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Machine unlearning aims to efficiently eliminate the influence of specific training data, known as the forget set, from the model.
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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When machine unlearning jeopardizes privacy
Min Chen, Zhikun Zhang, Tianhao Wang, Michael Backes, Mathias Humbert, and Yang Zhang. 2021 · 2021
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Amnesiac machine learning
Laura Graves, Vineel Nagisetty, and Vijay Ganesh. 2021 · 2021
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Language models (mostly) know what they know
Saurav Kadavath, Tom Conerly, Amanda Askell, Tom Henighan, Dawn Drain, Ethan Perez, Nicholas Schiefer, Zac Hatfield-Dodds, Nova DasSarma, Eli Tran-Johnson, et al. 2022 · 2022
Earlier work this paper cites.
Learn to forget: Machine unlearning via neuron masking
Zhuo Ma, Yang Liu, Ximeng Liu, Jian Liu, Jianfeng Ma, and Kui Ren. 2022 · 2022
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A survey of machine unlearning
Thanh Tam Nguyen, Thanh Trung Huynh, Phi Le Nguyen, Alan Wee-Chung Liew, Hongzhi Yin, and Quoc Viet Hung Nguyen. 2022 · 2022
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Unlearn what you want to forget: Efficient unlearning for llms
Jiaao Chen and Diyi Yang. 2023 · 2023
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Can large language models be an alternative to human evaluations?
Cheng-Han Chiang and Hung-yi Lee. 2023 · 2023
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Can bad teaching induce forgetting? unlearning in deep networks using an incompetent teacher
Vikram S Chundawat, Ayush K Tarun, Murari Mandal, and Mohan Kankanhalli. 2023 · 2023
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Who’s harry potter? approximate unlearning in llms
Ronen Eldan and Mark Russinovich. 2023 · 2023
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Survey of hallucination in natural language generation
Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. 2023 · 2023
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Copyright violations and large language models
Antonia Karamolegkou, Jiaang Li, Li Zhou, and Anders Søgaard. 2023 · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023 · 2023
Cited alongside, same era.
Large language model unlearning
Yuanshun Yao, Xiaojun Xu, and Yang Liu. 2023 · 2023
Cited alongside, same era.
Right to be forgotten in the era of large language models: Implications, challenges, and solutions
Dawen Zhang, Pamela Finckenberg-Broman, Thong Hoang, Shidong Pan, Zhenchang Xing, Mark Staples, and Xiwei Xu. 2023 · 2023
Cited alongside, same era.
Soft prompting for unlearning in large language models
Karuna Bhaila, Minh-Hao Van, and Xintao Wu. 2024 · 2024
Cited alongside, same era.
Unmemorization in large language models via self-distillation and deliberate imagination
Yoonhwa Jung, Ikhyun Cho, Shun-Hsiang Hsu, and Julia Hockenmaier. 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, Long Phan, et al. 2024 · 2024
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Machine unlearning in 2024
Ken Ziyu Liu. 2024 · 2024
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Tofu: A task of fictitious unlearning for llms
Pratyush Maini, Zhili Feng, Avi Schwarzschild, Zachary C Lipton, and J Zico Kolter. 2024 · 2024
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Yijiang River Dong, Hongzhou Lin, Mikhail Belkin, Ramon Huerta, and Ivan Vulić. 2024 · 2024
Cited alongside, same era.
Do membership inference attacks work on large language models?
Michael Duan, Anshuman Suri, Niloofar Mireshghallah, Sewon Min, Weijia Shi, Luke Zettlemoyer, Yulia Tsvetkov, Yejin Choi, David Evans, and Hannaneh Hajishirzi. 2024 · 2024
Cited alongside, same era.
Erasing conceptual knowledge from language models
Rohit Gandikota, Sheridan Feucht, Samuel Marks, and David Bau. 2024 · 2024
Cited alongside, same era.
Practical unlearning for large language models
Chongyang Gao, Lixu Wang, Chenkai Weng, Xiao Wang, and Qi Zhu. 2024 · 2024
Cited alongside, same era.
Offset unlearning for large language models
James Y. Huang, Wenxuan Zhou, Fei Wang, Fred Morstatter, Sheng Zhang, Hoifung Poon, and Muhao Chen. 2024 · 2024
Cited alongside, same era.
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
Cited alongside, same era.
Soul: Unlocking the power of second-order optimization for llm unlearning
Jinghan Jia, Yihua Zhang, Yimeng Zhang, Jiancheng Liu, Bharat Runwal, James Diffenderfer, Bhavya Kailkhura, and Sijia Liu. 2024 · 2024
Cited alongside, same era.
Rwku: Benchmarking real-world knowledge unlearning for large language models
Zhuoran Jin, Pengfei Cao, Chenhao Wang, Zhitao He, Hongbang Yuan, Jiachun Li, Yubo Chen, Kang Liu, and Jun Zhao. 2024 · 2024
Cited alongside, same era.
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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A probabilistic perspective on unlearning and alignment for large language models
Yan Scholten, Stephan Günnemann, and Leo Schwinn. 2024 · 2024
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Detecting pretraining data from large language models
Weijia Shi, Anirudh Ajith, Mengzhou Xia, Yangsibo Huang, Daogao Liu, Terra Blevins, Danqi Chen, and Luke Zettlemoyer. 2024 · 2024
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Guardrail baselines for unlearning in llms
Pratiksha Thaker, Yash Maurya, and Virginia Smith. 2024 · 2024
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Are we making progress in unlearning? findings from the first neurips unlearning competition
Eleni Triantafillou, Peter Kairouz, Fabian Pedregosa, Jamie Hayes, Meghdad Kurmanji, Kairan Zhao, Vincent Dumoulin, Julio Jacques Junior, Ioannis Mitliagkas, Jun Wan, et al. 2024 · 2024
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Negative preference optimization: From catastrophic collapse to effective unlearning
Ruiqi Zhang, Licong Lin, Yu Bai, and Song Mei. 2024 · 2024
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