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Language models (LMs) are trained on vast amounts of text data, which may include private and copyrighted content.
Incremental and decremental support vector machine learning
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Incremental and decremental proximal support vector classification using decay coefficients
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The enron corpus: A new dataset for email classification research
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Nuanced metrics for measuring unintended bias with real data for text classification, 2019
Daniel Borkan, Lucas Dixon, Jeffrey Sorensen, Nithum Thain, and Lucy Vasserman · 2019
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Making ai forget you: Data deletion in machine learning
Antonio Ginart, Melody Guan, Gregory Valiant, and James Y Zou · 2019
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Saurabh Shintre, Kevin A Roundy, and Jasjeet Dhaliwal · 2019
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Mimic-iv
Alistair Johnson, Lucas Bulgarelli, Tom Pollard, Steven Horng, Leo Anthony Celi, and Roger Mark · 2020
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Gaoyang Liu, Xiaoqiang Ma, Yang Yang, Chen Wang, and Jiangchuan Liu · 2020
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Information leakage in embedding models
Congzheng Song and Ananth Raghunathan · 2020
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Deltagrad: Rapid retraining of machine learning models
Yinjun Wu, Edgar Dobriban, and Susan Davidson · 2020
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Lucas Bourtoule, Varun Chandrasekaran, Christopher A Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot · 2021
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Approximate data deletion from machine learning models
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Remember what you want to forget: Algorithms for machine unlearning
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Machine unlearning via algorithmic stability
Enayat Ullah, Tung Mai, Anup Rao, Ryan A Rossi, and Raman Arora · 2021
Privacy implications of retrieval-based language models
Yangsibo Huang, Samyak Gupta, Zexuan Zhong, Kai Li, and Danqi Chen · 2023
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Editing models with task arithmetic, 2023
Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Suchin Gururangan, Ludwig Schmidt, Hannaneh Hajishirzi, and Ali Farhadi · 2023
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Knowledge unlearning for mitigating privacy risks in language models
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Silo language models: Isolating legal risk in a nonparametric datastore
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Gpt-4 technical report, 2023
OpenAI · 2023
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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4:22-cv-06823, N.D. Cal. 2022
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Yangsibo Huang, Chun-Yin Huang, Xiaoxiao Li, and Kai Li · 2022
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Bo Liu, Qiang Liu, and Peter Stone · 2022
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Quark: Controllable text generation with reinforced unlearning
Ximing Lu, Sean Welleck, Jack Hessel, Liwei Jiang, Lianhui Qin, Peter West, Prithviraj Ammanabrolu, and Yejin Choi · 2022
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Martin Pawelczyk, Seth Neel, and Himabindu Lakkaraju · 2023
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Direct preference optimization: Your language model is secretly a reward model, 2023
Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D. Manning, and Chelsea Finn · 2023
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Nianwen Si, Hao Zhang, Heyu Chang, Wenlin Zhang, Dan Qu, and Weiqiang Zhang · 2023
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Llama 2: Open foundation and fine-tuned chat models, 2023
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom · 2023
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Neurips 2023 machine unlearning challenge, 2023
Eleni Triantafillou, Fabian Pedregosa, Jamie Hayes, Peter Kairouz, Isabelle Guyon, Meghdad Kurmanji, Gintare Karolina Dziugaite, Peter Triantafillou, Kairan Zhao, Lisheng Sun Hosoya, Julio C. S. Jacques Junior, Vincent Dumoulin, Ioannis Mitliagkas, Sergio Escalera, Jun Wan, Sohier Dane, Maggie Demkin, and Walter Reade · 2023
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Depn: Detecting and editing privacy neurons in pretrained language models
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Machine unlearning: A survey
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Large language model unlearning
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Unlearning bias in language models by partitioning gradients
Charles Yu, Sullam Jeoung, Anish Kasi, Pengfei Yu, and Heng Ji · 2023
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Forget-me-not: Learning to forget in text-to-image diffusion models
Eric Zhang, Kai Wang, Xingqian Xu, Zhangyang Wang, and Humphrey Shi · 2023
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Leace: Perfect linear concept erasure in closed form
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Fantastic copyrighted beasts and how (not) to generate them
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