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Unlearning has been proposed to remove copyrighted and privacy-sensitive data from Large Language Models (LLMs).
Discriminant analysis
William R Klecka. 1980 · 1980
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Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2020 · 2009
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The right to be forgotten
Jeffrey Rosen. 2011 · 2011
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Towards making systems forget with machine unlearning
Yinzhi Cao and Junfeng Yang. 2015 · 2015
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The eu general data protection regulation (gdpr)
Paul Voigt and Axel Von dem Bussche. 2017 · 2017
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Universal language model fine-tuning for text classification
Jeremy Howard and Sebastian Ruder. 2018 · 2018
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Large-scale hierarchical text classification with recursively regularized deep graph-cnn
Hao Peng, Jianxin Li, Yu He, Yaopeng Liu, Mengjiao Bao, Lihong Wang, Yangqiu Song, and Qiang Yang. 2018 · 2018
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Machine unlearning
Lucas Bourtoule, Varun Chandrasekaran, Christopher A Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot. 2021 · 2021
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Online forgetting process for linear regression models
Yuantong Li, Chi-Hua Wang, and Guang Cheng. 2021 · 2021
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Machine unlearning of features and labels
Alexander Warnecke, Lukas Pirch, Christian Wressnegger, and Konrad Rieck. 2021 · 2021
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Continual learning and private unlearning
Bo Liu, Qiang Liu, and Peter Stone. 2022 · 2022
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Who’s harry potter? approximate unlearning in llms
Ronen Eldan and Mark Russinovich. 2023 · 2023
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Regulating chatgpt and other large generative ai models
Philipp Hacker, Andreas Engel, and Marco Mauer. 2023 · 2023
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Knowledge unlearning for mitigating privacy risks in language models
Joel Jang, Dongkeun Yoon, Sohee Yang, Sungmin Cha, Moontae Lee, Lajanugen Logeswaran, and Minjoon Seo. 2023 · 2023
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Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al. 2023 · 2023
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An empirical study of catastrophic forgetting in large language models during continual fine-tuning
Yun Luo, Zhen Yang, Fandong Meng, Yafu Li, Jie Zhou, and Yue Zhang. 2023 · 2023
Cited alongside, same era.
The linear representation hypothesis and the geometry of large language models
Kiho Park, Yo Joong Choe, and Victor Veitch. 2023 · 2023
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Transferable unlearnable examples
Jie Ren, Han Xu, Yuxuan Wan, Xingjun Ma, Lichao Sun, and Jiliang Tang. 2023 · 2023
Cited alongside, same era.
Knowledge unlearning for llms: Tasks, methods, and challenges
Nianwen Si, Hao Zhang, Heyu Chang, Wenlin Zhang, Dan Qu, and Weiqiang Zhang. 2023 · 2023
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On effects of steering latent representation for large language model unlearning
Dang Huu-Tien, Trung-Tin Pham, Hoang Thanh-Tung, and Naoya Inoue. 2024 · 2024
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Reversing the forget-retain objectives: An efficient llm unlearning framework from logit difference
Jiabao Ji, Yujian Liu, Yang Zhang, Gaowen Liu, Ramana Rao Kompella, Sijia Liu, and Shiyu Chang. 2024 · 2024
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Towards unbounded machine unlearning
Meghdad Kurmanji, Peter Triantafillou, Jamie Hayes, and Eleni Triantafillou. 2024 · 2024
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The wmdp benchmark: Measuring and reducing malicious use with unlearning
Nathaniel Li, Alexander Pan, Anjali Gopal, Summer Yue, Daniel Berrios, Alice Gatti, Justin D Li, Ann-Kathrin Dombrowski, Shashwat Goel, Gabriel Mukobi, et al. 2024 · 2024
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Chatgpt: a case study on copyright challenges for generative artificial intelligence systems
Nicola Lucchi. 2024 · 2024
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Yuanshun Yao, Xiaojun Xu, and Yang Liu. 2023 · 2023
Cited alongside, same era.
Investigating the catastrophic forgetting in multimodal large language models
Yuexiang Zhai, Shengbang Tong, Xiao Li, Mu Cai, Qing Qu, Yong Jae Lee, and Yi Ma. 2023 · 2023
Cited alongside, same era.
Zhiqi Bu, Xiaomeng Jin, Bhanukiran Vinzamuri, Anil Ramakrishna, Kai-Wei Chang, Volkan Cevher, and Mingyi Hong. 2024 · 2024
Cited alongside, same era.
Towards robust and cost-efficient knowledge unlearning for large language models
Sungmin Cha, Sungjun Cho, Dasol Hwang, and Moontae Lee. 2024 · 2024
Cited alongside, same era.
Do unlearning methods remove information from language model weights?
Aghyad Deeb and Fabien Roger. 2024 · 2024
Cited alongside, same era.
Does unlearning truly unlearn? a black box evaluation of llm unlearning methods
Jai Doshi and Asa Cooper Stickland. 2024 · 2024
Cited alongside, same era.
Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. 2024 · 2024
Cited alongside, same era.
Simplicity prevails: Rethinking negative preference optimization for llm unlearning
Chongyu Fan, Jiancheng Liu, Licong Lin, Jinghan Jia, Ruiqi Zhang, Song Mei, and Sijia Liu. 2024 · 2024
Cited alongside, same era.
Later among the works it cites.
Eight methods to evaluate robust unlearning in llms
Aengus Lynch, Phillip Guo, Aidan Ewart, Stephen Casper, and Dylan Hadfield-Menell. 2024 · 2024
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TOFU: A task of fictitious unlearning for LLMs
Pratyush Maini, Zhili Feng, Avi Schwarzschild, Zachary Chase Lipton, and J Zico Kolter. 2024 · 2024
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Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. 2024 · 2024
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Copyright protection in generative ai: A technical perspective
Jie Ren, Han Xu, Pengfei He, Yingqian Cui, Shenglai Zeng, Jiankun Zhang, Hongzhi Wen, Jiayuan Ding, Pei Huang, Lingjuan Lyu, et al. 2024 · 2024
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Ulmr: Unlearning large language models via negative response and model parameter average
Shaojie Shi, Xiaoyu Tan, Xihe Qiu, Chao Qu, Kexin Nie, Yuan Cheng, Wei Chu, Xu Yinghui, and Yuan Qi. 2024a · 2024
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Unstar: Unlearning with self-taught anti-sample reasoning for llms
Yash Sinha, Murari Mandal, and Mohan Kankanhalli. 2024 · 2024
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To forget or not? towards practical knowledge unlearning for large language models
Bozhong Tian, Xiaozhuan Liang, Siyuan Cheng, Qingbin Liu, Mengru Wang, Dianbo Sui, Xi Chen, Huajun Chen, and Ningyu Zhang. 2024 · 2024
Later among the works it cites.
Llm surgery: Efficient knowledge unlearning and editing in large language models
Akshaj Kumar Veldanda, Shi-Xiong Zhang, Anirban Das, Supriyo Chakraborty, Stephen Rawls, Sambit Sahu, and Milind Naphade. 2024 · 2024
Later among the works it cites.
Exploring concept depth: How large language models acquire knowledge and concept at different layers?
Mingyu Jin, Qinkai Yu, Jingyuan Huang, Qingcheng Zeng, Zhenting Wang, Wenyue Hua, Haiyan Zhao, Kai Mei, Yanda Meng, Kaize Ding, et al. 2025 · 2025
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