219 citations · 330 across the 10 of their papers we have counts for
10 papers
LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models
Neel Guha, Julian Nyarko, Daniel E. Ho +37
The advent of large language models (LLMs) and their adoption by the legal community has given rise to the question: what types of legal reasoning can LLMs perform? To enable great…
Freedom of Speech and AI Output
Eugene Volokh, Mark Lemley, Peter Henderson
Is the output of generative AI entitled to First Amendment protection? We're inclined to say yes. Even though current AI programs are of course not people and do not themselves hav…
Where's the Liability in Harmful AI Speech?
Peter Henderson, Tatsunori Hashimoto, Mark Lemley
Generative AI, in particular text-based "foundation models" (large models trained on a huge variety of information including the internet), can generate speech that could be proble…
Foundation Models and Fair Use
Peter Henderson, Xuechen Li, Dan Jurafsky +3
Existing foundation models are trained on copyrighted material. Deploying these models can pose both legal and ethical risks when data creators fail to receive appropriate attribut…
Self-Destructing Models: Increasing the Costs of Harmful Dual Uses of Foundation Models
Peter Henderson, Eric Mitchell, Christopher D. Manning +2
A growing ecosystem of large, open-source foundation models has reduced the labeled data and technical expertise necessary to apply machine learning to many new problems. Yet found…
Entropy Regularization for Population Estimation
Ben Chugg, Peter Henderson, Jacob Goldin +1
Entropy regularization is known to improve exploration in sequential decision-making problems. We show that this same mechanism can also lead to nearly unbiased and lower-variance…
Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset
Peter Henderson, Mark S. Krass, Lucia Zheng +4
One concern with the rise of large language models lies with their potential for significant harm, particularly from pretraining on biased, obscene, copyrighted, and private inform…
Integrating Reward Maximization and Population Estimation: Sequential Decision-Making for Internal Revenue Service Audit Selection
Peter Henderson, Ben Chugg, Brandon Anderson +5
We introduce a new setting, optimize-and-estimate structured bandits. Here, a policy must select a batch of arms, each characterized by its own context, that would allow it to both…
Beyond Ads: Sequential Decision-Making Algorithms in Law and Public Policy
Peter Henderson, Ben Chugg, Brandon Anderson +1
We explore the promises and challenges of employing sequential decision-making algorithms -- such as bandits, reinforcement learning, and active learning -- in law and public polic…
Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims
Miles Brundage, Shahar Avin, Jasmine Wang +56
With the recent wave of progress in artificial intelligence (AI) has come a growing awareness of the large-scale impacts of AI systems, and recognition that existing regulations an…