mlsanityData to 7 Oct 2026

The Foundation Model Transparency Index

arXiv 2310.12941 ↗First posted 19 Oct 2023cs.LG cs.AIcited by 63

Not on the map. Why?

Abstract (arXiv)

Foundation models have rapidly permeated society, catalyzing a wave of generative AI applications spanning enterprise and consumer-facing contexts. While the societal impact of foundation models is growing, transparency is on the decline, mirroring the opacity that has plagued past digital technologies (e.g. social media). Reversing this trend is essential: transparency is a vital precondition for public accountability, scientific innovation, and effective governance. To assess the transparency of the foundation model ecosystem and help improve transparency over time, we introduce the Foundation Model Transparency Index. The Foundation Model Transparency Index specifies 100 fine-grained indicators that comprehensively codify transparency for foundation models, spanning the upstream resources used to build a foundation model (e.g data, labor, compute), details about the model itself (e.g. size, capabilities, risks), and the downstream use (e.g. distribution channels, usage policies, affected geographies). We score 10 major foundation model developers (e.g. OpenAI, Google, Meta) against the 100 indicators to assess their transparency. To facilitate and standardize assessment, we score developers in relation to their practices for their flagship foundation model (e.g. GPT-4 for OpenAI, PaLM 2 for Google, Llama 2 for Meta). We present 10 top-level findings about the foundation model ecosystem: for example, no developer currently discloses significant information about the downstream impact of its flagship model, such as the number of users, affected market sectors, or how users can seek redress for harm. Overall, the Foundation Model Transparency Index establishes the level of transparency today to drive progress on foundation model governance via industry standards and regulatory intervention.

Key facts. Cited by 63 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Oct 2023.

Builds on

0 of 0 references.

No references here yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 32; 2025: 20; 2026: 9.

63 papers.

Sort:

By area:

  1. 2024OLMoE: Open Mixture-of-Experts Language Modelscited by 291
  2. 2024RedPajama: an Open Dataset for Training Large Language Modelscited by 178
  3. 2024Black-Box Access is Insufficient for Rigorous AI Auditscited by 123
  4. 2024Benchmarking Benchmark Leakage in Large Language Modelscited by 102
  5. 2024Foundation Model for Advancing Healthcare: Challenges, Opportunities, and Future Directionscited by 77
  6. 2024Unintended Impacts of LLM Alignment on Global Representationcited by 66
  7. 2024CroissantLLM: A Truly Bilingual French-English Language Modelcited by 52
  8. 2024Evaluating Copyright Takedown Methods for Language Modelscited by 42
  9. 2024Copyright Traps for Large Language Modelscited by 36
  10. 2024RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Contentcited by 26
  11. 2024Position: Measure Dataset Diversity, Don't Just Claim Itcited by 26
  12. 2024The 2024 Foundation Model Transparency Indexcited by 18
38 more
  1. 2024AI Risk Management Should Incorporate Both Safety and Securitycited by 17
  2. 2024LLMs and Memorization: On Quality and Specificity of Copyright Compliancecited by 16
  3. 2024Language model developers should report train-test overlapcited by 16
  4. 2024AI Governance and Accountability: An Analysis of Anthropic's Claudecited by 14
  5. 2025The AI Agent Indexcited by 14
  6. 2025FlexOlmo: Open Language Models for Flexible Data Usecited by 14
  7. 2024Benchmark Inflation: Revealing LLM Performance Gaps Using Retro-Holdoutscited by 13
  8. 2024Exploring Prompt Engineering Practices in the Enterprisecited by 11
  9. 2024Epistemology of Language Models: Do Language Models Have Holistic Knowledge?cited by 9
  10. 2025Distributive Fairness in Large Language Models: Evaluating Alignment with Human Valuescited by 9
  11. 2025Levels of Analysis for Large Language Modelscited by 9
  12. 2025The 2025 Foundation Model Transparency Indexcited by 9
  13. 2025User Privacy and Large Language Models: An Analysis of Frontier Developers' Privacy Policiescited by 8
  14. 2025AlignVLM: Bridging Vision and Language Latent Spaces for Multimodal Document Understandingcited by 7
  15. 2025In-House Evaluation Is Not Enough: Towards Robust Third-Party Flaw Disclosure for General-Purpose AIcited by 7
  16. 2025MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AIcited by 7
  17. 2024AI Act and Large Language Models (LLMs): When critical issues and privacy impact require human and ethical oversightcited by 6
  18. 2025Register Always Matters: Analysis of LLM Pretraining Data Through the Lens of Language Variationcited by 6
  19. 2025Winter Soldier: Backdooring Language Models at Pre-Training with Indirect Data Poisoningcited by 6
  20. 2023An International Consortium for Evaluations of Societal-Scale Risks from Advanced AIcited by 5
  21. 20247B Fully Open Source Moxin-LLM/VLM -- From Pretraining to GRPO-based Reinforcement Learning Enhancementcited by 5
  22. 2025AI Governance through Marketscited by 5
  23. 2025Science Is Falling Behind the Frontier: Foundation Model Adoption Across Half a Million Paperscited by 5
  24. 2023A Turing Test: Are AI Chatbots Behaviorally Similar to Humans?cited by 4
  25. 2024Laissez-Faire Harms: Algorithmic Biases in Generative Language Modelscited by 4
  26. 2024Jet Expansions of Residual Computationcited by 4
  27. 2024A Comprehensive Survey and Classification of Evaluation Criteria for Trustworthy Artificial Intelligencecited by 3
  28. 2025NeurIPS should lead scientific consensus on AI policycited by 3
  29. 2024Data-Centric AI in the Age of Large Language Modelscited by 2
  30. 2024Ethical AI Governance: Methods for Evaluating Trustworthy AIcited by 2
  31. 2025Building Trustworthy AI for Materials Discovery: From Autonomous Laboratories to Z-scorescited by 2
  32. 2026How Open Must Language Models be to Enable Reliable Scientific Inference?cited by 2
  33. 2026The ATOM Report: Measuring the Open Language Model Ecosystemcited by 2
  34. 2024Prospector Heads: Generalized Feature Attribution for Large Models & Datacited by 1
  35. 2024Towards Assurance of LLM Adversarial Robustness using Ontology-Driven Argumentationcited by 1
  36. 2025AI Data Development: A Scorecard for the System Card Frameworkcited by 1
  37. 2025Measuring What Matters: The AI Pluralism Indexcited by 1
  38. 2025TRUST: A Decentralized Framework for Auditing Large Language Model Reasoningcited by 1

50 most cited of 63 All 63 (JSON)