Does Machine Unlearning Truly Remove Model Knowledge? A Framework for Auditing Unlearning in LLMs

This paper introduces a comprehensive auditing framework to evaluate the effectiveness of machine unlearning algorithms in removing sensitive information from Large Language Models (LLMs), addressing privacy and ownership concerns. It includes benchmark datasets, multiple unlearning methods, and novel techniques such as intermediate activation perturbations to improve robustness beyond traditional prompt-based assessments.