Suppression Is Not Forgetting: Residual Recoverability in Visual Concept Unlearning for VLMs
Vision-language models (VLMs) may need to forget visual concepts after deployment because of privacy, copyright, licensing, safety, or policy changes. Conventional machine unlearning modifies model parameters, which may be costly or inaccessible for API-only models. Prompt-based suppression offers a training-free alternative, but does it make a concept inaccessible or merely change the model's answer? We investigate this question in off-the-shelf VLMs. Our visually grounded, multi-probe evaluation first verifies that a model recognizes each concept from the image, then tests its recoverability through multiple-choice, short-answer, and indirect queries. Across objects, scenes, and identit...
| Authors | Zhangyun Tan, Zeliang Zhang, Jiani Liu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 3 April 2026 |
| Last updated at source | 28 September 2026 |
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