Large language models (LLMs) are used worldwide, yet disproportionately reflect Western values, limiting their ability to represent diverse value systems. We introduce PLURAL, a large-scale, value-focused preference dataset grounded in the Integrated Values Survey (IVS), a nationally representative survey spanning 92 countries. Using a two-stage generation pipeline, we transform survey responses into synthetic preference triplets that preserve normative value signals while producing realistic scenarios. We release an initial version of PLURAL containing ~500,000 preference triplets representing people in 20 diverse countries. We evaluate PLURAL in three ways: (i) dataset-level validation showing that it preserves both cross-country value differences and within-country diversity from the original survey; (ii) automated evaluation showing that training on PLURAL improves alignment with target countries' cultural profiles, reducing mean absolute error by up to 27.7% relative to strong baselines; and (iii) blind human evaluation with 176 evaluators in India, Brazil, and Japan, who judge PLURAL-aligned responses as more representative of their national values. Together, these results show that PLURAL contains learnable signal for value steering, offering a scalable resource for pluralistic alignment. Dataset: https://huggingface.co/datasets/agdhruv/plural-alignment
Pluralistic Alignment for Healthcare: A Role-Driven Framework
Cheriton School of Computer Science, University of Waterloo（滑铁卢大学计算机科学学院）；School of Computing, FSE, Macquarie University（麦觉大学计算机学院）；School of Computing and Information System, the University of Melbourne（墨尔本大学计算机与信息系统学院）；Rajax Network Technology (ele.me), China（中国Rajax网络技术（饿了么））；Alibaba Cloud Computing, Alibaba Group, China（中国阿里云 computing，阿里集团）
VISPA: Pluralistic Alignment via Automatic Value Selection and Activation
University of Waterloo（滑铁卢大学）；University of Melbourne（墨尔本大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；MBZUAI（马克斯·普朗克智能系统研究所）；Macquarie University（麦考瑞大学）
From Sycophantic Consensus to Pluralistic Repair: Why AI Alignment Must Surface Disagreement
Department of Computer Science, University of Oxford（牛津大学计算机科学系）；Institute for Ethics in AI, University of Oxford（牛津大学人工智能伦理研究所）；Responsible Technology Institute, University of Oxford（牛津大学负责任技术研究所）
A Survey on Personalized and Pluralistic Preference Alignment in Large Language Models
University of California, San Diego（加利福尼亚大学圣地亚哥分校）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Adobe Research（奥多比研究院）；The Ohio State University（俄亥俄州立大学）；Allen Institute for AI（艾伦人工智能研究所）
VALUEFLOW: Toward Pluralistic and Steerable Value-based Alignment in Large Language Models
Department of Electrical and Computer Engineering, Seoul National University（首尔国立大学电气与计算机工程系）；Interdisciplinary Program in Artificial Intelligence, Seoul National University（首尔国立大学人工智能交叉学科项目）
From Noise to Signal to Selbstzweck: Reframing Human Label Variation in the Era of Post-training in NLP
Department of Computer Science, University of Copenhagen, Denmark（丹麦哥本哈根大学计算机科学系）；Faculty of Law, University of Copenhagen, Denmark（丹麦哥本哈根大学法学院）；Amazon（亚马逊）；LMU Munich & Munich Center for Machine Learning (MCML)（慕尼黑大学及慕尼黑机器学习中心（MCML））
VITAL: A New Dataset for Benchmarking Pluralistic Alignment in Healthcare
School of Computing and Information System, the University of Melbourne, Australia（墨尔本大学计算与信息学院）；School of Computing, FSE, Macquarie University, Australia（麦考瑞大学计算学院）
Positive Alignment: Artificial Intelligence for Human Flourishing
Department of Psychiatry, University of Oxford（牛津大学精神病学系）；Flourishing Intelligence Program, Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford（牛津大学幸福智能计划、幸福与人类繁荣中心、林acre学院）；Google DeepMind（谷歌DeepMind）；LIFE；OpenAI；Anthropic；University of California, Los Angeles（加州大学洛杉矶分校）；Aily Labs（Aily实验室）；Stanford University（斯坦福大学）；Tufts University（塔夫茨大学）；Positive AI Labs（积极AI实验室）；Department of Informatics, University of Sussex（Sussex大学信息学系）；Department of Brain Sciences, Imperial College London（伦敦帝国理工学院脑科学系）
