mlsanityData to 14 Oct 2026

Regulating Large Language Models: A Roundtable Report

arXiv 2403.15397 ↗First posted 16 Feb 2024cs.CY cs.AI cs.CLcited by 0

Not on the map. Why?

Abstract (arXiv)

On July 20, 2023, a group of 27 scholars and digital rights advocates with expertise in law, computer science, political science, and other disciplines gathered for the Large Language Models, Law and Policy Roundtable, co-hosted by the NYU School of Law's Information Law Institute and the Center for Democracy & Technology. The roundtable convened to discuss how law and policy can help address some of the larger societal problems posed by large language models (LLMs). The discussion focused on three policy topic areas in particular: 1. Truthfulness: What risks do LLMs pose in terms of generating mis- and disinformation? How can these risks be mitigated from a technical and/or regulatory perspective? 2. Privacy: What are the biggest privacy risks involved in the creation, deployment, and use of LLMs? How can these risks be mitigated from a technical and/or regulatory perspective? 3. Market concentration: What threats do LLMs pose concerning market/power concentration? How can these risks be mitigated from a technical and/or regulatory perspective? In this paper, we provide a detailed summary of the day's proceedings. We first recap what we deem to be the most important contributions made during the issue framing discussions. We then provide a list of potential legal and regulatory interventions generated during the brainstorming discussions.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 22 references that are in the map. First posted on arXiv 16 Feb 2024.

Builds on

8 of 22 references.

References · 8

  1. 2022Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learnedcited by 729
  2. 2023Causal Reasoning and Large Language Models: Opening a New Frontier for Causalitycited by 353
  3. 2022What Does it Mean for a Language Model to Preserve Privacy?cited by 195
  4. 2023Generative Language Models and Automated Influence Operations: Emerging Threats and Potential Mitigationscited by 158
  5. 2023Emergent and Predictable Memorization in Large Language Modelscited by 148
  6. 2022Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization?cited by 91
  7. 2023The Foundation Model Transparency Indexcited by 66
  8. 2023Differentially Private Natural Language Models: Recent Advances and Future Directionscited by 20

Built on by

Not cited yet.