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Personalized large language models (LLMs) aim to tailor interactions, content, and recommendations to 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. 2022 · 1965
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ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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Improvements to bm25 and language models examined
Andrew Trotman, Antti Puurula, and Blake Burgess. 2014 · 2014
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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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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Evaluating conversational recommender systems via user simulation
Shuo Zhang and Krisztian Balog. 2020 · 2020
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Learning technology models that support personalization within blended learning environments in higher education
Hamdan A Alamri, Sunnie Watson, and William Watson. 2021 · 2021
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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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Precision medicine, ai, and the future of personalized health care
Kevin B Johnson, Wei-Qi Wei, Dilhan Weeraratne, Mark E Frisse, Karl Misulis, Kyu Rhee, Juan Zhao, and Jane L Snowdon. 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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Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le. 2021 · 2021
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Fusing finetuned models for better pretraining
Leshem Choshen, Elad Venezian, Noam Slonim, and Yoav Katz. 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
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Editing models with task arithmetic
Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Ludwig Schmidt, Hannaneh Hajishirzi, and Ali Farhadi. 2022 · 2022
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Dataless knowledge fusion by merging weights of language models
Xisen Jin, Xiang Ren, Daniel Preotiuc-Pietro, and Pengxiang Cheng. 2022 · 2022
Cited alongside, same era.
Branch-train-merge: Embarrassingly parallel training of expert language models
Margaret Li, Suchin Gururangan, Tim Dettmers, Mike Lewis, Tim Althoff, Noah A Smith, and Luke Zettlemoyer. 2022 · 2022
Cited alongside, same era.
Rethinking the role of demonstrations: What makes in-context learning work?
Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2022 · 2022
Cited alongside, same era.
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
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 · 2022
Cited alongside, same era.
Knowledge-augmented large language models for personalized contextual query suggestion
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
Later among the works it cites.
Merging by matching models in task subspaces
Derek Tam, Mohit Bansal, and Colin Raffel. 2023 · 2023
Later among the works it cites.
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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alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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.
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.
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.
Chat-rec: Towards interactive and explainable llms-augmented recommender system
Yunfan Gao, Tao Sheng, Youlin Xiang, Yun Xiong, Haofen Wang, and Jiawei Zhang. 2023 · 2023
Cited alongside, same era.
Mixture of cluster-conditional lora experts for vision-language instruction tuning
Yunhao Gou, Zhili Liu, Kai Chen, Lanqing Hong, Hang Xu, Aoxue Li, Dit-Yan Yeung, James T Kwok, and Yu Zhang. 2023 · 2023
Cited alongside, same era.
Lorahub: Efficient cross-task generalization via dynamic lora composition
Chengsong Huang, Qian Liu, Bill Yuchen Lin, Tianyu Pang, Chao Du, and Min Lin. 2023 · 2023
Cited alongside, same era.
Danqing Wang, Kevin Yang, Hanlin Zhu, Xiaomeng Yang, Andrew Cohen, Lei Li, and Yuandong Tian. 2023 · 2023
Later among the works it cites.
Personalized news recommendation: Methods and challenges
Chuhan Wu, Fangzhao Wu, Yongfeng Huang, and Xing Xie. 2023 · 2023
Later among the works it cites.
Composing parameter-efficient modules with arithmetic operation
Jinghan Zhang, Junteng Liu, Junxian He, et al. 2023 · 2023
Later among the works it cites.
Avoiding copyright infringement via machine unlearning
Guangyao Dou, Zheyuan Liu, Qing Lyu, Kaize Ding, and Eric Wong. 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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The benefits, risks and bounds of personalizing the alignment of large language models to individuals
Hannah Rose Kirk, Bertie Vidgen, Paul Röttger, and Scott A Hale. 2024 · 2024
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Aligning to thousands of preferences via system message generalization
Seongyun Lee, Sue Hyun Park, Seungone Kim, and Minjoon Seo. 2024 · 2024
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Personalized language modeling from personalized human feedback
Xinyu Li, Zachary C Lipton, and Liu Leqi. 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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Learning to route among specialized experts for zero-shot generalization
Mohammed Muqeeth, Haokun Liu, Yufan Liu, and Colin Raffel. 2024 · 2024
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Principled rlhf from heterogeneous feedback via personalization and preference aggregation
Chanwoo Park, Mingyang Liu, Kaiqing Zhang, and Asuman Ozdaglar. 2024 · 2024
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Optimization methods for personalizing large language models through retrieval augmentation
Alireza Salemi, Surya Kallumadi, and Hamed Zamani. 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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Democratizing large language models via personalized parameter-efficient fine-tuning
Zhaoxuan Tan, Qingkai Zeng, Yijun Tian, Zheyuan Liu, Bing Yin, and Meng Jiang. 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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Loraretriever: Input-aware lora retrieval and composition for mixed tasks in the wild
Ziyu Zhao, Leilei Gan, Guoyin Wang, Wangchunshu Zhou, Hongxia Yang, Kun Kuang, and Fei Wu. 2024 · 2024
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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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