Report of the 1st Workshop on Generative AI and Law
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Abstract (arXiv)
This report presents the takeaways of the inaugural Workshop on Generative AI and Law (GenLaw), held in July 2023. A cross-disciplinary group of practitioners and scholars from computer science and law convened to discuss the technical, doctrinal, and policy challenges presented by law for Generative AI, and by Generative AI for law, with an emphasis on U.S. law in particular. We begin the report with a high-level statement about why Generative AI is both immensely significant and immensely challenging for law. To meet these challenges, we conclude that there is an essential need for 1) a shared knowledge base that provides a common conceptual language for experts across disciplines; 2) clarification of the distinctive technical capabilities of generative-AI systems, as compared and contrasted to other computer and AI systems; 3) a logical taxonomy of the legal issues these systems raise; and, 4) a concrete research agenda to promote collaboration and knowledge-sharing on emerging issues at the intersection of Generative AI and law. In this report, we synthesize the key takeaways from the GenLaw workshop that begin to address these needs. All of the listed authors contributed to the workshop upon which this report is based, but they and their organizations do not necessarily endorse all of the specific claims in this report.
Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Nov 2023.
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Citing papers per year: 2024: 6; 2025: 4; 2026: 3.
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- 2024Rethinking LLM Memorization through the Lens of Adversarial Compressioncited by 94
- 2024Measuring memorization in language models via probabilistic extractioncited by 41
- 2026Extracting books from production language modelscited by 17
- 2024Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy and Researchcited by 15
- 2024Replication in Visual Diffusion Models: A Survey and Outlookcited by 11
- 2025The Landscape of Memorization in LLMs: Mechanisms, Measurement, and Mitigationcited by 10
- 2024On the Standardization of Behavioral Use Clauses and Their Adoption for Responsible Licensing of AIcited by 7
- 2024Legal Minds, Algorithmic Decisions: How LLMs Apply Constitutional Principles in Complex Scenarioscited by 7
- 2025Position: LLM Watermarking Should Align Stakeholders' Incentives for Practical Adoptioncited by 7
- 2025Memories Retrieved from Many Paths: A Multi-Prefix Framework for Robust Detection of Training Data Leakage in Large Language Modelscited by 2
- 2025Blameless Users in a Clean Room: Defining Copyright Protection for Generative Modelscited by 1
- 2026Estimating near-verbatim extraction risk in language models with decoding-constrained beam searchcited by 1
1 more
- 2026Extractable Memorization From First Principlescited by 0
- 2026Extractable Memorization From First Principles14 Jul 2026
- 2026Estimating near-verbatim extraction risk in language models with decoding-constrained beam search26 Mar 2026
- 2026Extracting books from production language models6 Jan 2026
- 2025Memories Retrieved from Many Paths: A Multi-Prefix Framework for Robust Detection of Training Data Leakage in Large Language Models25 Nov 2025
- 2025Position: LLM Watermarking Should Align Stakeholders' Incentives for Practical Adoption21 Oct 2025
- 2025The Landscape of Memorization in LLMs: Mechanisms, Measurement, and Mitigation8 Jul 2025
- 2025Blameless Users in a Clean Room: Defining Copyright Protection for Generative Models23 Jun 2025
- 2024Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy and Research9 Dec 2024
- 2024Measuring memorization in language models via probabilistic extraction25 Oct 2024
- 2024Legal Minds, Algorithmic Decisions: How LLMs Apply Constitutional Principles in Complex Scenarios29 Jul 2024
- 2024Replication in Visual Diffusion Models: A Survey and Outlook7 Jul 2024
- 2024Rethinking LLM Memorization through the Lens of Adversarial Compression23 Apr 2024
- 2026Extracting books from production language modelscited by 17
- 2024Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy and Researchcited by 15
- 2025The Landscape of Memorization in LLMs: Mechanisms, Measurement, and Mitigationcited by 10
- 2025Position: LLM Watermarking Should Align Stakeholders' Incentives for Practical Adoptioncited by 7
- 2026Extracting books from production language models6 Jan 2026
- 2025Position: LLM Watermarking Should Align Stakeholders' Incentives for Practical Adoption21 Oct 2025
- 2025The Landscape of Memorization in LLMs: Mechanisms, Measurement, and Mitigation8 Jul 2025
- 2024Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy and Research9 Dec 2024
- 2024Replication in Visual Diffusion Models: A Survey and Outlookcited by 11
- 2024Measuring memorization in language models via probabilistic extractioncited by 41
- 2024On the Standardization of Behavioral Use Clauses and Their Adoption for Responsible Licensing of AIcited by 7
- 2024Legal Minds, Algorithmic Decisions: How LLMs Apply Constitutional Principles in Complex Scenarioscited by 7
- 2025Memories Retrieved from Many Paths: A Multi-Prefix Framework for Robust Detection of Training Data Leakage in Large Language Modelscited by 2
- 2025Blameless Users in a Clean Room: Defining Copyright Protection for Generative Modelscited by 1
- 2026Estimating near-verbatim extraction risk in language models with decoding-constrained beam searchcited by 1
- 2026Extractable Memorization From First Principlescited by 0
- 2026Extractable Memorization From First Principles14 Jul 2026
- 2026Estimating near-verbatim extraction risk in language models with decoding-constrained beam search26 Mar 2026
- 2025Memories Retrieved from Many Paths: A Multi-Prefix Framework for Robust Detection of Training Data Leakage in Large Language Models25 Nov 2025
- 2025Blameless Users in a Clean Room: Defining Copyright Protection for Generative Models23 Jun 2025
- 2024Measuring memorization in language models via probabilistic extraction25 Oct 2024
- 2024Legal Minds, Algorithmic Decisions: How LLMs Apply Constitutional Principles in Complex Scenarios29 Jul 2024
- 2024On the Standardization of Behavioral Use Clauses and Their Adoption for Responsible Licensing of AI7 Feb 2024