Intrinsic Test of Unlearning Using Parametric Knowledge Traces

Understand

The task of "unlearning" certain concepts in large language models (LLMs) has attracted immense attention recently, due to its importance in mitigating undesirable model behaviours, such as the generation of harmful, private, or incorrect information.

• Current protocols to evaluate unlearning methods largely rely on behavioral tests, without monitoring the presence of unlearned knowledge within the model's parameters.
• This residual knowledge can be adversarially exploited to recover the erased information post-unlearning.
• We argue that unlearning should also be evaluated internally, by considering changes in the parametric knowledge traces of the unlearned concepts.