LLM Personalization

LLM: Large Language Model

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10 papers in the last four weeks, up 25% on the four weeks before. 0.1% of all new papers.

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CoPA: Benchmarking Personalized Question Answering with Data-Informed Cognitive Factors
Apr 16, 2026Hang Su, Zequn Liu, Chen Hu +3LLM Personalization Question Answering
East China Normal University, China · Zhongguancun Academy, China
DeepTutor: Towards Agentic Personalized Tutoring
Apr 10, 2026Bingxi Zhao, Jiahao Zhang, Xubin Ren +4LLM Personalization Intelligent Tutoring Systems
The University of Hong Kong
Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data
Mar 10, 2026Hyunji Nam, Haoran Li, Natasha JaquesLLM Personalization Mutual Information Maximization
Stanford University · Character AI · University of Washington
Towards Natural Personalization: Evaluating Long-Horizon Preference Following in Personalized User-LLM Interactions
Mar 4, 2026Qianyun Guo, Yibo Li, Yue Liu +1LLM Evaluation LLM Personalization
National University of Singapore, Singapore.
Synthetic Interaction Data for Scalable Personalization in Large Language Models
Feb 12, 2026Yuchen Ma, Yue Huang, Wenjie Wang +3RL for Language Models LLM Personalization
Munich Center for Machine Learning & LMU Munich · University of Notre Dame · Sichuan University
SPRInG: Continual LLM Personalization via Selective Parametric Adaptation and Retrieval-Interpolated Generation
Jan 15, 2026Seoyeon Kim, Jaehyung KimContinual Learning for LLMs LLM Personalization
Yonsei University
Do Implicit Personalization and Explicit Styles Conflict? PsPLUG: A Lightweight Plug-in for Balancing Personalization and Style in Customized LLMs
Jan 10, 2026Yutong Song, Jiang Wu, Shaofan Yuan +5LLM Personalization Controllable Text Generation
University of California, Irvine · Independent Researcher · TikTok
GMTRouter: Personalized LLM Router over Multi-turn User Interactions
Oct 29, 2025Yihang Sun, Encheng Xie, Tao Feng +1LLM Personalization LLM Routing
University of Illinois Urbana-Champaign
POPI: Personalizing LLMs via Optimized Natural Language Preference Inference
Oct 17, 2025Yizhuo Chen, Xin Liu, Ruijie Wang +7LLM Personalization Personalized Language Models
University of Illinois Urbana-Champaign · Amazon · University of Notre Dame
TagPR: Tag-Guided Process Supervision for Personalization Reasoning in Large Language Models
Sep 27, 2025Song Jin, Juntian Zhang, Ruyu Lyu +7LLM Alignment LLM Personalization
Gaoling School of Artificial Intelligence, Renmin University of China · Meituan · Wuhan University
FaST: Feature-aware Sampling and Tuning for Personalized Preference Alignment with Limited Data
Aug 6, 2025Thibaut Thonet, Germán Kruszewski, Jos Rozen +2LLM Personalization Personalized Language Models
1NAVER Labs Europe · 2Independent Researcher
Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy
May 28, 2025Saleh Afzoon, Ali Shahsavandi, Phuong Thao Huynh +4LLM Personalization Human-AI Interaction
School of Computing, Macquarie University, Sydney, Australia · Department of Computer Engineering and Information Technology, Shiraz University of Technology, Shiraz, Iran
Learning Personalized Prompts for Healthcare Guidance
Dec 20, 2024Ruize Shi, Hong Huang, Wei Zhou +3Prompt Learning LLM Personalization
Huazhong University of Science and Technology, Wuhan, China · 1Huazhong University of Science and Technology, Wuhan, China · 2Tongji Medical College +1
PACIFIC: Can LLMs Discern the Psychometric Traits Influencing Your Preferences? Personality-Driven Preference Alignment in LLMs
Date pendingTianyu Zhao, Siqi Li, Yasser Shoukry +1LLM Personalization Preference Alignment
Department of Electrical Engineering and Computer Science University of California, Irvine Irvine, CA 92697, USA

Apr 16, 2026, cs.CLJ/K move · Enter open · S save

CoPA: Benchmarking Personalized Question Answering with Data-Informed Cognitive Factors

Hang Su, Zequn Liu, Chen Hu, Xuesong Lu+2

East China Normal University, China · Zhongguancun Academy, China

LLM Personalization Question Answering User Preference Modeling

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While LLMs have demonstrated remarkable potential in Question Answering (QA), evaluating personalization remains a critical bottleneck. Existing paradigms predominantly rely on lexical-level similarity or manual heuristics, often lacking sufficient data-driven validation. We address this by mining Community-Individual Preference Divergence (CIPD), where individual choices override consensus, to distill six key personalization factors as evaluative dimensions. Accordingly, we introduce CoPA, a benchmark with 1,985 user profiles for fine-grained, factor-level assessment. By quantifying the alignment between model outputs and user-specific cognitive preferences inferred from interaction patterns, CoPA provides a more comprehensive and discriminative standard for evaluating personalized QA than generic metrics. The code is available at https://github.com/bjzgcai/CoPA.

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