Generative Personality Simulation via Theory-Informed Structured Interview
Department of Psychological Sciences & 2 Department of Computer Science, Rice University（1 心理学系 & 2 计算机科学系，莱斯大学）；Department of Electrical and Computer Engineering, Northeastern University（3 电气与计算机工程系，东北大学）；Department of Computer Science, Johns Hopkins University（4 计算机科学系，约翰霍普金斯大学）；Microsoft Research Asia（微软亚洲研究院）
Pengda Wang, Huiqi Zou, Han Jiang, Hanjie Chen, Tianjun Sun, Xiaoyuan Yi, Ziang Xiao, Frederick L. Oswald
备注：
Accepted at EACL 2026; 87 Pages, 68 Tables, 10 Figures
Despite their potential as human proxies, LLMs often fail to generate heterogeneous data with human-like diversity, thereby diminishing their value in advancing social science research. To address this gap, we propose a novel method to incorporate psychological insights into LLM simulation through the Personality Structured Interview (PSI). PSI leverages psychometric scale-development procedures to capture personality-related linguistic information from a formal psychological perspective. To systematically evaluate simulation fidelity, we developed a measurement theory grounded evaluation procedure that considers the latent construct nature of personality and evaluates its reliability, structural validity, and external validity. Results from three experiments demonstrate that PSI effectively improves human-like heterogeneity in LLM-simulated personality data and predicts personality-related behavioral outcomes. We further offer a theoretical framework for designing theory-informed structured interviews to enhance the reliability and effectiveness of LLMs in simulating human-like data for broader psychometric research.
Prompting-in-a-Series: Psychology-Informed Contents and Embeddings for Personality Recognition With Decoder-Only Models
Laboratoire de traitement et transport de l'information, Université Sorbonne Paris Nord, France（信息处理与传输实验室，索邦巴黎北大学，法国）；Institute of Communication Studies, National Yang Ming Chiao Tung University, Taiwan（传播学研究所，国立阳明交通大学，台湾）
InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviews
Fudan University（复旦大学）；Carnegie Mellon University（卡内基梅隆大学）；The Chinese University of Hong Kong（香港中文大学）；Renmin University of China（中国人民大学）；Beijing University of Technology（北京工业大学）；Boston University（波士顿大学）；RhineAI（莱茵AI公司）；SenseTime（商汤科技）
University of Oxford（牛津大学）；The Alan Turing Institute（艾伦·图灵研究所）；University of Warwick（华威大学）
Bipolar disorder (BD) and borderline personality disorder (BPD) are both chronic psychiatric disorders. However, their overlapping symptoms and common comorbidity make it challenging for the clinicians to distinguish the two conditions on the basis of a clinical interview. In this work, we first present a new multi-modal dataset containing interviews involving individuals with BD or BPD being interviewed about a non-clinical topic . We investigate the automatic detection of the two conditions, and demonstrate a good linear classifier that can be learnt using a down-selected set of features fro…
LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals
Computer Science Department, Stanford University（斯坦福大学计算机科学系）；Department of Communication Studies, Northwestern University（西北大学传播学系）；Department of Communication, University of Washington（华盛顿大学传播学系）；Google DeepMind（谷歌DeepMind）；Department of Sociology, Stanford University（斯坦福大学社会学系）；Sciences Po（巴黎政治学院）
Computational Phenomenology of Borderline Personality Disorder: A Comparative Evaluation of LLM-Simulated Expert Personas and Human Clinical Experts
IDEAS Research Institute（IDEAS研究机构）；Adam Mickiewicz University（亚当·密茨凯维奇大学）；AMU Center for Artificial Intelligence（AMU人工智能中心）；Poznań University of Medical Sciences（波兹南医科大学）；Maria Curie-Skłodowska University（玛丽·居里-斯洛多夫斯卡大学）；University of Warsaw（华沙大学）
University of Oxford（牛津大学）；The Alan Turing Institute（艾伦·图灵研究所）；University of Sheffield（谢菲尔德大学）；Queen Mary University of London（伦敦玛丽女王大学）
Bipolar disorder (BD) and borderline personality disorder (BPD) are two chronic mental health conditions that clinicians find challenging to distinguish based on clinical interviews, due to their overlapping symptoms. In this work, we investigate the automatic detection of these two conditions by modelling both verbal and non-verbal cues in a set of interviews. We propose a new approach of modelling short-term features with visibility-signature transform, and compare it with widely used high-level statistical functions. We demonstrate the superior performance of our proposed signature-based mo…
Knowledge-Based Design Requirements for Generative Social Robots in Higher Education
Zurich University of Applied Sciences（应用科学大学苏黎世）；University of Applied Sciences and Arts Northwestern Switzerland（西北瑞士应用科学与艺术大学）；Bielefeld University（比勒菲尔德大学）
PRISMA: Preference-Reinforced Self-Training Approach for Interpretable Emotionally Intelligent Negotiation Dialogues
Department of Computer Science and Engineering, Indian Institute of Technology Patna, India（印度理工学院帕纳瓦分校计算机科学与工程系）；School of Artificial Intelligence and Data Science, Indian Institute of Technology Jodhpur, India（印度理工学院朱道尔分校人工智能与数据科学学院）
PsychoGAT: A Novel Psychological Measurement Paradigm through Interactive Fiction Games with LLM Agents
Tsinghua University（清华大学）；The Second Xiangya Hospital, Central South University（中南大学湘雅二医院）；Medical Psychological Institute, Central South University（中南大学医学心理学研究所）；National Clinical Research Center for Mental Disorders（国家精神疾病临床研究中心）；Carnegie Mellon University（卡内基梅隆大学）
