Large Language Models (LLMs) demonstrate increasingly human-like abilities across a wide variety of tasks. In this paper, we investigate whether LLMs like ChatGPT can accurately infer the psychological dispositions of social media users and whether their ability to do so varies across socio-demographic groups. Specifically, we test whether GPT-3.5 and GPT-4 can derive the Big Five personality traits from users' Facebook status updates in a zero-shot learning scenario. Our results show an average correlation of r = .29 (range = [.22, .33]) between LLM-inferred and self-reported trait scores - a level of accuracy that is similar to that of supervised machine learning models specifically trained to infer personality. Our findings also highlight heterogeneity in the accuracy of personality inferences across different age groups and gender categories: predictions were found to be more accurate for women and younger individuals on several traits, suggesting a potential bias stemming from the underlying training data or differences in online self-expression. The ability of LLMs to infer psychological dispositions from user-generated text has the potential to democratize access to cheap and scalable psychometric assessments for both researchers and practitioners. On the one hand, this democratization might facilitate large-scale research of high ecological validity and spark innovation in personalized services. On the other hand, it also raises ethical concerns regarding user privacy and self-determination, highlighting the need for stringent ethical frameworks and regulation.
Who We Are, Where We Are: Mental Health at the Intersection of Person, Situation, and Large Language Models
Department of Computer Science, Stony Brook University（石英布大学计算机科学系）；Oslo Metropolitan University, Oslo Business School（奥斯陆 Metropolitan 大学奥斯陆商学院）；University of Texas at Dallas, Dept. of Psychology（德克萨斯大学达拉斯分校心理学系）
PsychēChat: An Empathic Framework Focused on Emotion Shift Tracking and Safety Risk Analysis in Psychological Counseling
East China University of Science and Technology（东华大学）；Shanghai Changning Mental Health Center, Affiliated Mental Health Center of East China Normal University（上海昌宁心理健康中心，华东师范大学附属心理健康中心）
Mechanistic Interpretability of Emotion Inference in Large Language Models
Institute for Creative Technologies, University of Southern California (USC)（创意技术研究所，南加州大学）；Information Sciences Institute, University of Southern California (USC)（信息科学研究所，南加州大学）；Department of Computer Science, University of Southern California (USC)（计算机科学系，南加州大学）；Department of Statistics and Data Science, University of California, Los Angeles (UCLA)（统计与数据科学系，加州大学洛杉矶分校）
Measuring the Unspoken: A Disentanglement Model and Benchmark for Psychological Analysis in the Wild
College of Computer Science, National University of Defense Technology（计算机科学学院，国防科技大学）；Shien-Ming Wu School of Intelligent Engineering, South China University of Technology（智能工程学院，华南理工大学）
Cognition Chain for Explainable Psychological Stress Detection on Social Media
University of Oxford（牛津大学）；Tsinghua University（清华大学）；Beijing Normal University（北京师范大学）；Wuhan University（武汉大学）；Oxford Suzhou Centre for Advanced Research（牛津苏州高等研究院）
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）
EmoStage: A Framework for Accurate Empathetic Response Generation via Perspective-Taking and Phase Recognition
The University of Electro-Communications（电气通信大学）；iDEAR Human Support Service（iDEAR人类支持服务）；Japanese Organization of Mental Health and Educational Agencies（日本心理健康与教育机构组织）
Helen Wills Neuroscience Institute, University of California Berkeley, USA（加州大学伯克利分校海伦·威尔斯神经科学研究所）；Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London, UK（马克斯·普朗克伦敦大学学院计算精神病学与衰老研究中心）；Department of Psychology, University of California Berkeley, USA（加州大学伯克利分校心理学系）；Vision Science Group, University of California Berkeley, USA（加州大学伯克利分校视觉科学组）
EERPD: Leveraging Emotion and Emotion Regulation for Improving Personality Detection
State Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University（北京大学计算机学院多媒体信息处理国家重点实验室）；School of Software, BNRist, Tsinghua University（清华大学软件学院北京信息科学与技术国家研究中心）
ValueGraph: Value-Signal Guided Graph Pre-training for Contextualized User Representation
School of Computing and Information Systems, Singapore Management University（新加坡管理大学计算与信息系统学院）；Institute of Advanced Intelligence and Computing, A*STAR（新加坡科研局先进智能与计算研究所）
From Visual Perception to Deep Empathy: An Automated Assessment Framework for House-Tree-Person Drawings Using Multimodal LLMs and Multi-Agent Collaboration
Tsinghua University（清华大学）；Shenzhen University（深圳大学）；University of Oxford（牛津大学）；Shenzhen Institute of Education Sciences（深圳市教育科学研究院）；Guangzhou University of Chinese Medicine（广州中医药大学）；Harbin Institute of Technology（哈尔滨工业大学）
ELLIS Alicante（阿利坎特ELLIS）；University of South Bohemia（南波希米亚大学）；Czech Institute of Informatics, Robotics, and Cybernetics, CTU in Prague（布拉格捷克理工大学捷克信息学、机器人与控制论研究所）
Sibyl: Empowering Empathetic Dialogue Generation in Large Language Models via Sensible and Visionary Commonsense Inference
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；WeChat AI, Tencent Inc（腾讯微信人工智能）；Meituan Group（美团集团）
Rethinking stance detection: A theoretically-informed research agenda for user-level inference using language models
Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（高性能计算研究所（A*STAR））；School of Computing and Information Systems, Singapore Management University（新加坡管理大学计算与信息系统学院）；The Hong Kong Polytechnic University（香港理工大学）
Illusory Pattern Perception Drives Spurious Inference in Large Language Models
National University of Singapore (Chongqing) Research Institute（新加坡国立大学重庆研究院）；National University of Singapore（新加坡国立大学）；Zhejiang University（浙江大学）；The Chinese University of Hong Kong（香港中文大学）；University of Macau（澳门大学）
An Offline Mobile Conversational Agent for Mental Health Support: Learning from Emotional Dialogues and Psychological Texts with Student-Centered Evaluation
Department of Electrical Engineering and Computer Science, Indian Institute of Science Education and Research Bhopal（电子工程与计算机科学系，比哈尔印度科学教育与研究中心）
Social media, as a major platform for communication and information exchange, is a rich repository of the opinions and sentiments of 2.3 billion users about a vast spectrum of topics. To sense the whys of certain social user's demands and cultural-driven interests, however, the knowledge embedded in the 1.8 billion pictures which are uploaded daily in public profiles has just started to be exploited since this process has been typically been text-based. Following this trend on visual-based social analysis, we present a novel methodology based on Deep Learning to build a combined image-and-text…
