arXiv AI June 9, 2026 By Cristina Garbacea

Large Language Models Should Learn Personalized Rather Than Aggregated Human Preferences

arXiv:2606. 07629v1 Announce Type: cross Abstract: Current approaches to aligning large language models (LLMs) aggregate diverse human preferences into a single reward signal, effectively optimizing for a hypothetical ``average user'' who represents no real person particularly well.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 9

Personalization Meets Safety:Mechanisms,Risks,and Mitigations in Personalized LLMs

arXiv:2606. 09038v1 Announce Type: new Abstract: Large Language Models (LLMs) have enabled increasingly personalized interactions by adapting to users' preferences, contexts, and long-term histories.

By Yanyan Luo, Xue Han, Ruiqiao Bai, Xin Huang, Yitong Wang, Qian Hu, Qing Wang, Chunxu Zhao, Jie Liu, Cong Geng, Lehao Xing, Pengwei Hu, Junlan Feng

llms agents reinforcement-learning fine-tuning efficiency multimodal safety

arXiv AI

Jun 24

When Preferences Fail to Become Incentives: A Utility-Behavior Gap in Large Language Models

arXiv:2606. 22974v2 Announce Type: replace Abstract: Recent work on preference elicitation in large language models (LLMs) has demonstrated that, when given a series of choices between two outcomes, LLMs reveal a coherent, model-specific utility structure.

By Yujun Zhou, Christopher M. Ackerman

llms safety

arXiv AI

Jun 2

Isolating LLM Lexical Bias: A Curation-Free Triangulated Metric for Preference-Stage Learning

arXiv:2606. 00334v1 Announce Type: cross Abstract: Various language domains have undergone remarkable changes in recent years; these shifts are largely attributed to the advent of Large Language Models and their misalignment with natural language usage.

By Xiaoyang Ming, Jose Hernandez, Thomas Stephan Juzek

llms reinforcement-learning safety

arXiv Machine Learning

Jun 30

Synthetic Interaction Data for Scalable Personalization in Large Language Models

arXiv:2602. 12394v2 Announce Type: replace Abstract: Personalized prompting offers large opportunities for deploying large language models (LLMs) to diverse users, yet existing prompt optimization methods primarily focus on task-level optimization while largely overlooking user-specific preferences and latent constraints of individual users.

By Yuchen Ma, Yue Huang, Wenjie Wang, Xiaonan Luo, Xiangliang Zhang, Stefan Feuerriegel

llms agents reinforcement-learning benchmarks safety

arXiv AI

Jun 8

Re-Centering Humans in LLM Personalization

arXiv:2606. 06614v1 Announce Type: cross Abstract: Despite growing interest, most evaluations of large language models' (LLMs') personalization abilities have relied on synthetic data.

By Lechen Zhang, Jiarui Liu, Tal August

llms

arXiv AI

Aug 7

Cautious Context Steering for Language Model Personalization

arXiv:2608. 05813v1 Announce Type: new Abstract: Personalizing language models (LMs) to individual user preferences is essential for aligning responses with diverse goals and backgrounds.

By Gihoon Kim, Jeyoung Lee, Suhan Woo, Sekwon Oh, Minsu Jeon, Hyounsoo Han, Euntai Kim

llms reinforcement-learning fine-tuning efficiency benchmarks

Related stories

Personalization Meets Safety:Mechanisms,Risks,and Mitigations in Personalized LLMs

When Preferences Fail to Become Incentives: A Utility-Behavior Gap in Large Language Models

Isolating LLM Lexical Bias: A Curation-Free Triangulated Metric for Preference-Stage Learning

Synthetic Interaction Data for Scalable Personalization in Large Language Models

Re-Centering Humans in LLM Personalization

Cautious Context Steering for Language Model Personalization