Do Unlearning Methods Remove Information from Language Model Weights?

Aghyad Deeb, Fabien Roger

Keywords: Unlearning, LLMs, Evaluation, RMU, Fine-tuning, Red-teaming, Safety

TL;DR: We evaluate whether unlearning removes information from the weights by training on some harmful facts and evaluating accuracy on others.

Abstract: Large Language Models' knowledge of how to perform cyber-security attacks, create bioweapons, and manipulate humans poses risks of misuse. Previous work has proposed methods to unlearn this knowledge. Historically, it has been unclear whether unlearning techniques are removing information from the model weights or just making it harder to access. To disentangle these two objectives, we propose an adversarial evaluation method to test for the removal of information from model weights: we give an attacker access to some facts that were supposed to be removed, and using those, the attacker tries to recover other facts from the same distribution that cannot be guessed from the accessible facts. We show that using fine-tuning on the accessible facts can recover 88% of the pre-unlearning accuracy when applied to current unlearning methods, revealing the limitations of these methods in removing information from the model weights.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

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Submission Number: 3850