Comparison-based Active Preference Learning for Multi-dimensional Personalization
Department of Computer Science and Engineering, POSTECH, South Korea（计算机科学与工程系，POSTECH，韩国）；Graduate School of Artificial Intelligence, POSTECH, South Korea（人工智能研究生院，POSTECH，韩国）
Large language models (LLMs) have shown remarkable success, but aligning them with human preferences remains a core challenge. As individuals have their own, multi-dimensional preferences, recent studies have explored multi-dimensional personalization, which aims to enable models to generate responses personalized to explicit preferences. However, human preferences are often implicit and thus difficult to articulate, limiting the direct application of this approach. To bridge this gap, we propose Active Multi-dimensional Preference Learning (AMPLe), designed to capture implicit user preferences from interactively collected comparative feedback. Building on Bayesian inference, our work introduces a modified posterior update procedure to mitigate estimation bias and potential noise in comparisons. Also, inspired by generalized binary search, we employ an active query selection strategy to minimize the number of required comparisons by a user. Through theoretical analysis and experiments on language generation tasks, we demonstrate feedback efficiency and effectiveness of our framework in personalizing model responses. Our code is publicly available at https://github.com/ml-postech/AMPLe .
T-POP: Test-Time Personalization with Online Preference Feedback
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；East China Normal University（华东师范大学）；Shenzhen Loop Area Institute（深圳河套学院）；Tianjin University（天津大学）；The Chinese University of Hong Kong（香港中文大学）；Beihang University（北京航空航天大学）；City University of Hong Kong（香港城市大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港理工大学（广州））
BAPO: Base-Anchored Preference Optimization for Overcoming Forgetting in Large Language Models Personalization
Graduate School of AI, KAIST（韩国科学技术院人工智能研究生院）；Samsung Advanced Institute of Technology（三星先进技术研究院）；Department of Electrical and Computer Engineering, UBC（不列颠哥伦比亚大学电气与计算机工程系）
Bi-directional personalization reinforcement learning-based architecture with active learning using a multi-model data service for the travel nursing industry
Interpretability Is Not Just a Tax: Sparsity and Diversity Yield Legible and Composable Reward Experts for Personalization
Saarland University（萨尔兰大学）；Independent Researcher（独立研究者）；Bielefeld University（比勒菲尔德大学）；Max Planck Institute for Software Systems（马克斯·普朗克软件系统研究所）；Max Planck Institute for Informatics（马克斯·普朗克信息研究所）
University of California, Riverside（加州大学河滨分校）；Yale University（耶鲁大学）；Georgia Institute of Technology（佐治亚理工学院）；University of Michigan, Ann Arbor（密歇根大学安娜堡分校）；University of Toronto（多伦多大学）
