name
stringclasses 24
values | url
stringclasses 24
values | tags
list |
|---|---|---|
Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild
|
https://arxiv.org/abs/1906.02569
|
[
"inclusive"
] |
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
|
https://arxiv.org/abs/1910.01108
|
[
"sustainable"
] |
RAFT: A Real-World Few-Shot Text Classification Benchmark
|
https://arxiv.org/abs/2109.14076
|
[
"rigorous"
] |
Interactive Model Cards: A Human-Centered Approach to Model Documentation
|
https://arxiv.org/abs/2205.02894
|
[
"rigorous"
] |
Data Governance in the Age of Large-Scale Data-Driven Language Technology
|
https://arxiv.org/abs/2206.03216
|
[
"consentful"
] |
Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets
|
https://arxiv.org/abs/2103.12028
|
[
"rigorous"
] |
A Framework for Deprecating Datasets: Standardizing Documentation, Identification, and Communication
|
https://arxiv.org/abs/2111.04424
|
[
"rigorous"
] |
Bugs in the Data: How ImageNet Misrepresents Biodiversity
|
https://arxiv.org/abs/2208.11695
|
[
"rigorous",
"socially conscious"
] |
Measuring Data
|
https://arxiv.org/abs/2212.05129
|
[
"rigorous"
] |
Perturbation Augmentation for Fairer NLP
|
https://arxiv.org/abs/2205.12586
|
[
"rigorous"
] |
SEAL : Interactive Tool for Systematic Error Analysis and Labeling
|
https://arxiv.org/abs/2210.05839
|
[
"rigorous"
] |
Multitask Prompted Training Enables Zero-Shot Task Generalization
|
https://arxiv.org/abs/2110.08207
|
[
"rigorous"
] |
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
|
https://arxiv.org/abs/2211.05100
|
[
"inclusive"
] |
The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset
|
https://arxiv.org/abs/2303.03915
|
[
"inclusive"
] |
Evaluate & Evaluation on the Hub: Better Best Practices for Data and Model Measurements
|
https://arxiv.org/abs/2210.01970
|
[
"rigorous"
] |
Spacerini: Plug-and-play Search Engines with Pyserini and Hugging Face
|
https://arxiv.org/abs/2302.14534
|
[
"inclusive"
] |
The ROOTS Search Tool: Data Transparency for LLMs
|
https://arxiv.org/abs/2302.14035
|
[
"rigorous"
] |
Fair Diffusion: Instructing Text-to-Image Generation Models on Fairness
|
https://arxiv.org/abs/2302.10893
|
[
"rigorous"
] |
Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learning
|
https://arxiv.org/abs/2302.08476
|
[
"sustainable"
] |
The Gradient of Generative AI Release: Methods and Considerations
|
https://arxiv.org/abs/2302.04844
|
[
"inquisitive"
] |
BigScience: A Case Study in the Social Construction of a Multilingual Large Language Model
|
https://arxiv.org/abs/2212.04960
|
[
"inquisitive"
] |
Towards Openness Beyond Open Access: User Journeys through 3 Open AI Collaboratives
|
https://arxiv.org/abs/2301.08488
|
[
"inquisitive"
] |
Stable Bias: Analyzing Societal Representations in Diffusion Models
|
https://arxiv.org/abs/2303.11408
|
[
"rigorous"
] |
Stronger Together: on the Articulation of Ethical Charters, Legal Tools, and Technical Documentation in ML
|
https://arxiv.org/abs/2305.18615
|
[
"rigorous",
"inquisitive"
] |
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