Creating human-like large language model (LLM) agents is crucial for faithful social simulation. Having LLMs role-play based on demographic information sometimes improves human likeness but often does not. This study assessed whether LLM alignment with human behavior can be improved by integrating information from empirically-derived human belief networks. Using data from a human survey, we estimated a belief network encompassing 64 topics loading on nine non-overlapping latent factors. We then seeded LLM-based agents with an opinion on one topic, and assessed the alignment of its expressed opinions on remaining test topics with corresponding human data. Role-playing based on demographic information alone did not align LLM and human opinions, but seeding the agent with a single belief greatly improved alignment for topics related in the belief network, and not for topics outside the network. These results suggest a novel path for human-LLM belief alignment in work seeking to simulate and understand patterns of belief distributions in society.
CuMA: Aligning LLMs with Sparse Cultural Values via Demographic-Aware Mixture of Adapters
Southeast University（东南大学）；ByteDance Inc.（字节跳动公司）；Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China（新一代人工智能技术及其交叉应用重点实验室（东南大学），中华人民共和国教育部，中国）
DiverValue-Bench: A Benchmark and Fine-Tuning Framework for Aligning Large Language Models with Diverse Human Values
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Renmin University of China（中国人民大学）；Beijing Institute of AI Safety and Governance（北京人工智能安全与治理研究院）；University of Chinese Academy of Sciences（中国科学院大学）
Large Language Model-based Role-Playing for Personalized Medical Jargon Extraction
Manning College of Information and Computer Sciences, University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校曼宁信息与计算机科学学院）；Mendoza College of Business, University of Notre Dame（圣母大学门多萨商学院）；University of Massachusetts Medical School（马萨诸塞大学医学院）；Miner School of Computer and Information Sciences, University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校迈纳计算机与信息科学学院）；VA Bedford Health Care（贝德福德退伍军人事务医疗中心）
Human Psychometric Questionnaires Mischaracterize LLM Behavior
Graduate School of Data Science, Seoul National University（首尔国立大学数据科学研究生院）；Department of Communication, Interdisciplinary Program in Artificial Intelligence, Seoul National University（首尔国立大学通信系人工智能交叉学科项目）
VisIT-Bench: A Benchmark for Vision-Language Instruction Following Inspired by Real-World Use
Hebrew University（希伯来大学）；Google Research（谷歌研究院）；UCLA（加州大学洛杉矶分校）；Allen Institute for AI（艾伦人工智能研究所）；University of Washington（华盛顿大学）；UCSB（加州大学圣塔芭芭拉分校）；Stanford（斯坦福大学）；LAION
When Persona Attributes Improve Population Alignment in Large Language Models
GESIS – Leibniz Institute for the Social Sciences（GESIS——莱布尼茨社会科学研究所）；University of Mannheim（曼海姆大学）；Complexity Science Hub（复杂科学中心）；RWTH Aachen University（亚琛工业大学）
