Personalized Language Models

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Personalized language models aim to make responses relevant to a particular user rather than relying only on general patterns learned during pretraining. Common approaches retrieve relevant records from user histories, maintain structured profiles or persistent memories, or adapt models with parameter-efficient methods such as LoRA; inference-time steering, preference ranking, and feedback-based learning offer alternatives. Benchmarks such as LaMP, LongLaMP, and PersonaMem test preference matching, memory use, and generation across users and longer interactions, while human studies examine whether personalization feels useful and appropriate. A central challenge is using personal information when it is relevant without over-applying it, compromising privacy, reinforcing bias, or weakening general response quality.

Latest papers

89 papers21 papers in the last four weeks, up 40% on the four weeks before. 0.1% of all new papers.

Personality Latents: Conditioning Frozen Language Models on Individuals from Their Own Writing
Oct 27, 2026Gunhee Cho, Yun-Gyung CheongPersonality Modeling in Language Models Personalized Language Models
Dept. of AI, Sungkyunkwan University
Profile-DP: Differential Privacy for User-Controlled Profile Release in Personalized LLM Inference
Oct 19, 2026Mingchen Li, Zifan Peng, Kewei Sha +5Privacy-Preserving Language Models Differential Privacy
University of North Texas · School of Computing, Newcastle University, UK · Augusta University
Chance-Constrained Personalized LLM Routing with Online Preference Learning
Oct 15, 2026Ameya Anjarlekar, Rasoul Etesami, R SrikantLLM Routing Cost-Aware Inference
Explaining the Roles of Experts in Mixture-of-Experts LLMs for Personalization
Oct 15, 2026Sekh Mainul Islam, Pepa Atanasova, Isabelle AugensteinMixture-of-Experts Expert Specialization Personalized Language Models
University of Copenhagen
Learning What to Keep: Transfer-Aware Personal Skill Learning from User Feedback
Oct 14, 2026Li Wang, Chen Zhang, Yirong Yang +4Personalized Language Models Transfer Learning
Beihang University · Chengdu University of Information Technology · Nanyang Technological University +2
PersEvalBench: Can LLM Evaluators Judge Contextually Appropriate Use of Persona Information?
Oct 13, 2026Xinyi Liu, Hooshang Nayyeri, Emine Yilmaz +3LLM Evaluation LLM-as-a-Judge
Amazon · University of Illinois Urbana-Champaign · University College London
Does Personalization Break Trust? Open-Set Rejection Calibration Under Few-Shot On-Device Intent Adaptation
Oct 12, 2026Vaibhav Prajapati, Pushp GargOpen-Set Recognition OOD Detection
University of Technology Nuremberg, Nuremberg, Germany · Indian Institute of Information Sri City, Chittoor, India
Fast and Accurate: Improving Personalized LLM Performance by Semantic Concentration Inspired Workload Split between RAG and Fine-Tuning
Oct 11, 2026Xingke Yang, Sicong Li, Lingfeng Yao +4Personalized Language Models LLM Inference Efficiency
University of Houston, USA · Independent Researcher, USA
FleetPilot: Fine-Grained Steering Empowers Real-World LLM Personalization
Oct 10, 2026Kounianhua Du, Jianxing Liu, Kangning Zhang +7Personalized Language Models Language Model Steering
Shanghai Jiao Tong University · Xiaohongshu Inc., Shanghai, China
Personalization Without Stereotyping: Do Speech LLMs Know When to Turn a Deaf Ear?
Oct 9, 2026Kshitij Ambilduke, Caroline Brun, Thibaut Thonet +2Social Bias in Language Models Speech Language Models
Université Paris-Saclay, France · NAVER LABS Europe, France
RegulaTutor: A Theory-Informed Framework for Long-Term Personalized Learning
Oct 9, 2026Yonggang Li, Boqian Han, Rui Sheng +2Self-Regulated Learning Personalized Learning
The Hong Kong University of Science and Technology · The Chinese University of Hong Kong
DPPM: Dual-Path Parametric Memory for Personalized Language Models
Oct 8, 2026Yuhao Chen, Shuochen Liu, Jiayao Shi +7Memory-Augmented Language Models Persistent Memory for Language Models
University of Science and Technology of China · iFLYTEK Research Group
MIRROR: From Imitation to Internalization in LLM Personalization
Oct 7, 2026Huayi Lai, Jicheng Yang, Min Yi +1Personalized Language Models Personalized Language Model Alignment
Baidu Inc.
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
Collaborative Personalized Preference Alignment for LLMs under Data Deficiency
Oct 5, 2026Liyan Yang, Yige Yuan, Zhiqin YangPersonalized Language Models Personalized Language Model Alignment
City University of Hong Kong · University of Washington · The Hong Kong University of Science and Technology
PsyEvo: A Personalized Counseling Agent That Self-Evolves at Test Time
Oct 2, 2026Yuting Yan, Shihao Xu, Junhao Yu +6LLM Agent Self-Improvement Mental Health Counseling
Lyncia Lab · Tianqiao and Chrissy Chen Institute
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
The Missing Coefficients: Bayesian Pairwise Merging for Model Personalization
Sep 30, 2026Yaling Shen, Tongtong Wu, Siyuan Yan +1Pairwise Preference Learning Personalized Language Models
Monash University
AMU:Admission and Memory Update for Personalized Conversations---Structured Memory with SLM Guided Control
Sep 29, 2026Tao Hwang, Yishi DiaoConversational Memory Persistent Memory for Language Models
Independent Researcher · Nanchang University
Harness Evolution as Learning: Approximation, Generalization, and Optimization Limits of Self-Improving Personal Agents
Sep 29, 2026Zeyu Gan, Zixuan Gong, Yong LiuAgent Harness Optimization LLM Agent Self-Improvement
Gaoling School of Artificial Intelligence Renmin University of China Beijing, China
Rethinking Personalized Generation: Test-Time Alignment via Factorized Ranking Models
Sep 28, 2026Qiyao Ma, Junshan Zhang, Zhe ZhaoLearning to Rank LLM Reranking
University of California, Davis
Over-Personalization Is a Decision Failure: Generation-Induced Apply Bias in LLMs
Sep 28, 2026Haeun Jang, Yonghyun Jun, Hwanhee LeePersonalized Language Models LLM Decision-Making
Department of Artificial Intelligence, Chung-Ang University
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
ProTTT: Learning to Learn Semantic User Memory with Test-Time Training
Sep 26, 2026Sejun Park, Hyoungjo Bhang, Hyein Jeong +1Personalized Language Models Persistent Memory for Language Models
Graduate School of Data Science, Seoul National University
FAVoR: Measuring and Mitigating Author-Style Homogenization in Federated Personalized Generation
Sep 25, 2026Lu Han, Jingyao Zhang, Katy Ilonka Gero +1Personalized Language Models Personalized Text Generation
School of Computer Science, The University of Sydney Sydney, NSW 2006, Australia
From Static Personal Values to Contextualized Personalization: Bayesian Personalized Value Alignment for LLMs
Sep 24, 2026Hanze Guo, Aixuan Song, Jing Yao +4LLM Alignment Personalized Language Models
Gaoling School of Artificial Intelligence, Renmin University of China · Microsoft Research Asia · Beijing Key Laboratory of Research on Large Models and Intelligent Governance +1
COPE: Continual Personalization of LLMs under Sparse User Feedback via User Embeddings and Self-Evaluation
Sep 22, 2026Ruike Cao, Fugen Yao, Liang Dong +3Continual Learning for LLMs Personalized Language Models
University of Science and Technology of China · Qwen Business Unit of Alibaba
LoRA-generating hypernetworks for efficient on-device LLM generative personalization
Sep 21, 2026Sean Augenstein, Li Ding, Jihwan Lee +2On-Device Language Model Inference Low-Rank Adaptation
Google · Formerly at Google, work done while at Google
From Pattern Recognizers to Personalized Companions: A Survey of Large Language Models in Mental Health
Sep 21, 2026He Hu, Yucheng Zhou, Qianning Wang +8Personalized Language Models Medical LLMs
Pretrained Persona Mixture Models and Tandem Models for Human Simulation
Sep 18, 2026Minwoo Kang, Téa Wright, Seun Eisape +6Large Language Model-Based Role-Play Simulation Large Language Model-Based Social Simulation
University of California, Berkeley · Google

Oct 27, 2026J/K move · Enter open · S save

Personality Latents: Conditioning Frozen Language Models on Individuals from Their Own Writing

Gunhee Cho, Yun-Gyung Cheong

Dept. of AI, Sungkyunkwan University

Personality Modeling in Language Models Personalized Language Models Persona-Conditioned Generation

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Controlling the personality of a language model usually relies on written profiles or named trait categories, so it can only express what an author writes down or what a taxonomy names. We instead propose Personality Latents (PL), which learn a latent for each person from their own writing, with no personality labels and no authored profile, and inject it into a frozen language model as a soft prefix. Following generative latent optimization, each training person receives a free latent that is optimized through the frozen model itself, while an encoder learns to predict these latents so that a new person needs only one forward pass, with optional refinement at test time. We evaluate on held-out people by having the conditioned model answer neutral and interview questions and reading personality from its answers with scorers trained on real human writing. On the MBTI axes PL reaches an AUC of , ahead of persona embedding and prompt-based baselines under identical settings, and the ranking holds when the answers are scored in three different embedding spaces. A latent learned with Qwen2.5-3B-Instruct also transfers personality to Llama-3.1-8B-Instruct through a new adapter alone.