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The evolving paradigm of Large Language Model-based Recommendation (LLMRec) customizes Large Language Models (LLMs) through parameter-efficient fine-tuning (PEFT) using recommendation data.
Improving recommendation lists through topic diversification. In Proceedings of the 14th international conference on World Wide Web . 22–32
Cai-Nicolas Ziegler, Sean M McNee, Joseph A Konstan, and Georg Lausen. 2005 · 2005
Earlier work this paper cites.
Towards making systems forget with machine unlearning. In 2015 IEEE symposium on security and privacy . IEEE, 463–480
Yinzhi Cao and Junfeng Yang. 2015 · 2015
Earlier work this paper cites.
The movielens datasets: History and context
F Maxwell Harper and Joseph A Konstan. 2015 · 2015
Earlier work this paper cites.
Approximate data deletion from machine learning models. In International Conference on Artificial Intelligence and Statistics . PMLR, 2008–2016
Zachary Izzo, Mary Anne Smart, Kamalika Chaudhuri, and James Zou. 2021 · 2016
Earlier work this paper cites.
General data protection regulation (GDPR)
General Data Protection Regulation. 2018 · 2018
Earlier work this paper cites.
Parameter-efficient transfer learning for NLP. In International Conference on Machine Learning . PMLR, 2790–2799
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019 · 2019
Earlier work this paper cites.
Intrinsic dimensionality explains the effectiveness of language model fine-tuning
Armen Aghajanyan, Luke Zettlemoyer, and Sonal Gupta. 2020 · 2020
Earlier work this paper cites.
Lightgcn: Simplifying and powering graph convolution network for recommendation. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval . 639–648
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020 · 2020
Earlier work this paper cites.
Fawkes: Protecting privacy against unauthorized deep learning models. In 29th USENIX security symposium (USENIX Security 20) . 1589–1604
Shawn Shan, Emily Wenger, Jiayun Zhang, Huiying Li, Haitao Zheng, and Ben Y Zhao. 2020 · 2020
Earlier work this paper cites.
Analyzing information leakage of updates to natural language models. In Proceedings of the 2020 ACM SIGSAC conference on computer and communications security . 363–375
Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople, Victor Rühle, Andrew Paverd, Olga Ohrimenko, Boris Köpf, and Marc Brockschmidt. 2020 · 2020
Earlier work this paper cites.
Machine unlearning. In 2021 IEEE Symposium on Security and Privacy (SP) . IEEE, 141–159
Lucas Bourtoule, Varun Chandrasekaran, Christopher A Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot. 2021 · 2021
Earlier work this paper cites.
Extracting training data from large language models. In 30th USENIX Security Symposium (USENIX Security 21) . 2633–2650
Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, et al · 2021
Earlier work this paper cites.
Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021 · 2021
Earlier work this paper cites.
Recommendation unlearning. In Proceedings of the ACM Web Conference 2022 . 2768–2777
Chong Chen, Fei Sun, Min Zhang, and Bolin Ding. 2022a · 2022
Earlier work this paper cites.
Graph unlearning. In Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security . 499–513
Min Chen, Zhikun Zhang, Tianhao Wang, Michael Backes, Mathias Humbert, and Yang Zhang. 2022b · 2022
Cited alongside, same era.
Forgetting Fast in Recommender Systems
Wenyan Liu, Juncheng Wan, Xiaoling Wang, Weinan Zhang, Dell Zhang, and Hang Li. 2022 · 2022
Cited alongside, same era.
Hard to forget: Poisoning attacks on certified machine unlearning. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 36. 7691–7700
Neil G Marchant, Benjamin IP Rubinstein, and Scott Alfeld. 2022 · 2022
Cited alongside, same era.
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
Cited alongside, same era.
Llama-adapter v2: Parameter-efficient visual instruction model
Peng Gao, Jiaming Han, Renrui Zhang, Ziyi Lin, Shijie Geng, Aojun Zhou, Wei Zhang, Pan Lu, Conghui He, Xiangyu Yue, et al · 2023
Later among the works it cites.
Challenges and applications of large language models
Jean Kaddour, Joshua Harris, Maximilian Mozes, Herbie Bradley, Roberta Raileanu, and Robert McHardy. 2023 · 2023
Later among the works it cites.
A multi-facet paradigm to bridge large language model and recommendation
Xinyu Lin, Wenjie Wang, Yongqi Li, Fuli Feng, See-Kiong Ng, and Tat-Seng Chua. 2023 · 2023
Later among the works it cites.
In-Context Unlearning: Language Models as Few Shot Unlearners
Martin Pawelczyk, Seth Neel, and Himabindu Lakkaraju. 2023 · 2023
Later among the works it cites.
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Wei Qian, Chenxu Zhao, Huajie Shao, Minghan Chen, Fei Wang, and Mengdi Huai. 2022 · 2022
Cited alongside, same era.
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time. In International Conference on Machine Learning . PMLR, 23965–23998
Mitchell Wortsman, Gabriel Ilharco, Samir Ya Gadre, Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, et al · 2022
Cited alongside, same era.
Arcane: An efficient architecture for exact machine unlearning. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22 . 4006–4013
Haonan Yan, Xiaoguang Li, Ziyao Guo, Hui Li, Fenghua Li, and Xiaodong Lin. 2022 · 2022
Cited alongside, same era.
A bi-step grounding paradigm for large language models in recommendation systems
Keqin Bao, Jizhi Zhang, Wenjie Wang, Yang Zhang, Zhengyi Yang, Yancheng Luo, Fuli Feng, Xiangnaan He, and Qi Tian. 2023a · 2023
Cited alongside, same era.
TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation. In Proceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023 . 1007–1014
Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He. 2023b · 2023
Cited alongside, same era.
Aldo Gael Carranza, Rezsa Farahani, Natalia Ponomareva, Alex Kurakin, Matthew Jagielski, and Milad Nasr. 2023 · 2023
Cited alongside, same era.
Shizhe Diao, Tianyang Xu, Ruijia Xu, Jiawei Wang, and Tong Zhang. 2023 · 2023
Cited alongside, same era.
Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models
Haonan Duan, Adam Dziedzic, Nicolas Papernot, and Franziska Boenisch. 2023 · 2023
Cited alongside, same era.
Karan Singhal, Shekoofeh Azizi, Tao Tu, S Sara Mahdavi, Jason Wei, Hyung Won Chung, Nathan Scales, Ajay Tanwani, Heather Cole-Lewis, Stephen Pfohl, et al · 2023
Later among the works it cites.
Fast yet effective machine unlearning
Ayush K Tarun, Vikram S Chundawat, Murari Mandal, and Mohan Kankanhalli. 2023 · 2023
Later among the works it cites.
MT4CrossOIE: Multi-stage Tuning for Cross-lingual Open Information Extraction
Zixiang Wang, Linzheng Chai, Jian Yang, Jiaqi Bai, Yuwei Yin, Jiaheng Liu, Hongcheng Guo, Tongliang Li, Liqun Yang, Zhoujun Li, et al · 2023
Later among the works it cites.
Machine Unlearning: A Survey
Heng Xu, Tianqing Zhu, Lefeng Zhang, Wanlei Zhou, and Philip S. Yu. 2023 · 2023
Later among the works it cites.
Large Language Model Unlearning
Yuanshun Yao, Xiaojun Xu, and Yang Liu. 2023 · 2023
Later among the works it cites.
Recommendation unlearning via influence function
Yang Zhang, Zhiyu Hu, Yimeng Bai, Fuli Feng, Jiancan Wu, Qifan Wang, and Xiangnan He. 2023b · 2023
Later among the works it cites.
Reformulating CTR Prediction: Learning Invariant Feature Interactions for Recommendation
Yang Zhang, Tianhao Shi, Fuli Feng, Wenjie Wang, Dingxian Wang, Xiangnan He, and Yongdong Zhang. 2023c · 2023
Later among the works it cites.
Multimodal chain-of-thought reasoning in language models
Zhuosheng Zhang, Aston Zhang, Mu Li, Hai Zhao, George Karypis, and Alex Smola. 2023d · 2023
Later among the works it cites.
Exploring Adapter-based Transfer Learning for Recommender Systems: Empirical Studies and Practical Insights
Junchen Fu, Fajie Yuan, Yu Song, Zheng Yuan, Mingyue Cheng, Shenghui Cheng, Jiaqi Zhang, Jie Wang, and Yunzhu Pan. 2024 · 2024
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