LLM Personalization

LLM: Large Language Model

LLM personalization studies how models can infer what a user wants and use that knowledge to tailor answers, recommendations, and agent actions. Common approaches include user profiles and memory retrieval, personalized prompts, preference optimization, and lightweight adapters or inference-time steering. Benchmarks such as LaMP and PersonaMem test preference following across tasks and interactions, but long histories, implicit or changing preferences, and the gap between remembering a preference and acting on it remain difficult. A central challenge is making personalization reliable and useful without over-inference, privacy loss, unsafe influence, or poor performance for new users.

Latest papers

130 papers10 papers in the last four weeks, down 37% on the four weeks before. 0.1% of all new papers.

Neither Over- nor Under-Constrained: Evaluating LLM Personalization under a Constraint Qualification Audit
Oct 19, 2026Xuran Li, Guanqin Zhang, Imran Razzak +4LLM Evaluation LLM Personalization
University of New South Wales · Mohamed bin Zayed University of Artificial Intelligence · The Technology Innovation Institute +1
Bridging Exploration and Persona: Information-Directed Dueling for Test-Time Agent Personalization
Oct 17, 2026Jie Mao, Pingchen Lu, Xiangyi Wang +4LLM Personalization User Preference Modeling
The Chinese University of Hong Kong, Shenzhen · University of Copenhagen · City University of Hong Kong +1
Online LLM Personalization from Pairwise Preferences Across Users
Oct 12, 2026Romain Puech, Georgy Salakhutdinov, Dimitris BertsimasEducational QA LLM Personalization
Massachusetts Institute of Technology
PersonaFactor: Preference Factors with Dynamic Selection for LLM Personalization
Oct 12, 2026Hang Su, Zequn Liu, Jianghan Chao +3LLM Personalization User Preference Modeling
East China Normal University · Beijing Zhongguancun Academy · Renmin University of China +1
Do Personalized LLMs Need the Whole Conversation? Matching the Full History with Only 5% of Its Text via Relevance-Filtered Context
Oct 10, 2026Erika Hayashi, Andrew ShinLLM Context Management Conversational Memory
Keio University
MoF: Preference-Aware Mixture Modeling for Black-Box LLM Personalization
Oct 6, 2026Hun ParkLLM Personalization Personalized Language Models
Department of Artificial Intelligence Korea University Seoul, Republic of Korea
Personal-Agent Mediated Recommendation with Cross-Platform User History
Oct 6, 2026Yu Xia, Jiangfan Zhang, Jun Xiao +2LLM Personalization Recommender Systems
University of California San Diego · Meta · Meta AI
Does Every User Need a Private LoRA? Decoupling Personalization from Per-User Adaptation
Oct 1, 2026Songyuan Sui, Srikanth Malla, Chiho Choi +1LLM Personalization Personalized Language Models
Samsung Semiconductor, US · Rice University
AdaptArena: Evaluating Test-Time Personalization of Web Agents
Sep 29, 2026Dongchan Shin, Xing Han Lù, Jiaqi Deng +7Web Agents LLM Personalization
Mila - Quebec AI Institute · McGill University · ServiceNow Research +2
Rubric-Aware On-Policy Self-Distillation for LLM Personalization
Sep 28, 2026Yilun Qiu, Xiaoyan Zhao, Chengbing Wang +6LLM Personalization On-Policy Self-Distillation
Xiaohongshu Inc. · National University of Singapore · University of Science and Technology of China +1
Unlocking Latent Personalization in LLMs
Sep 27, 2026Wei Chen, Guanghui Zhu, Zhongliang Cai +1LLM Personalization Personalized Language Models
State Key Laboratory for Novel Software Technology, Nanjing University
AgentRecommender: LLM Agents Enable Customizable Recommender Systems on the User Side
Sep 25, 2026Ryoma SatoRecommender Systems Large Language Model-Based Recommendation
National Institute of Informatics
Annie, Are You Okay? How Style- and Context-Based Personalization Shape AI-Assisted Decision-Making
Sep 21, 2026Hasibur Rahman, Benjamin R. Cowan, Smit DesaiAI-Assisted Decision Making Human-AI Decision Making
Northeastern University, Boston, Massachusetts, USA · University College Dublin, Dublin, Ireland
One Prompt Does Not Fit All: Self-Meta-Evolve for Personalized Information Extraction
Sep 18, 2026Hongliang Li, Lu Wang, Yong Xu +6Automatic Prompt Optimization LLM Information Extraction
Microsoft · Beijing Jiaotong University
Memory Has Geometry: Non-Uniform Geometric Memory for Long-Horizon Personalized AI
Sep 16, 2026Jiahong Liu, Wenhao Yu, Zexuan Qiu +2Neural Representation Geometry LLM Personalization
The Chinese University of Hong Kong, Hong Kong SAR, China · The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China
Mind2Dialogue: Training Human-Aware Language Models by Simulating User Mental States
Sep 14, 2026Zixuan Wang, Yufan Zhou, Jinzhou Tang +16LLM Personalization Theory of Mind
UC San Diego · KU Leuven · Johns Hopkins University +2
Generate to Explore, Select to Exploit: Aligning LLM-based Headline Generation with Personalized Recommendation
Sep 14, 2026Yi Chen, Rufeng Cheng, Qiang Xie +1RL for Language Models LLM Personalization
Baidu Inc., Beijing, China
Creating an Atomic User Model for Personality-Aware Large Language Model Interaction
Sep 10, 2026B. Sankar, Deepthika S, Pawni Yadav +1LLM Personalization Personality Modeling in Language Models
Department of Mechanical Engineering, Indian Institute of Science (IISc), Bengaluru, 560012, Karnataka, India · Department of Design and Manufacturing, Indian Institute of Science (IISc), Bengaluru, 560012, Karnataka, India
HyperTrace: Hypothesis-Based Preference Tracing for Online LLM Personalization
Sep 9, 2026Jianzhi Shen, Keyu Mao, Minghao Shao +7LLM Personalization Personalized Language Models
Johns Hopkins University · Institute of Science Tokyo · NYU Tandon +1
Less Is Personal: Learning Minimal Sufficient User Profiles for Personalized Language Models
Sep 8, 2026Minghang Liu, Qiang Qiu, Yuanzhuo Wang +2LLM Personalization Memory-Augmented Language Models
State Key Laboratory of AI Safety, Institute of Computing Technology, CAS · University of Chinese Academy of Sciences, Beijing, China
Aegix Pulse: A Traceable Three-Stage Architecture for Personalized Content Generation and Context-Preserving Revision
Sep 7, 2026Hongnan Zhao, Shiyu Chen, Zhihao ChenLLM Personalization Evidence-Grounded Generation
1Aegix Insight
Aplaud: Adaptive Personalized Low-Rank Decomposition for User-Specific LLM
Sep 4, 2026Xinyu Li, Ruoming Jin, Jianfeng Zhu +2LLM Personalization Low-Rank Adaptation
Department of Computer Science, Kent State University · iLambda Inc.
PLUME: Parameter-Efficient Personalization of Large Language Models via Low-Rank User Modulation in Shared Subspaces
Sep 4, 2026Xinyu Li, Hao Zhou, Jianfeng Zhu +4LLM Personalization Low-Rank Adaptation
Department of Computer Science Kent State University Kent, OH 44242, USA
A Prompt-Engineering Approach to Develop Scalable, Flexible, and Real-Time Hybrid Micro-Level Personalization in a General Purpose AI Teaching Assistant
Sep 3, 2026Saptarshi Basu, Sandeep Kakar, Ashok GoelLLM Personalization Intelligent Tutoring Systems
Georgia Institute of Technology, Atlanta GA 30332, USA
VIBE-Bench: Evaluating Personalized Large Language Models When Profiles Don't Mean Preferences
Sep 1, 2026Yiwen Jiang, Yang Deng, Stephanie Fong +9LLM Evaluation LLM Personalization
1Monash University · 2Singapore Management University · University of Liverpool +1
Hypotheses-Guided Self Distillation for Continual Personalization
Aug 31, 2026EunJeong Hwang, Kushan Mitra, Dan Zhang +2Continual Learning for LLMs LLM Personalization
University of British Columbia · Work done as an intern at Megagon Labs. · Megagon Labs, USA
PersonaEdit: Representative Sample Selection for Personalized Model Editing
Aug 28, 2026You-Mei Huang, Chung-Chi Chen, An-Zi YenLLM Personalization Model Editing
Department of Computer Science, National Yang Ming Chiao Tung University, Taiwan · Human-Agent Ally Lab (HAA Lab), National Institute of Informatics (NII), Japan
Personalized Auto-Research: Towards a True AI Co-Scientist
Aug 14, 2026Bo Ni, Franck Dernoncourt, Hongjie Chen +5AI-Assisted Scientific Research Autonomous Research Agents
Vanderbilt University Nashville, Tennessee, USA · Adobe Research San Jose, California, USA · Dolby Laboratories San Francisco, California, USA +3
Locating and Controlling Implicit Personalization in Large Language Models
Aug 12, 2026Yueru Yan, Siqi Wu, Thai LeLLM Personalization LLM Interpretability
Indiana University Bloomington, USA
Weightless Fine-Tuning: Personalizing LLMs via Logit-Space Transport
Aug 11, 2026Bohan Zhang, Anqi Ni, Yixin Wang +1Supervised Fine-Tuning LLM Personalization
University of Michigan · University of Chicago

Oct 19, 2026J/K move · Enter open · S save

Neither Over- nor Under-Constrained: Evaluating LLM Personalization under a Constraint Qualification Audit

Xuran Li, Guanqin Zhang, Imran Razzak, Hakim Hacid+3

University of New South Wales · Mohamed bin Zayed University of Artificial Intelligence · The Technology Innovation Institute+1

LLM Evaluation LLM Personalization Language Model Auditing

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Accurate evaluation of LLM personalization is hindered by current evaluation paradigms that over- or under-constrain the response space, yielding frequent false rejections and false acceptances. To reduce these false rates, we introduce ConQual-Audit, an audit-based framework that evaluates LLM personalization through a three-stage process: (1) extracting semantic constraints from user profiles and task-grounded reference; (2) qualifying constraints by pruning unnecessary constraints and recovering missing constraints; and (3) verifying candidate responses against the audited constraints via natural language inference. Across PrefEval and LaMP, ConQual-Audit outperforms nine baselines, attaining top AUCs (0.88 and 0.90) and the lowest false rate (0.21) on the leanest constraint sets at an amortizable audit cost. Crucially, this constraint audit is portable: integrating it into ExPerT and PREF helps revise smaller, higher-quality constraint sets to reduce their evaluation false rates.