mlsanityData to 7 Oct 2026

EleutherAI: Going Beyond "Open Science" to "Science in the Open"

arXiv 2210.06413 ↗First posted 12 Oct 2022cs.CLcited by 13

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Abstract (arXiv)

Over the past two years, EleutherAI has established itself as a radically novel initiative aimed at both promoting open-source research and conducting research in a transparent, openly accessible and collaborative manner. EleutherAI's approach to research goes beyond transparency: by doing research entirely in public, anyone in the world can observe and contribute at every stage. Our work has been received positively and has resulted in several high-impact projects in Natural Language Processing and other fields. In this paper, we describe our experience doing public-facing machine learning research, the benefits we believe this approach brings, and the pitfalls we have encountered.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 5 of its 12 references that are in the map. First posted on arXiv 12 Oct 2022.

Builds on

5 of 12 references.

References · 5

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,634
  3. 2020The Pile: An 800GB Dataset of Diverse Text for Language Modelingcited by 2,346
  4. 2022GPT-NeoX-20B: An Open-Source Autoregressive Language Modelcited by 711
  5. 2022VQGAN-CLIP: Open Domain Image Generation and Editing with Natural Language Guidancecited by 285

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 4; 2024: 4; 2025: 3; 2026: 1.

13 papers.

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  1. 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,968
  2. 2023Pythia: A Suite for Analyzing Large Language Models Across Training and Scalingcited by 1,734
  3. 2023Emergent and Predictable Memorization in Large Language Modelscited by 147
  4. 2023Opening up ChatGPT: Tracking openness, transparency, and accountability in instruction-tuned text generatorscited by 56
  5. 2024The Model Openness Framework: Promoting Completeness and Openness for Reproducibility, Transparency, and Usability in Artificial Intelligencecited by 21
  6. 2024AstroPT: Scaling Large Observation Models for Astronomycited by 9
  7. 2023Hazards from Increasingly Accessible Fine-Tuning of Downloadable Foundation Modelscited by 7
  8. 2024Automated Program Repair: Emerging trends pose and expose problems for benchmarkscited by 7
  9. 2025AstroLLaVA: towards the unification of astronomical data and natural languagecited by 6
  10. 2024Enterprise Benchmarks for Large Language Model Evaluationcited by 5
  11. 2025A Cartography of Open Collaboration in Open Source AI: Mapping Practices, Motivations, and Governance in 14 Open Large Language Model Projectscited by 1
  12. 2025Causal Understanding by LLMs: The Role of Uncertaintycited by 0
1 more
  1. 2026Selecting Language Models for Social Science: Start Small, Start Open, and Validatecited by 0