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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. 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 Machine Learning

Sep 23

Adaptive Helpfulness-Harmlessness Alignment with Preference Vectors

arXiv:2504.20106v4 Announce Type: replace Abstract: Ensuring that large language models (LLMs) are both helpful and harmless is a critical challenge, as overly strict constraints can lead to excessiv...

By Ren-Wei Liang, Chin-Ting Hsu, Chan-Hung Yu, Saransh Agrawal, Shih-Cheng Huang, Chieh-Yen Lin, Shang-Tse Chen, Kuan-Hao Huang, Shao-Hua Sun

llms reinforcement-learning safety

arXiv AI

Sep 1

Evaluating the Hidden Costs of Personalization in Large Language Models

arXiv:2608.28833v1 Announce Type: new Abstract: While Large language models (LLMs) incorporate user personalization signals to improve usability and helpfulness, they increasingly shift from providin...

By Yumeng Wang, Yuchen Wu, Cheng Qian, Zhiyuan Fan, Hyeonjeong Ha, Shujin Wu, Jiayu Liu, Heng Ji, Ge Wang

llms safety

Hugging Face Trending Papers

Sep 8

Suan: Rectifying Direct Preference Safety Alignment in Large Language Models

Integrating robust safety guardrails into Large Language Models (LLMs) is essential for delivering helpful yet harmless responses. While proprietary systems exhibit reliable safety controls, their und...

llms benchmarks 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

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Related stories

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

Adaptive Helpfulness-Harmlessness Alignment with Preference Vectors

Evaluating the Hidden Costs of Personalization in Large Language Models

Suan: Rectifying Direct Preference Safety Alignment in Large Language Models

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