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The significant advancements in large language models (LLMs) give rise to a promising research direction, i.e., leveraging LLMs as recommenders (LLMRec).
The eu proposal for a general data protection regulation and the roots of the ‘right to be forgotten’
Alessandro Mantelero · 2013
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Towards making systems forget with machine unlearning
Yinzhi Cao and Junfeng Yang · 2015
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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Certified data removal from machine learning models
Chuan Guo, Tom Goldstein, Awni Hannun, and Laurens Van Der Maaten · 2019
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California consumer privacy act
Erin Illman and Paul Temple · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Eternal sunshine of the spotless net: Selective forgetting in deep networks
Aditya Golatkar, Alessandro Achille, and Stefano Soatto · 2020
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Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 2020
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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
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Amnesiac machine learning
Laura Graves, Vineel Nagisetty, and Vijay Ganesh · 2021
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Lora: Low-rank adaptation of large language models
Edward J Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al · 2021
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Approximate data deletion from machine learning models
Zachary Izzo, Mary Anne Smart, Kamalika Chaudhuri, and James Zou · 2021
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Remember what you want to forget: Algorithms for machine unlearning
Ayush Sekhari, Jayadev Acharya, Gautam Kamath, and Ananda Theertha Suresh · 2021
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Recommendation unlearning
Chong Chen, Fei Sun, Min Zhang, and Bolin Ding · 2022
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Graph unlearning
Min Chen, Zhikun Zhang, Tianhao Wang, Michael Backes, Mathias Humbert, and Yang Zhang · 2022
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Unlearning protected user attributes in recommendations with adversarial training
Christian Ganhör, David Penz, Navid Rekabsaz, Oleg Lesota, and Markus Schedl · 2022
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Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5)
Shijie Geng, Shuchang Liu, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang · 2022
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Neural statistics for click-through rate prediction
Yanhua Huang, Hangyu Wang, Yiyun Miao, Ruiwen Xu, Lei Zhang, and Weinan Zhang · 2022
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Forgetting fast in recommender systems
Wenyan Liu, Juncheng Wan, Xiaoling Wang, Weinan Zhang, Dell Zhang, and Hang Li · 2022
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Deep unlearning via randomized conditionally independent hessians
Ronak Mehta, Sourav Pal, Vikas Singh, and Sathya N Ravi · 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
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Forget me now: Fast and exact unlearning in neighborhood-based recommendation
Sebastian Schelter, Mozhdeh Ariannezhad, and Maarten de Rijke · 2023
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Knowledge unlearning for llms: Tasks, methods, and challenges
Nianwen Si, Hao Zhang, Heyu Chang, Wenlin Zhang, Dan Qu, and Weiqiang Zhang · 2023
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Flip: Towards fine-grained alignment between id-based models and pretrained language models for ctr prediction
Hangyu Wang, Jianghao Lin, Xiangyang Li, Bo Chen, Chenxu Zhu, Ruiming Tang, Weinan Zhang, and Yong Yu · 2023
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A survey on large language models for recommendation, 2023
Likang Wu, Zhi Zheng, Zhaopeng Qiu, Hao Wang, Hongchao Gu, Tingjia Shen, Chuan Qin, Chen Zhu, Hengshu Zhu, Qi Liu, Hui Xiong, and Enhong Chen · 2023
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A bird’s-eye view of reranking: from list level to page level
Yunjia Xi, Jianghao Lin, Weiwen Liu, Xinyi Dai, Weinan Zhang, Rui Zhang, Ruiming Tang, and Yong Yu · 2023
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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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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Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He · 2023
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Unlearn what you want to forget: Efficient unlearning for llms
Jiaao Chen and Diyi Yang · 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
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Who’s harry potter? approximate unlearning in llms
Ronen Eldan and Mark Russinovich · 2023
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Recommender systems in the era of large language models (llms), 2023
Wenqi Fan, Zihuai Zhao, Jiatong Li, Yunqing Liu, Xiaowei Mei, Yiqi Wang, Zhen Wen, Fei Wang, Xiangyu Zhao, Jiliang Tang, and Qing Li · 2023
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On the effectiveness of unlearning in session-based recommendation
Xin Xin, Liu Yang, Ziqi Zhao, Pengjie Ren, Zhumin Chen, Jun Ma, and Zhaochun Ren · 2023
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Netflix and forget: Efficient and exact machine unlearning from bi-linear recommendations
Mimee Xu, Jiankai Sun, Xin Yang, Kevin Yao, and Chong Wang · 2023
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Large language model unlearning
Yuanshun Yao, Xiaojun Xu, and Yang Liu · 2023
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Sequence unlearning for sequential recommender systems
Shanshan Ye and Jie Lu · 2023
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Federated unlearning for on-device recommendation
Wei Yuan, Hongzhi Yin, Fangzhao Wu, Shijie Zhang, Tieke He, and Hao Wang · 2023
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A survey of large language models
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al · 2023
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Adapting large language models by integrating collaborative semantics for recommendation
Bowen Zheng, Yupeng Hou, Hongyu Lu, Yu Chen, Wayne Xin Zhao, and Ji-Rong Wen · 2023
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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
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Machine unlearning for recommendation systems: An insight
Bhavika Sachdeva, Harshita Rathee, Arun Sharma, Witold Wydmański, et al · 2024
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Prompting large language models for recommender systems: A comprehensive framework and empirical analysis, 2024
Lanling Xu, Junjie Zhang, Bingqian Li, Jinpeng Wang, Mingchen Cai, Wayne Xin Zhao, and Ji-Rong Wen · 2024
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Machine unlearning of pre-trained large language models
Jin Yao, Eli Chien, Minxin Du, Xinyao Niu, Tianhao Wang, Zezhou Cheng, and Xiang Yue · 2024
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