Generative Model Unlearning: A Survey through Target Events, Unlearning Operators, and Evaluation Protocols

[2507.19894] Generative Model Unlearning: A Survey through Target Events, Unlearning Operators, and Evaluation Protocols Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2507.19894 (cs) [Submitted on 26 Jul 2025 ( v1 ), last revised 15 Aug 2026 (this version, v2)] Title: Generative Model Unlearning: A Survey through Target Events, Unlearning Operators, and Evaluation Protocols Authors: Xiaohua Feng , Jiaming Zhang , Fengyuan Yu , Chengye Wang , Li Zhang , Kaixiang Li , Yuyuan Li , Lingjuan Lyu , Chaochao Chen , Jianwei Yin View a PDF of the paper titled Generative Model Unlearning: A Survey through Target Events, Unlearning Operators, and Evaluation Protocols, by Xiaohua Feng and 9 other authors View PDF HTML (experimental) Abstract: With the rapid advancement of generative models, privacy, copyright, safety, and reliability risks have attracted growing attention. To mitigate these risks, machine unlearning has been increasingly adapted from traditional classification models to generative settings. Despite notable progress, existing studies remain fragmented in their target definitions, unlearning mechanisms, and evaluation protocols, making objective comparison difficult across models, modalities, and applications. Moreover, modality-specific surveys often overlook the shared structure of Generative Model Unlearning (GenMU). To address this gap, we provide a comprehensive review of GenMU and formulate it as target-constrained distributional projection: given a target event, an unlearning operator transforms the generative distribution to suppress target-related outputs while preserving useful behavior and controlling operator cost. Under this framework, an unlearning request is specified by a target event, implemented through an unlearning operator, and assessed by empirical evidence over target suppression, distribution preservation, and operator cost. We further reorganize existing GenMU studies under this view. From this perspective, we provide the first explicit and unified account of how GenMU connects to mainstream applications, including privacy protection, copyright and style protection, safety alignment, hallucination mitigation, and deployment defense. Finally, we identify key open problems and future directions toward reliable, scalable, robust, and auditable GenMU. We consistently maintain the related open-source materials at this https URL . Subjects: Machine Learning (cs.LG) Cite as: arXiv:2507.19894 [cs.LG] (or arXiv:2507.19894v2 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2507.19894 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Jiaming Zhang [ view email ] [v1] Sat, 26 Jul 2025 09:49:57 UTC (457 KB) [v2] Sat, 15 Aug 2026 13:49:25 UTC (813 KB) Full-text links: Access Paper: View a PDF of the paper titled Generative Model Unlearning: A Survey through Target Events, Unlearning Operators, and Evaluation Protocols, by Xiaohua Feng and 9 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2025-07 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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