Reassessing EMNLP 2024’s Best Paper: Does Divergence-Based Calibration for Membership Inference Attacks Hold Up?
Anshuman Suri and Pratyush Maini wrote a blog about the EMNLP 2024 best paper award winner: Reassessing EMNLP 2024’s Best Paper: Does Divergence-Based Calibration for Membership Inference Attacks Hold Up?. As we explored in Do Membership Inference Attacks Work on Large Language Models?, to test a membership inference attack it is essentail to have a candidate set where the members and non-members are from the same distribution. If the distributions are different, the ability of an attack to distinguish members and non-members is indicative of distribution inference, not necessarily membership inference.Common Way To Test for Leaks in Large Language Models May Be Flawed
UVA News has an article on our LLM membership inference work: Common Way To Test for Leaks in Large Language Models May Be Flawed: UVA Researchers Collaborated To Study the Effectiveness of Membership Inference Attacks, Eric Williamson, 13 November 2024.