Jing-Kun Xue

Not Every Change Is Necessary: Recoverable Drift in Large Language Model Unlearning

Machine unlearning in large language models aims to remove unwanted knowledge while preserving the model's remaining capabilities. Although existing methods use retention objectives or restrict where edits occur, achieving the desired forgetting level can still leave collateral changes that impair non-target behavior....