3 papers
cs.LG2025
Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy and Research
A. Feder Cooper, Christopher A. Choquette-Choo, Miranda Bogen +34
"Machine unlearning" is a popular proposed solution for mitigating the existence of content in an AI model that is problematic for legal or moral reasons, including privacy, copyri…
cs.CL2025
Language Models May Verbatim Complete Text They Were Not Explicitly Trained On
Ken Ziyu Liu, Christopher A. Choquette-Choo, Matthew Jagielski +4
An important question today is whether a given text was used to train a large language model (LLM). A \emph{completion} test is often employed: check if the LLM completes a suffici…
cs.LG2025
Exploring and Mitigating Adversarial Manipulation of Voting-Based Leaderboards
Yangsibo Huang, Milad Nasr, Anastasios Angelopoulos +10
It is now common to evaluate Large Language Models (LLMs) by having humans manually vote to evaluate model outputs, in contrast to typical benchmarks that evaluate knowledge or ski…