provide efficient transportation and mitigate congestion waste. In
+recent, promising results have been attained by Reinforcement Learning (RL)
+methods through trial and error in simulators, bringing confidence in solving
+cities' congestion headaches. However, there still exist performance gaps when
+simulator-trained policies are deployed to the real world. This issue is mainly
+introduced by the system dynamic difference between the training simulator and
+the real-world environments. The Large Language Models (LLMs) are trained on
+mass knowledge and proved to be equipped with astonishing inference abilities.
+In this work, we leverage LLMs to understand and profile the system dynamics by
+a prompt-based grounded action transformation. Accepting the cloze prompt
+template, and then filling in the answer based on accessible context, the
+pre-trained LLM's inference ability is exploited and applied to understand how
+weather conditions, traffic states, and road types influence traffic dynamics,
+being aware of this, the policies' action is taken and grounded based on
+realistic dynamics, thus help the agent learn a more realistic policy. We
+conduct experiments using DQN to show the effectiveness of the proposed
+PromptGAT's ability in mitigating the performance gap from simulation to
+reality (sim-to-real).
+"
+AnoVL: Adapting Vision-Language Models for Unified Zero-shot Anomaly  Localization,Hanqiu Deng,http://arxiv.org/pdf/2308.15939v1.pdf,2023-08-30,['cs.cv'],2308.15939v1.pdf,"  Contrastive Language-Image Pre-training (CLIP) models have shown promising
+performance on zero-shot visual recognition tasks by learning visual
+representations under natural language supervision. Recent studies attempt the
+use of CLIP to tackle zero-shot anomaly detection by matching images with
+normal and abnormal state prompts. However, since CLIP focuses on building
+correspondence between paired text prompts and global image-level
+representations, the lack of patch-level vision to text alignment limits its
+capability on precise visual anomaly localization. In this work, we introduce a
+training-free adaptation (TFA) framework of CLIP for zero-shot anomaly
+localization. In the visual encoder, we innovate a training-free value-wise
+attention mechanism to extract intrinsic local tokens of CLIP for patch-level
+local description. From the perspective of text supervision, we particularly
+design a unified domain-aware contrastive state prompting template. On top of
+the proposed TFA, we further introduce a test-time adaptation (TTA) mechanism
+to refine anomaly localization results, where a layer of trainable parameters
+in the adapter is optimized using TFA's pseudo-labels and synthetic
+noise-corrupted tokens. With both TFA and TTA adaptation, we significantly
+exploit the potential of CLIP for zero-shot anomaly localization and
+demonstrate the effectiveness of our proposed methods on various datasets.
+"
+Investigating the Applicability of Self-Assessment Tests for Personality  Measurement of Large Language Models,Akshat Gupta,http://arxiv.org/pdf/2309.08163v1.pdf,2023-09-15,"['cs.cl', 'cs.ai']",2309.08163v1.pdf,"  As large language models (LLM) evolve in their capabilities, various recent
+studies have tried to quantify their behavior using psychological tools created
+to study human behavior. One such example is the measurement of ""personality""
+of LLMs using personality self-assessment tests. In this paper, we take three
+such studies on personality measurement of LLMs that use personality
+self-assessment tests created to study human behavior. We use the prompts used
+in these three different papers to measure the personality of the same LLM. We
+find that all three prompts lead very different personality scores. This simple
+test reveals that personality self-assessment scores in LLMs depend on the
+subjective choice of the prompter. Since we don't know the ground truth value
+of personality scores for LLMs as there is no correct answer to such questions,
+there's no way of claiming if one prompt is more or less correct than the
+other. We then introduce the property of option order symmetry for personality
+measurement of LLMs. Since most of the self-assessment tests exist in the form
+of multiple choice question (MCQ) questions, we argue that the scores should
+also be robust to not just the prompt template but also the order in which the
+options are presented. This test unsurprisingly reveals that the answers to the
+self-assessment tests are not robust to the order of the options. These simple
+tests, done on ChatGPT and Llama2 models show that self-assessment personality
+tests created for humans are not appropriate for measuring personality in LLMs.
+"
+InstructCV: Instruction-Tuned Text-to-Image Diffusion Models as Vision  Generalists,Yulu Gan,http://arxiv.org/pdf/2310.00390v1.pdf,2023-09-30,['cs.cv'],2310.00390v1.pdf,"  Recent advances in generative diffusion models have enabled text-controlled
+synthesis of realistic and diverse images with impressive quality. Despite
+these remarkable advances, the application of text-to-image generative models
+in computer vision for standard visual recognition tasks remains limited. The
+current de facto approach for these tasks is to design model architectures and
+loss functions that are tailored to the task at hand. In this paper, we develop
+a unified language interface for computer vision tasks that abstracts away
+task-specific design choices and enables task execution by following natural
+language instructions. Our approach involves casting multiple computer vision
+tasks as text-to-image generation problems. Here, the text represents an
+instruction describing the task, and the resulting image is a visually-encoded
+task output. To train our model, we pool commonly-used computer vision datasets
+covering a range of tasks, including segmentation, object detection, depth
+estimation, and classification. We then use a large language model to
+paraphrase prompt templates that convey the specific tasks to be conducted on
+each image, and through this process, we create a multi-modal and multi-task
+training dataset comprising input and output images along with annotated
+instructions. Following the InstructPix2Pix architecture, we apply
+instruction-tuning to a text-to-image diffusion model using our constructed
+dataset, steering its functionality from a generative model to an
+instruction-guided multi-task vision learner. Experiments demonstrate that our
+model, dubbed InstructCV, performs competitively compared to other generalist
+and task-specific vision models. Moreover, it exhibits compelling
+generalization capabilities to unseen data, categories, and user instructions.
+"
+Revisit Input Perturbation Problems for LLMs: A Unified Robustness  Evaluation Framework for Noisy Slot Filling Task,Guanting Dong,http://arxiv.org/pdf/2310.06504v1.pdf,2023-10-10,"['cs.cl', 'cs.ai', 'cs.lg']",2310.06504v1.pdf,"  With the increasing capabilities of large language models (LLMs), these
+high-performance models have achieved state-of-the-art results on a wide range
+of natural language processing (NLP) tasks. However, the models' performance on
+commonly-used benchmark datasets often fails to accurately reflect their
+reliability and robustness when applied to real-world noisy data. To address
+these challenges, we propose a unified robustness evaluation framework based on
+the slot-filling task to systematically evaluate the dialogue understanding
+capability of LLMs in diverse input perturbation scenarios. Specifically, we
+construct a input perturbation evaluation dataset, Noise-LLM, which contains
+five types of single perturbation and four types of mixed perturbation data.
+Furthermore, we utilize a multi-level data augmentation method (character,
+word, and sentence levels) to construct a candidate data pool, and carefully
+design two ways of automatic task demonstration construction strategies
+(instance-level and entity-level) with various prompt templates. Our aim is to
+assess how well various robustness methods of LLMs perform in real-world noisy
+scenarios. The experiments have demonstrated that the current open-source LLMs
+generally achieve limited perturbation robustness performance. Based on these
+experimental observations, we make some forward-looking suggestions to fuel the
+research in this direction.
+"
+Do Language Models Learn about Legal Entity Types during Pretraining?,Claire Barale,http://arxiv.org/pdf/2310.13092v1.pdf,2023-10-19,['cs.cl'],2310.13092v1.pdf,"  Language Models (LMs) have proven their ability to acquire diverse linguistic
+knowledge during the pretraining phase, potentially serving as a valuable
+source of incidental supervision for downstream tasks. However, there has been
+limited research conducted on the retrieval of domain-specific knowledge, and
+specifically legal knowledge. We propose to explore the task of Entity Typing,
+serving as a proxy for evaluating legal knowledge as an essential aspect of
+text comprehension, and a foundational task to numerous downstream legal NLP
+applications. Through systematic evaluation and analysis and two types of
+prompting (cloze sentences and QA-based templates) and to clarify the nature of
+these acquired cues, we compare diverse types and lengths of entities both
+general and domain-specific entities, semantics or syntax signals, and
+different LM pretraining corpus (generic and legal-oriented) and architectures
+(encoder BERT-based and decoder-only with Llama2). We show that (1) Llama2
+performs well on certain entities and exhibits potential for substantial
+improvement with optimized prompt templates, (2) law-oriented LMs show
+inconsistent performance, possibly due to variations in their training corpus,
+(3) LMs demonstrate the ability to type entities even in the case of
+multi-token entities, (4) all models struggle with entities belonging to
+sub-domains of the law (5) Llama2 appears to frequently overlook syntactic
+cues, a shortcoming less present in BERT-based architectures.
+"
+LlamaRec: Two-Stage Recommendation using Large Language Models for  Ranking,Zhenrui Yue,http://arxiv.org/pdf/2311.02089v1.pdf,2023-10-25,"['cs.ir', 'cs.ai', 'cs.cl']",2311.02089v1.pdf,"  Recently, large language models (LLMs) have exhibited significant progress in
+language understanding and generation. By leveraging textual features,
+customized LLMs are also applied for recommendation and demonstrate
+improvements across diverse recommendation scenarios. Yet the majority of
+existing methods perform training-free recommendation that heavily relies on
+pretrained knowledge (e.g., movie recommendation). In addition, inference on
+LLMs is slow due to autoregressive generation, rendering existing methods less
+effective for real-time recommendation. As such, we propose a two-stage
+framework using large language models for ranking-based recommendation
+(LlamaRec). In particular, we use small-scale sequential recommenders to
+retrieve candidates based on the user interaction history. Then, both history
+and retrieved items are fed to the LLM in text via a carefully designed prompt
+template. Instead of generating next-item titles, we adopt a verbalizer-based
+approach that transforms output logits into probability distributions over the
+candidate items. Therefore, the proposed LlamaRec can efficiently rank items
+without generating long text. To validate the effectiveness of the proposed
+framework, we compare against state-of-the-art baseline methods on benchmark
+datasets. Our experimental results demonstrate the performance of LlamaRec,
+which consistently achieves superior performance in both recommendation
+performance and efficiency.
+"
+Prompting Multilingual Large Language Models to Generate Code-Mixed  Texts: The Case of South East Asian Languages,Zheng-Xin Yong,http://arxiv.org/pdf/2303.13592v4.pdf,2023-03-23,"['cs.cl', 'cs.ai']",2303.13592v4.pdf,"  While code-mixing is a common linguistic practice in many parts of the world,
+collecting high-quality and low-cost code-mixed data remains a challenge for
+natural language processing (NLP) research. The recent proliferation of Large
+Language Models (LLMs) compels one to ask: how capable are these systems in
+generating code-mixed data? In this paper, we explore prompting multilingual
+LLMs in a zero-shot manner to generate code-mixed data for seven languages in
+South East Asia (SEA), namely Indonesian, Malay, Chinese, Tagalog, Vietnamese,
+Tamil, and Singlish. We find that publicly available multilingual
+instruction-tuned models such as BLOOMZ and Flan-T5-XXL are incapable of
+producing texts with phrases or clauses from different languages. ChatGPT
+exhibits inconsistent capabilities in generating code-mixed texts, wherein its
+performance varies depending on the prompt template and language pairing. For
+instance, ChatGPT generates fluent and natural Singlish texts (an English-based
+creole spoken in Singapore), but for English-Tamil language pair, the system
+mostly produces grammatically incorrect or semantically meaningless utterances.
+Furthermore, it may erroneously introduce languages not specified in the
+prompt. Based on our investigation, existing multilingual LLMs exhibit a wide
+range of proficiency in code-mixed data generation for SEA languages. As such,
+we advise against using LLMs in this context without extensive human checks.
+"
+"Reason for Future, Act for Now: A Principled Framework for Autonomous  LLM Agents with Provable Sample Efficiency",Zhihan Liu,http://arxiv.org/pdf/2309.17382v2.pdf,2023-09-29,"['cs.ai', 'cs.lg']",2309.17382v2.pdf,"  Large language models (LLMs) demonstrate impressive reasoning abilities, but
+translating reasoning into actions in the real world remains challenging. In
+particular, it remains unclear how to complete a given task provably within a
+minimum number of interactions with the external environment, e.g., through an
+internal mechanism of reasoning. To this end, we propose a principled framework
+with provable regret guarantees to orchestrate reasoning and acting, which we
+call ""reason for future, act for now"" (\texttt{RAFA}). Specifically, we design
+a prompt template for reasoning that learns from the memory buffer and plans a
+future trajectory over a long horizon (""reason for future""). At each step, the
+LLM agent takes the initial action of the planned trajectory (""act for now""),
+stores the collected feedback in the memory buffer, and reinvokes the reasoning
+routine to replan the future trajectory from the new state.
+  The key idea is to cast reasoning in LLMs as learning and planning in
+Bayesian adaptive Markov decision processes (MDPs). Correspondingly, we prompt
+LLMs to form an updated posterior of the unknown environment from the memory
+buffer (learning) and generate an optimal trajectory for multiple future steps
+that maximizes a value function (planning). The learning and planning
+subroutines are performed in an ""in-context"" manner to emulate the actor-critic
+update for MDPs. Our theoretical analysis proves that the novel combination of
+long-term reasoning and short-term acting achieves a $\sqrt{T}$ regret. In
+particular, the regret bound highlights an intriguing interplay between the
+prior knowledge obtained through pretraining and the uncertainty reduction
+achieved by reasoning and acting. Our empirical validation shows that it
+outperforms various existing frameworks and achieves nearly perfect scores on a
+few benchmarks.
+"
+ClickPrompt: CTR Models are Strong Prompt Generators for Adapting  Language Models to CTR Prediction,Jianghao Lin,http://arxiv.org/pdf/2310.09234v2.pdf,2023-10-13,"['cs.ir', 'cs.ai']",2310.09234v2.pdf,"  Click-through rate (CTR) prediction has become increasingly indispensable for
+various Internet applications. Traditional CTR models convert the multi-field
+categorical data into ID features via one-hot encoding, and extract the
+collaborative signals among features. Such a paradigm suffers from the problem
+of semantic information loss. Another line of research explores the potential
+of pretrained language models (PLMs) for CTR prediction by converting input
+data into textual sentences through hard prompt templates. Although semantic
+signals are preserved, they generally fail to capture the collaborative
+information (e.g., feature interactions, pure ID features), not to mention the
+unacceptable inference overhead brought by the huge model size. In this paper,
+we aim to model both the semantic knowledge and collaborative knowledge for
+accurate CTR estimation, and meanwhile address the inference inefficiency
+issue. To benefit from both worlds and close their gaps, we propose a novel
+model-agnostic framework (i.e., ClickPrompt), where we incorporate CTR models
+to generate interaction-aware soft prompts for PLMs. We design a
+prompt-augmented masked language modeling (PA-MLM) pretraining task, where PLM
+has to recover the masked tokens based on the language context, as well as the
+soft prompts generated by CTR model. The collaborative and semantic knowledge
+from ID and textual features would be explicitly aligned and interacted via the
+prompt interface. Then, we can either tune the CTR model with PLM for superior
+performance, or solely tune the CTR model without PLM for inference efficiency.
+Experiments on four real-world datasets validate the effectiveness of
+ClickPrompt compared with existing baselines.
+"
+ALT: Towards Fine-grained Alignment between Language and CTR Models for  Click-Through Rate Prediction,Hangyu Wang,http://arxiv.org/pdf/2310.19453v1.pdf,2023-10-30,"['cs.ir', 'cs.ai']",2310.19453v1.pdf,"  Click-through rate (CTR) prediction plays as a core function module in
+various personalized online services. According to the data modality and input
+format, the models for CTR prediction can be mainly classified into two
+categories. The first one is the traditional CTR models that take as inputs the
+one-hot encoded ID features of tabular modality, which aims to capture the
+collaborative signals via feature interaction modeling. The second category
+takes as inputs the sentences of textual modality obtained by hard prompt
+templates, where pretrained language models (PLMs) are adopted to extract the
+semantic knowledge. These two lines of research generally focus on different
+characteristics of the same input data (i.e., textual and tabular modalities),
+forming a distinct complementary relationship with each other. Therefore, in
+this paper, we propose to conduct fine-grained feature-level Alignment between
+Language and CTR models (ALT) for CTR prediction. Apart from the common
+CLIP-like instance-level contrastive learning, we further design a novel joint
+reconstruction pretraining task for both masked language and tabular modeling.
+Specifically, the masked data of one modality (i.e., tokens or features) has to
+be recovered with the help of the other modality, which establishes the
+feature-level interaction and alignment via sufficient mutual information
+extraction between dual modalities. Moreover, we propose three different
+finetuning strategies with the option to train the aligned language and CTR
+models separately or jointly for downstream CTR prediction tasks, thus
+accommodating the varying efficacy and efficiency requirements for industrial
+applications. Extensive experiments on three real-world datasets demonstrate
+that ALT outperforms SOTA baselines, and is highly compatible for various
+language and CTR models.
+"