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Personalization in large language models (LLMs) is increasingly important, aiming to align the LLMs' interactions, content, and recommendations with individual user preferences.
Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning
Haokun Liu, Derek Tam, Mohammed Muqeeth, Jay Mohta, Tenghao Huang, Mohit Bansal, and Colin A Raffel. 2022a · 1965
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ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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Personalized recommendation combining user interest and social circle
Xueming Qian, He Feng, Guoshuai Zhao, and Tao Mei. 2013 · 2013
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Improvements to bm25 and language models examined
Andrew Trotman, Antti Puurula, and Blake Burgess. 2014 · 2014
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Parameter-efficient transfer learning for nlp
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019 · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019 · 2019
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Hierarchical user profiling for e-commerce recommender systems
Yulong Gu, Zhuoye Ding, Shuaiqiang Wang, and Dawei Yin. 2020 · 2020
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Array programming with numpy
Charles R Harris, K Jarrod Millman, Stéfan J Van Der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J Smith, et al. 2020 · 2020
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Personalized chatbot trustworthiness ratings
Biplav Srivastava, Francesca Rossi, Sheema Usmani, and Mariana Bernagozzi. 2020 · 2020
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al. 2020 · 2020
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Personalization in practice: Methods and applications
Dmitri Goldenberg, Kostia Kofman, Javier Albert, Sarai Mizrachi, Adam Horowitz, and Irene Teinemaa. 2021 · 2021
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Towards a unified view of parameter-efficient transfer learning
Junxian He, Chunting Zhou, Xuezhe Ma, Taylor Berg-Kirkpatrick, and Graham Neubig. 2021 · 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 · 2021
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
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One chatbot per person: Creating personalized chatbots based on implicit user profiles
Zhengyi Ma, Zhicheng Dou, Yutao Zhu, Hanxun Zhong, and Ji-Rong Wen. 2021 · 2021
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Adapterfusion: Non-destructive task composition for transfer learning
Jonas Pfeiffer, Aishwarya Kamath, Andreas Rücklé, Kyunghyun Cho, and Iryna Gurevych. 2021 · 2021
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Llm.int8(): 8-bit matrix multiplication for transformers at scale
Tim Dettmers, Mike Lewis, Younes Belkada, and Luke Zettlemoyer. 2022 · 2022
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Debertav3: Improving deberta using electra-style pre-training with gradient-disentangled embedding sharing
Pengcheng He, Jianfeng Gao, and Weizhu Chen. 2022 · 2022
Cited alongside, same era.
Multitask prompted training enables zero-shot task generalization
Victor Sanh, Albert Webson, Colin Raffel, Stephen H Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, et al. 2022 · 2022
Cited alongside, same era.
Super-naturalinstructions: Generalization via declarative instructions on 1600+ nlp tasks
Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Atharva Naik, Arjun Ashok, Arut Selvan Dhanasekaran, Anjana Arunkumar, David Stap, et al. 2022 · 2022
Cited alongside, same era.
Emergent abilities of large language models
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, et al. 2022 · 2022
Cited alongside, same era.
Knowledge-augmented large language models for personalized contextual query suggestion
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
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Revolutionizing education: harnessing the power of artificial intelligence for personalized learning
Muh Putra Pratama, Rigel Sampelolo, and Hans Lura. 2023 · 2023
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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
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Lamp: When large language models meet personalization
Alireza Salemi, Sheshera Mysore, Michael Bendersky, and Hamed Zamani. 2023 · 2023
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Jinheon Baek, Nirupama Chandrasekaran, Silviu Cucerzan, Sujay Kumar Jauhar, et al. 2023 · 2023
Cited alongside, same era.
When large language models meet personalization: Perspectives of challenges and opportunities
Jin Chen, Zheng Liu, Xu Huang, Chenwang Wu, Qi Liu, Gangwei Jiang, Yuanhao Pu, Yuxuan Lei, Xiaolong Chen, Xingmei Wang, et al. 2023 · 2023
Cited alongside, same era.
A survey on large language models for personalized and explainable recommendations
Junyi Chen. 2023 · 2023
Cited alongside, same era.
Everyone deserves a reward: Learning customized human preferences
Pengyu Cheng, Jiawen Xie, Ke Bai, Yong Dai, and Nan Du. 2023 · 2023
Cited alongside, same era.
Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E Gonzalez, et al. 2023 · 2023
Cited alongside, same era.
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
Cited alongside, same era.
Uncovering chatgpt’s capabilities in recommender systems
Sunhao Dai, Ninglu Shao, Haiyuan Zhao, Weijie Yu, Zihua Si, Chen Xu, Zhongxiang Sun, Xiao Zhang, and Jun Xu. 2023 · 2023
Cited alongside, same era.
Approaches to laparoscopic training in veterinary medicine: A review of personalized simulators
Cosmina Andreea Dejescu, Lucia V Bel, Iulia Melega, Stefana Maria Cristina Muresan, and Liviu Ioan Oana. 2023 · 2023
Cited alongside, same era.
Large language models can be easily distracted by irrelevant context
Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed H Chi, Nathanael Schärli, and Denny Zhou. 2023 · 2023
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User modeling in the era of large language models: Current research and future directions
Zhaoxuan Tan and Meng Jiang. 2023 · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023 · 2023
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Learning personalized story evaluation
Danqing Wang, Kevin Yang, Hanlin Zhu, Xiaomeng Yang, Andrew Cohen, Lei Li, and Yuandong Tian. 2023 · 2023
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Personalized news recommendation: Methods and challenges
Chuhan Wu, Fangzhao Wu, Yongfeng Huang, and Xing Xie. 2023 · 2023
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Lingling Xu, Haoran Xie, Si-Zhao Joe Qin, Xiaohui Tao, and Fu Lee Wang. 2023 · 2023
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Bookgpt: A general framework for book recommendation empowered by large language model
Aakas Zhiyuli, Yanfang Chen, Xuan Zhang, and Xun Liang. 2023 · 2023
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Avoiding copyright infringement via machine unlearning
Guangyao Dou, Zheyuan Liu, Qing Lyu, Kaize Ding, and Eric Wong. 2024 · 2024
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Rag vs fine-tuning: Pipelines, tradeoffs, and a case study on agriculture
Aman Gupta, Anup Shirgaonkar, Angels de Luis Balaguer, Bruno Silva, Daniel Holstein, Dawei Li, Jennifer Marsman, Leonardo O Nunes, Mahsa Rouzbahman, Morris Sharp, et al. 2024 · 2024
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Expedited training of visual conditioned language generation via redundancy reduction
Yiren Jian, Tingkai Liu, Yunzhe Tao, Chunhui Zhang, Soroush Vosoughi, and Hongxia Yang. 2024 · 2024
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Towards safer large language models through machine unlearning
Zheyuan Liu, Guangyao Dou, Zhaoxuan Tan, Yijun Tian, and Meng Jiang. 2024 · 2024
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Chenkai Sun, Ke Yang, Revanth Gangi Reddy, Yi R Fung, Hou Pong Chan, ChengXiang Zhai, and Heng Ji. 2024 · 2024
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Working memory identifies reasoning limits in language models
Chunhui Zhang, Yiren Jian, Zhongyu Ouyang, and Soroush Vosoughi. 2024 · 2024
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