name
stringclasses 24
values | url
stringclasses 24
values | tags
sequence |
|---|---|---|
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"
] |