Attention Smoothing Is All You Need For Unlearning
This addresses privacy and legal concerns for LLM users by improving unlearning stability, though it is an incremental method building on existing unlearning approaches.
The paper tackles the problem of removing memorized sensitive content from Large Language Models without retraining, proposing Attention Smoothing Unlearning (ASU) which flattens attention distributions to suppress associations, resulting in robust unlearning with minimal utility loss across benchmarks like TOFU, MUSE, and WMDP.
Large Language Models are prone to memorizing sensitive, copyrighted, or hazardous content, posing significant privacy and legal concerns. Retraining from scratch is computationally infeasible, whereas current unlearning methods exhibit unstable trade-offs between forgetting and utility, frequently producing incoherent outputs on forget prompts and failing to generalize due to the persistence of lexical-level and semantic-level associations in attention. We propose Attention Smoothing Unlearning (ASU), a principled framework that casts unlearning as self-distillation from a forget-teacher derived from the model's own attention. By increasing the softmax temperature, ASU flattens attention distributions and directly suppresses the lexical-level and semantic-level associations responsible for reconstructing memorized knowledge. This results in a bounded optimization objective that erases factual information yet maintains coherence in responses to forget prompts. Empirical evaluation on TOFU, MUSE, and WMDP, along with real-world and continual unlearning scenarios across question answering and text completion, demonstrates that ASU outperforms the baselines for most unlearning scenarios, delivering robust unlearning with minimal loss of model utility.