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In this work, we introduce the task of life-long personalization of large language models.
Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2005
Earlier work this paper cites.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al. 2018 · 2018
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
Earlier work this paper cites.
Explainable recommendations via attentive multi-persona collaborative filtering
Oren Barkan, Yonatan Fuchs, Avi Caciularu, and Noam Koenigstein. 2020 · 2020
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.
Training language models to follow instructions with human feedback
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe. 2022 · 2022
Earlier work this paper cites.
Agentverse: Facilitating multi-agent collaboration and exploring emergent behaviors
Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, and Jie Zhou. 2023 · 2023
Earlier work this paper cites.
Large language models for user interest journeys
Konstantina Christakopoulou, Alberto Lalama, Cj Adams, Iris Qu, Yifat Amir, Samer Chucri, Pierce Vollucci, Fabio Soldo, Dina Bseiso, Sarah Scodel, et al. 2023 · 2023
Earlier work this paper cites.
Assistgui: Task-oriented desktop graphical user interface automation
Difei Gao, Lei Ji, Zechen Bai, Mingyu Ouyang, Peiran Li, Dongxing Mao, Qinchen Wu, Weichen Zhang, Peiyi Wang, Xiangwu Guo, et al. 2023 · 2023
Earlier work this paper cites.
Faithful persona-based conversational dataset generation with large language models
Pegah Jandaghi, XiangHai Sheng, Xinyi Bai, Jay Pujara, and Hakim Sidahmed. 2023 · 2023
Earlier work this paper cites.
Do llms understand user preferences? evaluating llms on user rating prediction
Wang-Cheng Kang, Jianmo Ni, Nikhil Mehta, Maheswaran Sathiamoorthy, Lichan Hong, Ed Chi, and Derek Zhiyuan Cheng. 2023 · 2023
Cited alongside, same era.
Teach llms to personalize–an approach inspired by writing education
Cheng Li, Mingyang Zhang, Qiaozhu Mei, Yaqing Wang, Spurthi Amba Hombaiah, Yi Liang, and Michael Bendersky. 2023 · 2023
Cited alongside, same era.
Agentbench: Evaluating llms as agents
Xiao Liu, Hao Yu, Hanchen Zhang, Yifan Xu, Xuanyu Lei, Hanyu Lai, Yu Gu, Yuxian Gu, Hangliang Ding, Kai Men, Kejuan Yang, Shudan Zhang, Xiang Deng, Aohan Zeng, Zhengxiao Du, Chenhui Zhang, Shengqi Shen, Tianjun Zhang, Yu Su, Huan Sun, Minlie Huang, Yuxiao Dong, and Jie Tang. 2023 · 2023
Cited alongside, same era.
Gaia: a benchmark for general ai assistants
Grégoire Mialon, Clémentine Fourrier, Craig Swift, Thomas Wolf, Yann LeCun, and Thomas Scialom. 2023 · 2023
Cited alongside, same era.
Bookgpt: A general framework for book recommendation empowered by large language model
Aakas Zhiyuli, Yanfang Chen, Xuan Zhang, and Xun Liang. 2023 · 2023
Later among the works it cites.
Learning to predict persona information for dialogue personalization without explicit persona description
Wangchunshu Zhou, Qifei Li, and Chenle Li. 2023b · 2023
Later among the works it cites.
Language-based user profiles for recommendation
Yijia Dai, Joyce Zhou, and Thorsten Joachims. 2024 · 2024
Closest in time.
Personal llm agents: Insights and survey about the capability, efficiency and security
Yuanchun Li, Hao Wen, Weijun Wang, Xiangyu Li, Yizhen Yuan, Guohong Liu, Jiacheng Liu, Wenxing Xu, Xiang Wang, Yi Sun, Rui Kong, Yile Wang, Hanfei Geng, Jian Luan, Xuefeng Jin, Zilong Ye, Guanjing Xiong, Fan Zhang, Xiang Li, Mengwei Xu, Zhijun Li, Peng Li, Yang Liu, Ya-Qin Zhang, and Yunxin Liu. 2024 · 2024
Closest in time.
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Pearl: Personalizing large language model writing assistants with generation-calibrated retrievers
Sheshera Mysore, Zhuoran Lu, Mengting Wan, Longqi Yang, Steve Menezes, Tina Baghaee, Emmanuel Barajas Gonzalez, Jennifer Neville, and Tara Safavi. 2023 · 2023
Cited alongside, same era.
OpenAI. 2023 · 2023
Cited alongside, same era.
Integrating summarization and retrieval for enhanced personalization via large language models
Chris Richardson, Yao Zhang, Kellen Gillespie, Sudipta Kar, Arshdeep Singh, Zeynab Raeesy, Omar Zia Khan, and Abhinav Sethy. 2023 · 2023
Cited alongside, same era.
Taskbench: Benchmarking large language models for task automation
Yongliang Shen, Kaitao Song, Xu Tan, Wenqi Zhang, Kan Ren, Siyu Yuan, Weiming Lu, Dongsheng Li, and Yueting Zhuang. 2023 · 2023
Cited alongside, same era.
User modeling in the era of large language models: Current research and future directions
Zhaoxuan Tan and Meng Jiang. 2023 · 2023
Cited alongside, same era.
Rolecraft-glm: Advancing personalized role-playing in large language models
Meiling Tao, Xuechen Liang, Tianyu Shi, Lei Yu, and Yiting Xie. 2023 · 2023
Cited alongside, same era.
Cmat: A multi-agent collaboration tuning framework for enhancing small language models
Xuechen Liang, Meiling Tao, Yinghui Xia, Tianyu Shi, Jun Wang, and JingSong Yang. 2024a
Cited in the paper.
Self-evolving agents with reflective and memory-augmented abilities
Xuechen Liang, Meiling Tao, Yinghui Xia, Tianyu Shi, Jun Wang, and JingSong Yang. 2024b
Cited in the paper.
Guanyu Lin, Tao Feng, Pengrui Han, Ge Liu, and Jiaxuan You. 2024 · 2024
Closest in time.
Surveyagent: A conversational system for personalized and efficient research survey
Xintao Wang, Jiangjie Chen, Nianqi Li, Lida Chen, Xinfeng Yuan, Wei Shi, Xuyang Ge, Rui Xu, and Yanghua Xiao. 2024 · 2024
Closest in time.
Personalized large language models
Stanisław Woźniak, Bartłomiej Koptyra, Arkadiusz Janz, Przemysław Kazienko, and Jan Kocoń. 2024 · 2024
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Travelplanner: A benchmark for real-world planning with language agents
Jian Xie, Kai Zhang, Jiangjie Chen, Tinghui Zhu, Renze Lou, Yuandong Tian, Yanghua Xiao, and Yu Su. 2024 · 2024
Closest in time.
Symbolic learning enables self-evolving agents
Wangchunshu Zhou, Yixin Ou, Shengwei Ding, Long Li, Jialong Wu, Tiannan Wang, Jiamin Chen, Shuai Wang, Xiaohua Xu, Ningyu Zhang, Huajun Chen, and Yuchen Eleanor Jiang. 2024 · 2024
Closest in time.
Hydra: Model factorization framework for black-box llm personalization
Yuchen Zhuang, Haotian Sun, Yue Yu, Qifan Wang, Chao Zhang, and Bo Dai. 2024 · 2024
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