arXiv AI July 24, 2026 By Ashutosh Srivastava, Siddharth Yedlapati, Vinay Aggarwal, Yaman Kumar Singla, Shashwat Dixit, Jitendra Ajmera, Balaji Krishnamurthy

Benchmarking the Personalization Capabilities of Large Language Models

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arXiv:2607. 20471v1 Announce Type: new Abstract: Personalization, the act of varying a message to induce action from a specific receiver while keeping sender, channel, and time fixed, has a long tradition in psychology and marketing as a two-party problem in which sender and receiver have independent objectives.

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

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Jun 3

$\Psi$-Bench: Evaluating Persona-Sensitive Influencing in Persuasive Dialogues

arXiv:2606. 02754v1 Announce Type: new Abstract: Personalization is a crucial capability of modern language agents.

By Peixuan Han, Hongyi Du, Jiayu Liu, Yihang Sun, Yutong Liu, Jiaxuan You

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Jul 31

Personalized RewardBench: Evaluating Reward Models with Human Aligned Personalization

arXiv:2604. 07343v2 Announce Type: replace-cross Abstract: Pluralistic alignment has emerged as a critical frontier in the development of Large Language Models (LLMs), with reward models (RMs) serving as a central mechanism for capturing diverse human values.

By Qiyao Ma, Dechen Gao, Rui Cai, Boqi Zhao, Hanchu Zhou, Junshan Zhang, Zhe Zhao

llms reinforcement-learning benchmarks safety

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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

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Jun 6

A Model of Multi-turn Human Persuadability Using Probabilistic Belief Tracing

arXiv:2606. 05330v1 Announce Type: cross Abstract: Large language models can shift human beliefs across high-stakes domains, but most persuasion studies rely on pre/post belief change.

By Jared Moore, Noah Goodman, Nick Haber, Max Kleiman-Weiner

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Jun 26

GRAG: Generic Response-Augmented Generation Framework for Personalized Conversational Systems

arXiv:2606. 21097v2 Announce Type: replace-cross Abstract: Deploying highly capable personalized conversational agents in resource-constrained or privacy-sensitive environments remains a significant challenge.

By Junfeng Liu, Christopher T. Symons, Ranga Raju Vatsavai

llms agents fine-tuning benchmarks 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

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$\Psi$-Bench: Evaluating Persona-Sensitive Influencing in Persuasive Dialogues

Personalized RewardBench: Evaluating Reward Models with Human Aligned Personalization

Re-Centering Humans in LLM Personalization

A Model of Multi-turn Human Persuadability Using Probabilistic Belief Tracing

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