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In recent years, large language models (LLMs) have spurred a new research paradigm in natural language processing.
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Remember what you want to forget: Algorithms for machine unlearning, 2021
Ayush Sekhari, Jayadev Acharya, Gautam Kamath, and Ananda Theertha Suresh · 2021
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Editing factual knowledge in language models, 2021
Nicola De Cao, Wilker Aziz, and Ivan Titov · 2021
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Lora: Low-rank adaptation of large language models, 2021
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
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Learning transferable visual models from natural language supervision, 2021
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever · 2021
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Arcane: An efficient architecture for exact machine unlearning
Haonan Yan, Xiaoguang Li, Ziyao Guo, Hui Li, Fenghua Li, and Xiaodong Lin · 2022
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On the necessity of auditable algorithmic definitions for machine unlearning, 2022
Anvith Thudi, Hengrui Jia, Ilia Shumailov, and Nicolas Papernot · 2022
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Differentially private fine-tuning of language models, 2022
Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin A. Inan, Gautam Kamath, Janardhan Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, Sergey Yekhanin, and Huishuai Zhang · 2022
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Large language models can be strong differentially private learners, 2022
Xuechen Li, Florian Tramèr, Percy Liang, and Tatsunori Hashimoto · 2022
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Fast model editing at scale, 2022
Eric Mitchell, Charles Lin, Antoine Bosselut, Chelsea Finn, and Christopher D. Manning · 2022
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Knowledge unlearning for mitigating privacy risks in language models, 2022
Joel Jang, Dongkeun Yoon, Sohee Yang, Sungmin Cha, Moontae Lee, Lajanugen Logeswaran, and Minjoon Seo · 2022
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Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning, 2022
Haokun Liu, Derek Tam, Mohammed Muqeeth, Jay Mohta, Tenghao Huang, Mohit Bansal, and Colin Raffel · 2022
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Mass-editing memory in a transformer, 2023
Kevin Meng, Arnab Sen Sharma, Alex Andonian, Yonatan Belinkov, and David Bau · 2023
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Lingzhi Wang, Tong Chen, Wei Yuan, Xingshan Zeng, Kam-Fai Wong, and Hongzhi Yin · 2023
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Yuanshun Yao, Xiaojun Xu, and Yang Liu · 2023
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Depn: Detecting and editing privacy neurons in pretrained language models, 2023
Xinwei Wu, Junzhuo Li, Minghui Xu, Weilong Dong, Shuangzhi Wu, Chao Bian, and Deyi Xiong · 2023
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Who’s harry potter? approximate unlearning in llms, 2023
Ronen Eldan and Mark Russinovich · 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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Quantifying memorization across neural language models, 2023
Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramer, and Chiyuan Zhang · 2023
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Unlearn what you want to forget: Efficient unlearning for llms, 2023
Jiaao Chen and Diyi Yang · 2023
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Rrhf: Rank responses to align language models with human feedback without tears, 2023
Zheng Yuan, Hongyi Yuan, Chuanqi Tan, Wei Wang, Songfang Huang, and Fei Huang · 2023
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Llama 2: Open foundation and fine-tuned chat models, 2023
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