Intrinsic Evaluation of Unlearning Using Parametric Knowledge Traces

ACL ARR 2025 February Submission915 Authors

Abstract: 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. To this end, we propose a general evaluation methodology that leverages vocabulary projections to inspect concepts encoded in model parameters. We use this approach to localize “concept vectors” — parameter vectors that encode concrete concepts — and construct ConceptVectors, a benchmark dataset containing hundreds of common concepts and their parametric knowledge traces within two open-source LLMs. Evaluation on ConceptVectors shows that existing unlearning methods minimally impact concept vectors and mostly suppress them during inference, while directly ablating these vectors demonstrably removes the associated knowledge and significantly reduces the model’s susceptibility to adversarial manipulation. Our results highlight limitations in behavioral-based unlearning evaluations and call for future work to include parameter-based evaluations. To support this, we release our code and benchmark at https://anonymous.4open.science/r/ConceptVectors_review-98EF.

Paper Type: Long

Research Area: Interpretability and Analysis of Models for NLP

Research Area Keywords: LLM Interpretability, LLM Unlearning, LLM Safety, benchmark, evaluations

Contribution Types: Model analysis & interpretability

Languages Studied: English, German.

Submission Number: 915