RULER: Representation-Level Verification of Machine Unlearning

The paper introduces RULER, a set of representation-level verification metrics for machine unlearning, which aims to remove the influence of specific training records from a deployed model. Unlike current output-level evaluations, RULER detects residuals of forgotten records in intermediate representations, revealing that approximate unlearning methods may still encode forgotten information.