Distinguishable Deletion: Unifying Knowledge Erasure and Refusal for Large Language Model Unlearning
Mitigating sensitive and harmful outputs is fundamental to ensuring safe deployment of LLMs. Existing approaches typically follow two paradigms: Knowledge Deletion (KD), which erases undesirable information during training, and Distinguishable Refusal (DR), which steers models away from using sensitive knowledge during inference. Despite rapid progress, KD-based unlearning struggles with biased deletion due to suppressing specific token sequences as a substitute for complete knowledge removal, whereas DR-based unlearning risks the re-emergence of harmful knowledge because the underlying knowledge remains intact. To address these issues, we propose Distinguishable Deletion ($\mathrm{D^2}$)...
| Authors | Puning Yang, Junchi Yu, Qizhou Wang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 16 May 2026 |
| Last updated at source | 16 May 2026 |
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