aiqbench analysis
The paper proposes MLLMEraser, a training-free method that uses activation steering to make multimodal models "forget" designated content at inference time, without gradient-based retraining. Its reported gains are on LLaVA-1.5 and Qwen-2.5-VL, and the claims of stronger forgetting, lower cost, and minimal utility loss still need independent replication across more models and benchmarks. For readers, the practical question is whether test-time erasure can reliably handle privacy and harmful-content removal without the irreversibility and collateral damage of fine-tuning-based unlearning.
MLLMEraser: Achieving Test-Time Unlearning in Multimodal Large Language Models through Activation Steering
arXiv:2510.04217v4 Announce Type: replace Abstract: Multimodal large language models (MLLMs) have demonstrated remarkable capabilities across vision-language tasks, yet their large-scale deployment raises pressing concerns about memorized private data, outdated knowledge, and harmful content. Existing unlearning approaches for MLLMs typically adapt training-based strategies such as gradient ascent or preference optimization, but these methods are computationally expensive, irreversible, and often distort retained knowledge. In this work, we propose MLLMEraser, an input-aware, training-free framework for test-time unlearning. Our approach leverages activation steering to enable dynamic knowledge erasure without parameter updates. Specifically, we construct a multimodal erasure direction by contrasting adversarially perturbed, knowledge-recall image-text pairs with knowledge-erasure counterparts, capturing both textual and visual discrepancies. To prevent unnecessary interference, we further design an input-aware steering mechanism that adaptively determines when and how the erasure direction should be applied, preserving utility on retained knowledge while enforcing forgetting on designated content. Experiments on LLaVA-1.5 and Qwen-2.5-VL demonstrate that MLLMEraser consistently outperforms state-of-the-art MLLM unlearning baselines, achieving stronger forgetting performance with lower computational cost and minimal utility degradation.
Abstract:Multimodal large language models (MLLMs) have demonstrated remarkable capabilities across vision-language tasks, yet their large-scale deployment raises pressing concerns about memorized private data, outdated knowledge, and harmful content. Existing unlearning approaches for MLLMs typically adapt training-based strategies such as gradient ascent or preference optimization, but these methods are computationally expensive, irreversible, and often distort retained knowledge. In this work, we propose MLLMEraser, an input-aware, training-free framework for test-time unlearning. Our approach leverages activation steering to enable dynamic knowledge erasure without parameter updates. Specifically, we construct a multimodal erasure direction by contrasting adversarially perturbed, knowledge-recall image-text pairs with knowledge-erasure counterparts, capturing both textual and visual discrepancies. To prevent unnecessary interference, we further design an input-aware steering mechanism that adaptively determines when and how the erasure direction should be applied, preserving utility on retained knowledge while enforcing forgetting on designated content. Experiments on LLaVA-1.5 and Qwen-2.5-VL demonstrate that MLLMEraser consistently outperforms state-of-the-art MLLM unlearning baselines, achieving stronger forgetting performance with lower computational cost and minimal utility degradation.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2510.04217 [cs.LG]
(or arXiv:2510.04217v4 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2510.04217 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 260 words