Large Language Model

Using LLMs to Uncover Memorization in Instruction-Tuned Models

A study introducing a black-box prompt optimization approach to uncover higher levels of memorization in instruction-tuned LLMs.

Do Membership Inference Attacks Work on Large Language Models?

This paper evaluates the effectiveness of membership inference attacks on large language models, revealing that such attacks often perform no better than random guessing.

Membership Inference Attacks Against Fine-tuned Large Language Models via Self-prompt Calibration

A novel study introducing self-prompt calibration for membership inference attacks (MIAs) against fine-tuned large language models, improving reliability and practicality in privacy assessments.