Decouple to Forget: Semantic Mediation for Sensitive Unlearning in Large Language Models
Machine unlearning for large language models seeks to remove specified knowledge while preserving general capability, yet current methods often treat an entire response as the unit of forgetting. This coarse granularity causes unnecessary utility loss when the forget target is only a small set of sensitive facts embedded within otherwise benign explanations. In this paper, we introduce Decouple to Forget (D2F), a targeted unlearning method inspired by semantic mediation that separates sensitive factual content from non-sensitive meaning and surface expression. For each example to be forgotten, D2F extracts a fact-level "mediator" representation from the answer, identifies which parts are ...
| Authors | Jingyue Cong, Andy Song, Yang Yu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 13 October 2026 |
| Last updated at source | 13 October 2026 |
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