9 citations · 19 across the 7 of their papers we have counts for
7 papers
Probabilistic "Copies" in Generative AI Models
Mark A. Lemley, A. Feder Cooper
Recent work shows that it is possible to extract verbatim or near-verbatim text of some copyrighted works from some large language models (LLMs or models). That is evidence that th…
Extractable Memorization From First Principles
A. Feder Cooper, Marika Swanberg, Jamie Hayes +6
Recent work on extractable memorization in language models suffers from two contrasting validity problems. Some studies overstate extraction, for example, by using sequences too sh…
Estimating near-verbatim extraction risk in language models with decoding-constrained beam search
A. Feder Cooper, Mark A. Lemley, Christopher De Sa +6
Recent work shows that standard greedy-decoding extraction methods for quantifying memorization in LLMs miss how extraction risk varies across sequences. Probabilistic extraction -…
The Mirage of Artificial Intelligence Terms of Use Restrictions
Peter Henderson, Mark A. Lemley
Artificial intelligence (AI) model creators commonly attach restrictive terms of use to both their models and their outputs. These terms typically prohibit activities ranging from…
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…