Linear Token Unlearning: A Zero-Dependency Framework for Multimodal LLM Unlearning

Machine unlearning aims to eliminate the influence of designated training data from a model to preserve user privacy. For multi-modal large language models (MLLMs), this requires removing cross-modal and uni-modal knowledge; however, most existing MLLM unlearning frameworks only address the cross-modal case. Furthermore, these methods rely on external resources such as retain sets, auxiliary models, or human annotations to guide the unlearning process, significantly limiting their practical applicability. To address these shortcomings, we propose Linear Token Unlearning (LTU), a fast, resource-free token-level optimization framework capable of removing both cross-modal and uni-modal knowledge. LTU employs a first-order linear approximation to identify the tokens causally linked to the memorization of a given sequence in an efficient single-backward pass while avoiding the perturbation bias of prior brute-force scoring; it performs targeted token-level optimization on these tokens to remove the cross-modal and uni-modal knowledge. Experimental results demonstrate that LTU achieves up to a 7 improvement in forget quality over existing MLLM unlearning baselines while maintaining comparable model utility. To the best of our knowledge, LTU is the first MLLM unlearning framework that removes both cross-modal and uni-modal knowledge without reliance on any auxiliary resources.