arXiv AI

T-POP: Test-Time Personalization with Online Preference Feedback

arXiv:2509. 24696v2 Announce Type: replace-cross Abstract: Personalizing large language models (LLMs) to individual user preferences is a critical step beyond generating generically helpful responses.

arXiv Machine Learning

Jun 30

REAR: Test-time Preference Realignment through Reward Decomposition

arXiv:2606. 30339v1 Announce Type: cross Abstract: Aligning large language models (LLMs) with diverse user preferences is a critical yet challenging task.

By Fuxiang Zhang, Pengcheng Wang, Chenran Li, Yi-Chen Li, Yuxin Chen, Lang Feng, Chenfeng Xu, Masayoshi Tomizuka, Bo An

llms 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 30

Aligning Language Model Benchmarks with Pairwise Preferences

arXiv:2602. 02898v3 Announce Type: replace Abstract: Language model benchmarks are pervasive and computationally-efficient proxies for real-world performance.

By Marco Gutierrez, Xinyi Leng, Hannah Cyberey, Jonathan Richard Schwarz, Ahmed Alaa, Thomas Hartvigsen

llms 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

1d ago

Reasoning-Based Personalized Generation for Users with Sparse Data

arXiv:2602. 21219v2 Announce Type: replace-cross Abstract: Large Language Model (LLM) personalization holds great promise for tailoring responses by leveraging personal context and history.

By Bo Ni, Branislav Kveton, Samyadeep Basu, Subhojyoti Mukherjee, Leyao Wang, Franck Dernoncourt, Sungchul Kim, Seunghyun Yoon, Zichao Wang, Ruiyi Zhang, Puneet Mathur, Jihyung Kil, Jiuxiang Gu, Nedim Lipka, Yu Wang, Ryan A. Rossi, Tyler Derr

llms benchmarks safety

arXiv AI

Aug 11

SPRInG: Continual LLM Personalization via Selective Parametric Adaptation and Retrieval-Interpolated Generation

arXiv:2601. 09974v2 Announce Type: replace Abstract: Personalizing Large Language Models typically relies on static retrieval or one-time adaptation, assuming user preferences remain invariant over time.

By Seoyeon Kim, Jaehyung Kim

llms benchmarks

arXiv Machine Learning

2d ago

Retrieve-then-Adapt: Retrieval-Augmented Test-Time Adaptation for Sequential Recommendation

arXiv:2604. 05379v2 Announce Type: replace-cross Abstract: The sequential recommendation (SR) task aims to predict the next item based on users' historical interaction sequences.

By Xing Tang, Ziqiang Cui, Jingyang Bin, Xiaokun Zhang, Fuyuan Lyu, Jingyan Jiang, Dugang Liu, Chen Ma, Xiuqiang He

rag fine-tuning benchmarks

arXiv Machine Learning

Jun 5

Learning to Route LLMs from Implicit Cost-Performance Preferences via Meta-Learning

arXiv:2606. 06178v1 Announce Type: new Abstract: Large language models (LLMs) present a trade-off between performance and cost, where more powerful models incur greater expense.

By Jiahao Zeng, Ming Tang, Ningning Ding

llms reinforcement-learning

arXiv AI

Jun 9

Provably Efficient Personalized Multi-Objective Bandits with Proactive Conversational Queries

arXiv:2606. 08410v1 Announce Type: cross Abstract: Personalized decision-making in multi-objective bandits requires learning user-specific trade-offs among competing objectives.

By Linfeng Cao, Ming Shi, Ness B. Shroff

reinforcement-learning

arXiv AI

Jun 3

Taiji: Pareto Optimal Policy Optimization with Semantics-IDs Trade-off for Industrial LLM-Enhanced Recommendation

arXiv:2606. 03866v1 Announce Type: cross Abstract: Scaling recommender systems via large language models (LLMs) has become a prominent trend in the industry.

By Yuecheng Li, Zeyu Song, Jing Yao, Chi Lu, Peng Jiang, Kun Gai

llms reinforcement-learning fine-tuning safety

arXiv AI

Jul 2

PAPA: Online Personalized Active Preference Alignment

arXiv:2607. 00486v1 Announce Type: cross Abstract: Diffusion models are highly effective at modeling complex data distributions, including images and text.

By Anindya Sarkar, Nasik Muhammad Nafi, Isaac Lyngaas, Muralikrishnan Gopalakrishnan Meena, Yevgeniy Vorobeychik

diffusion reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jun 11

Toward Preference-aligned Large Language Models via Residual-based Model Steering

arXiv:2509. 23982v2 Announce Type: replace-cross Abstract: Preference alignment is a critical step in making Large Language Models (LLMs) useful and aligned with (human) preferences.

By Lucio La Cava, Andrea Tagarelli

llms reinforcement-learning benchmarks safety

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