arXiv AI

ExPerT: Personalizing LLM Responses to Users' Domain Expertise via Query-Wise Semantic and Keystroke Behavioral Cues

arXiv:2607. 01242v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used by end users, yet existing personalization methods relying on static profiles or text-only signals fail to capture query-specific expertise variation.

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

Jun 6

Evaluating the Utility of Personal Health Records in Personalized Health AI

arXiv:2605. 18937v2 Announce Type: replace Abstract: Patient-managed Personal Health Records (PHRs) promises to empower patients to better understand their health; but information in the record is complex, potentially hindering insights.

By Rory Sayres, Kejia Chen, Ayush Jain, Matthew Thompson, Jonathan Richina, Xiang Yin, Jimmy Hu, Fan Zhang, Bob Lou, Mike Sanchez, Ines Mezerreg, Meredith Schreier, Hamsa Subramaniam, I-Ching Lee, Yugang Jia, Daniel Mcduff, Yossi Matias, Avinatan Hassidim, Dale Webster, Yun Liu, Jackie Barr, Quang Duong

llms safety

arXiv AI

Jul 28

Steerable Chatbots: Exploring Personalization Control Interfaces via LLM Activation Steering

arXiv:2505. 04260v3 Announce Type: replace-cross Abstract: Personalizing LLM responses typically requires users to articulate their preferences through prompting, which can be burdensome at cold start and difficult to articulate in natural language.

By Jessica Y. Bo, Tianyu Xu, Ishan Chatterjee, Katrina Passarella-Ward, Achin Kulshrestha, D Shin

llms

arXiv AI

1d ago

QUMem: Personalized Memory for Query-Conditioned User-State Inference in LLM Agents

arXiv:2608. 16168v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly use external memory systems to support personalization by drawing on long and evolving interaction histories, in which user preferences may be distributed across time, change with context, and conflict with earlier evidence.

By Heng Wang, Yifei Li, Lingling Zhang, Pengyu Li, Xinyu Che, Xinyu Zhang, Zesheng Yang

llms agents benchmarks

arXiv AI

Jul 17

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation

arXiv:2607. 14109v1 Announce Type: cross Abstract: Probing the capabilities of Large Language Models (LLMs) and building robust solutions for Multiple-Choice Question Answering (MCQA) remain central challenges in natural language understanding.

By Inder Preet, Shuxin Lin, Dhaval Patel

llms nlp benchmarks

arXiv AI

Jun 16

Combining Retrieval-Augmented Text Generation with LLMs for Reading Content Recommendations

arXiv:2606. 14817v1 Announce Type: cross Abstract: This work presents the design, implementation, and evaluation of a system for generating personalized reading content using Large Language Models (LLMs) combined with Retrieval-Augmented Generation (RAG).

By Sooyeon Kim, Piotr S. Maci\k{a}g

llms rag safety

arXiv AI

Jun 6

Statistical Priors for Implicit Preferences: Decoupling Skill Selection as a Local Harness in Personal Agents

arXiv:2606. 05828v1 Announce Type: new Abstract: As Large Language Model (LLM) capabilities advance, locally deployed personal agents relying on API-based remote models and external skills have emerged as a novel paradigm.

By Zeyu Gan, Huayi Tang, Yong Liu

llms agents

arXiv Machine Learning

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

llms agents benchmarks

arXiv AI

Jul 2

Prompt Optimization for User Simulation in Conversational Recommender Systems: A Multi-Objective Framework

arXiv:2607. 00010v1 Announce Type: cross Abstract: Conversational recommender systems (CRSs) are a core component of next-generation intelligent recommender systems because they enable users to actively elicit preferences, clarify intentions, and adapt recommendations in real time.

By Nipun B Nair, Tongtong Wu, Weiqing Wang

llms safety

arXiv AI

Jun 3

Whose Name Comes Up? II: Benchmarking and Intervention-Based Auditing of LLM-Based Scholar Recommendation

arXiv:2602. 08873v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are now used for academic expert recommendation.

By Lisette Esp\'in-Noboa, Gonzalo Gabriel M\'endez

llms rag benchmarks safety

arXiv AI

Jun 2

SkillPager: Query-Adaptive Intra-Skill Navigation via Semantic Node Retrieval

arXiv:2606. 00822v1 Announce Type: cross Abstract: Skill-based LLM agents increasingly rely on long procedural documents, but full-document prompting wastes tokens and dilutes information critical to execution.

By Zicai Cui, Zihan Guo, Weiwen Liu, Weinan Zhang

llms agents benchmarks

Hugging Face Trending Papers

Jul 13

Enhancing LLMs through human feedback: a journey towards self-improvement

In the rapidly evolving landscape of information retrieval systems, the ability to adapt and improve through user feedback is paramount. This study introduces a novel methodology for refining the performance of a primary Retrieval Augmented Generation (RAG) system by strategically integrating an auxiliary feedback RAG system.

llms rag agents benchmarks

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