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cs.LG2026
Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks
Nobin Sarwar, Shubhashis Roy Dipta, Zheyuan Liu +1
With the growing adoption of VLMs, DMs, LLMs, and AFMs, these multimodal foundation models can inadvertently encode sensitive, copyrighted, biased, or unsafe cross-modal associatio…
cs.LG2025
Generative Model Unlearning: A Survey through Target Events, Unlearning Operators, and Evaluation Protocols
Xiaohua Feng, Jiaming Zhang, Fengyuan Yu +7
With the rapid advancement of generative models, privacy, copyright, safety, and reliability risks have attracted growing attention. To mitigate these risks, machine unlearning has…
cs.LG2025
A Neuro-inspired Interpretation of Unlearning in Large Language Models through Sample-level Unlearning Difficulty
Xiaohua Feng, Yuyuan Li, Chengye Wang +3
Driven by privacy protection laws and regulations, unlearning in Large Language Models (LLMs) is gaining increasing attention. However, current research often neglects the interpre…