@inproceedings{li-etal-2026-forgetting,
title = "Forgetting Across Modalities: Unlearning in Multimodal Large Language Models",
author = "Li, Yan and
Yu, Xingrui and
Wang, Zhiqiang and
Wang, Zhenyi and
Tsang, Ivor",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1327/",
pages = "30241--30261",
ISBN = "979-8-89176-459-0",
abstract = "Gradient conflict between forgetting and retaining objectives is a well-known obstacle in machine unlearning, where opposing gradients on shared parameters cause destructive interference. In Multimodal Large Language Models (MLLMs), cross-modal coupling entangles visual and textual representations within the same parameters, making this conflict considerably more severe. We propose EASE (Efficient Alignment-aware Selective Erasing), which explicitly balances the conflicting gradient directions. EASE first dynamically locates the conflicting parameters at each step by detecting opposite-sign gradients, then performs a manifold-constrained optimization within the two-dimensional subspace spanned by the task gradients to find the linear combination that maximally aligns them, yielding a valid descent direction for both objectives without any manually tuned trade-off coefficient. Nonconflicting parameters keep their standard updates, keeping the method lightweight. Experiments across multiple MLLM architectures and unlearning benchmarks show that EASE achieves superior unlearning efficiency and faster convergence while matching or surpassing existing methods in multimodal quality."
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<abstract>Gradient conflict between forgetting and retaining objectives is a well-known obstacle in machine unlearning, where opposing gradients on shared parameters cause destructive interference. In Multimodal Large Language Models (MLLMs), cross-modal coupling entangles visual and textual representations within the same parameters, making this conflict considerably more severe. We propose EASE (Efficient Alignment-aware Selective Erasing), which explicitly balances the conflicting gradient directions. EASE first dynamically locates the conflicting parameters at each step by detecting opposite-sign gradients, then performs a manifold-constrained optimization within the two-dimensional subspace spanned by the task gradients to find the linear combination that maximally aligns them, yielding a valid descent direction for both objectives without any manually tuned trade-off coefficient. Nonconflicting parameters keep their standard updates, keeping the method lightweight. Experiments across multiple MLLM architectures and unlearning benchmarks show that EASE achieves superior unlearning efficiency and faster convergence while matching or surpassing existing methods in multimodal quality.</abstract>
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%0 Conference Proceedings
%T Forgetting Across Modalities: Unlearning in Multimodal Large Language Models
%A Li, Yan
%A Yu, Xingrui
%A Wang, Zhiqiang
%A Wang, Zhenyi
%A Tsang, Ivor
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F li-etal-2026-forgetting
%X Gradient conflict between forgetting and retaining objectives is a well-known obstacle in machine unlearning, where opposing gradients on shared parameters cause destructive interference. In Multimodal Large Language Models (MLLMs), cross-modal coupling entangles visual and textual representations within the same parameters, making this conflict considerably more severe. We propose EASE (Efficient Alignment-aware Selective Erasing), which explicitly balances the conflicting gradient directions. EASE first dynamically locates the conflicting parameters at each step by detecting opposite-sign gradients, then performs a manifold-constrained optimization within the two-dimensional subspace spanned by the task gradients to find the linear combination that maximally aligns them, yielding a valid descent direction for both objectives without any manually tuned trade-off coefficient. Nonconflicting parameters keep their standard updates, keeping the method lightweight. Experiments across multiple MLLM architectures and unlearning benchmarks show that EASE achieves superior unlearning efficiency and faster convergence while matching or surpassing existing methods in multimodal quality.
%U https://aclanthology.org/2026.emnlp-main.1327/
%P 30241-30261
Markdown (Informal)
[Forgetting Across Modalities: Unlearning in Multimodal Large Language Models](https://aclanthology.org/2026.emnlp-main.1327/) (Li et al., EMNLP 2026)
ACL