Decoupling Knowledge Forgetting and Refusal Learning in MLLM Unlearning
Current evaluations of multimodal unlearning typically focus on whether a model stops outputting target information. This surface-level criterion, however, masks two critical failure modes. First, knowledge suppression techniques often induce confabulation, leading the model to hallucinate facts about the unlearned subject. Second, refusal training merely alters observable behavior without erasing the underlying knowledge; such models might decline open queries but still identify correct answers under multiple-choice probing. To overcome these limitations, we propose Forget-Aware Self-Distillation (FASD). This method is designed to achieve honest ignorance, a state where the model genuine...
| Authors | Haodong Chen, Shengtao Wen, Dong Liang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 10 October 2026 |
| Last updated at source | 10 October 2026 |
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