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arXiv Machine Learning By Chenlu Ding, Jiancan Wu, Leheng Sheng, Fan Zhang, Yancheng Yuan, Xiang Wang, Xiangnan He

MLLMEraser: Achieving Test-Time Unlearning in Multimodal Large Language Models through Activation Steering

Read the original on arXiv Machine Learning →

MLLMEraser is a training‑free, input‑aware framework that achieves test‑time unlearning for multimodal large language models by steering activations. It constructs an erasure direction through adversarial contrast between knowledge‑recall and knowledge‑erasure image‑text pairs, and applies this direction selectively to preserve retained knowledge while forgetting specified content. Experiments on LLaVA‑1.5 and Qwen‑2.5‑VL show that MLLMEraser outperforms existing unlearning baselines with lower computational cost and minimal utility loss.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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