Personalization of Large Language Models: A Survey
Dartmouth College（达特茅斯学院）；Adobe Research（Adobe研究）；Stanford University（斯坦福大学）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Pattern Data；Vanderbilt University（范德比尔特大学）；Dolby Research（Dolby研究）；University of California San Diego（加州大学圣地亚哥分校）；Cisco Research（思科研究）；University of Oregon（俄勒冈大学）
Zhehao Zhang, Ryan A. Rossi, Branislav Kveton, Yijia Shao, Diyi Yang, Hamed Zamani, Franck Dernoncourt, Joe Barrow, Tong Yu, Sungchul Kim, Ruiyi Zhang, Jiuxiang… 展开作者
Zhehao Zhang, Ryan A. Rossi, Branislav Kveton, Yijia Shao, Diyi Yang, Hamed Zamani, Franck Dernoncourt, Joe Barrow, Tong Yu, Sungchul Kim, Ruiyi Zhang, Jiuxiang Gu, Tyler Derr, Hongjie Chen, Junda Wu, Xiang Chen, Zichao Wang, Subrata Mitra, Nedim Lipka, Nesreen Ahmed, Yu Wang
备注：
Accepted at the Transactions on Machine Learning Research (TMLR) journal
Personalization of Large Language Models (LLMs) has recently become increasingly important with a wide range of applications. Despite the importance and recent progress, most existing works on personalized LLMs have focused either entirely on (a) personalized text generation or (b) leveraging LLMs for personalization-related downstream applications, such as recommendation systems. In this work, we bridge the gap between these two separate main directions for the first time by introducing a taxonomy for personalized LLM usage and summarizing the key differences and challenges. We provide a formalization of the foundations of personalized LLMs that consolidates and expands notions of personalization of LLMs, defining and discussing novel facets of personalization, usage, and desiderata of personalized LLMs. We then unify the literature across these diverse fields and usage scenarios by proposing systematic taxonomies for the granularity of personalization, personalization techniques, datasets, evaluation methods, and applications of personalized LLMs. Finally, we highlight challenges and important open problems that remain to be addressed. By unifying and surveying recent research using the proposed taxonomies, we aim to provide a clear guide to the existing literature and different facets of personalization in LLMs, empowering both researchers and practitioners.
Rethinking Personalization in Large Language Models at the Token Level
Institute for Artificial Intelligence, Peking University（人工智能研究院，北京大学）；State Key Lab of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University（通用人工智能国家重点实验室，北京大学智能科学与技术学院）；School of Mathematical Sciences, Peking University（数学科学学院，北京大学）
Reflective Personalization Optimization: A Post-hoc Rewriting Framework for Black-Box Large Language Models
School of Electronic and Electrical Engineering, Shanghai University of Engineering Science（上海工程技术大学电子与电气工程学院）；Tencent Youtu Lab（腾讯优图实验室）；Artificial Intelligence Innovation and Incubation Institute, Fudan University（复旦大学人工智能创新与孵化院）
PRIME: Large Language Model Personalization with Cognitive Dual-Memory and Personalized Thought Process
Computer Science and Engineering, University of Michigan, Ann Arbor, MI（计算机科学与工程系，密歇根大学，安娜堡分校）；Department of Political Science, Northeastern University, Boston, MA（政治学系，东北大学，波士顿分校）
该研究提出融合认知双记忆与个性化慢思考的 PRIME 框架，并构建基于 Reddit CMV 的长上下文个性化数据集，实验证明其可捕捉动态用户偏好。
ReLay: Personalized LLM-Generated Plain-Language Summaries for Better Understanding, but at What Cost?
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Virginia（弗吉尼亚大学）；University of Colorado Anschutz（科罗拉多大学安舒茨分校）；Johns Hopkins University（约翰霍普金斯大学）；University of Chicago Pritzker School of Medicine（芝加哥大学普ritzker法学院）；National Library of Medicine, National Institutes of Health（国家医学图书馆，国家卫生研究院）；University of Washington（华盛顿大学）
Personalized Graph-Based Retrieval for Large Language Models
University of California Santa Cruz（加州大学圣克ruz分校）；Meta AI；Adobe Research（Adobe研究）；University of Oregon（俄勒冈大学）；University of Southern California（南加州大学）；Cisco AI Research（思科人工智能研究）
AI PERSONA: Towards Life-long Personalization of LLMs
OPPO AI Center（OPPO AI中心）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Beihang University（北京航空航天大学）；Tsinghua University（清华大学）；Guangdong University of Technology（广东工业大学）
Evaluating LLM Personalization via Semantic Constraint Verification
University of New South Wales（新南威尔士大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；The Technology Innovation Institute（技术创新研究所）；The Hong Kong University of Science and Technology（香港科技大学）
Syntax-Guided Diffusion Language Models with User-Integrated Personalization
Department of Statistics and Data Science, Fudan University（复旦大学统计与数据科学系）；Department of Statistics and Applied Probability, University of California, Santa Barbara（加州大学圣巴巴拉分校统计与应用概率系）
Reading Between the Prompts: How Stereotypes Shape LLM's Implicit Personalization
Institute for Logic, Language and Computation, University of Amsterdam（逻辑、语言与计算研究所，阿姆斯特丹大学）；Center for Language and Cognition, University of Groningen（语言与认知中心，格罗宁根大学）
When Personalization Misleads: Understanding and Mitigating Hallucinations in Personalized LLMs
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学首都人工智能学院）；AI Lab at Lenovo Research（联想研究院人工智能实验室）；School of Artificial Intelligence and Data Science, University of International Business and Economics（国际商务与经济大学人工智能与数据科学学院）
BAPO: Base-Anchored Preference Optimization for Overcoming Forgetting in Large Language Models Personalization
Graduate School of AI, KAIST（韩国科学技术院人工智能研究生院）；Samsung Advanced Institute of Technology（三星先进技术研究院）；Department of Electrical and Computer Engineering, UBC（不列颠哥伦比亚大学电气与计算机工程系）
Text as a Universal Interface for Transferable Personalization
Software College, Northeastern University（东北大学软件学院）；Ant Group（蚂蚁集团）；Ant International（蚂蚁国际）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学商学院）
PRISP: Privacy-Safe Few-Shot Personalization via Lightweight Adaptation
Department of Electrical and Computer Engineering, Seoul National University（电子与计算机工程系，首尔国立大学）；ASRI/INMC/IPAI/AIIS, Seoul National University（ASRI/INMC/IPAI/AIIS，首尔国立大学）
Less Is Personal: Learning Minimal Sufficient User Profiles for Personalized Language Models
State Key Laboratory of AI Safety, Institute of Computing Technology, CAS（中国科学院计算技术研究所人工智能安全国家重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）
