This study investigates the capacity of Large Language Models (LLMs) to infer the Big Five personality traits from free-form user interactions. The results demonstrate that a chatbot powered by GPT-4 can infer personality with moderate accuracy, outperforming previous approaches drawing inferences from static text content. The accuracy of inferences varied across different conversational settings. Performance was highest when the chatbot was prompted to elicit personality-relevant information from users (mean r=.443, range=[.245, .640]), followed by a condition placing greater emphasis on naturalistic interaction (mean r=.218, range=[.066, .373]). Notably, the direct focus on personality assessment did not result in a less positive user experience, with participants reporting the interactions to be equally natural, pleasant, engaging, and humanlike across both conditions. A chatbot mimicking ChatGPT's default behavior of acting as a helpful assistant led to markedly inferior personality inferences and lower user experience ratings but still captured psychologically meaningful information for some of the personality traits (mean r=.117, range=[-.004, .209]). Preliminary analyses suggest that the accuracy of personality inferences varies only marginally across different socio-demographic subgroups. Our results highlight the potential of LLMs for psychological profiling based on conversational interactions. We discuss practical implications and ethical challenges associated with these findings.
Exploring the Impact of Personality Traits on Conversational Recommender Systems: A Simulation with Large Language Models
Chinese University of Hong Kong（香港中文大学）；National University of Singapore（新加坡国立大学）；Singapore Management University（新加坡管理大学）；Hong Kong Baptist University（香港浸会大学）；Huazhong University of Science and Technology（华中科技大学）
Knowing You at First Glance: Inferring Apparent Personality from Faces
Institute of Automation, CAS（中国科学院自动化研究所）；Institute of Information Engineering, CAS（中国科学院信息工程研究所）；School of Cyber Security, UCAS（中国科学院大学网络空间安全学院）；School of Artificial Intelligence, UCAS（中国科学院大学人工智能学院）；Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, CAS（中国科学院香港创新研究院人工智能与机器人中心）；School of Computer Science and Engineering, the Faculty of Innovation Engineering, M.U.S.T（澳门科技大学创新工程学院计算机科学与工程系）
Dynamic Personality Adaptation in Large Language Models via State Machines
Institute for Geoinformatics, University of Münster（地理信息研究所，穆尔斯特大学）；Faculty of Mathematics and Computer Science, University of Münster（数学与计算机科学学院，穆尔斯特大学）；Department of Psychology, University of Münster（心理学系，穆尔斯特大学）；Institute of Medical Education and Student Affairs, University of Münster（医学教育与学生事务研究所，穆尔斯特大学）
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）
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）
HIPPD: Brain-Inspired Hierarchical Information Processing for Personality Detection
School of Electronics and Computer Science University of Southampton（电子与计算机科学学院 英国南安普顿大学）；Department of Computational Biology Mohamed bin Zayed University of Artificial Intelligence（计算生物学系 摩洛哥本·扎耶德人工智能大学）
LLMvsSmall Model? Large Language Model Based Text Augmentation Enhanced Personality Detection Model
Beijing Institute of Technology（北京理工大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））
LL4G: Self-Supervised Dynamic Optimization for Graph-Based Personality Detection
University of Southampton（南安普顿大学）；University of Essex（埃塞克斯大学）；Birkbeck, University of London（伦敦大学伯克贝克学院）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
DPN-LE: Dual Personality Neuron Localization and Editing for Large Language Models
Southeast University（东南大学）；Shanghai Jiao Tong University（上海交通大学）；East China Normal University（华东师范大学）；Zhongguancun Academy（中关村学院）；Zhejiang University of Technology（浙江工业大学）；Kuaishou Technology（快手科技）；Northeastern University（东北大学）；Tsinghua University（清华大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）
Prompt-based Personality Profiling: Reinforcement Learning for Relevance Filtering
Institut für Maschinelle Sprachverarbeitung, University of Stuttgart, Germany（机械语言处理研究所，斯图加特大学）；Computational Digital Psychology, Interchange Forum for Reflecting on Intelligent Systems, University of Stuttgart, Germany（计算数字心理学，智能系统反思交流论坛，斯图加特大学）；Fundamentals of Natural Language Processing, University of Bamberg, Germany（自然语言处理基础，巴姆堡大学）
We present an intelligent virtual interviewer that engages with a user in a text-based conversation and automatically infers the user's psychological traits, such as personality. We investigate how the personality of a virtual interviewer influences a user's behavior from two perspectives: the user's willingness to confide in, and listen to, a virtual interviewer. We have developed two virtual interviewers with distinct personalities and deployed them in a real-world recruiting event. We present findings from completed interviews with 316 actual job applicants. Notably, users are more willing …
Understanding complex user behaviour under various conditions, scenarios and journeys can be fundamental to the improvement of the user-experience for a given system. Predictive models of user reactions, responses -- and in particular, emotions -- can aid in the design of more intuitive and usable systems. Building on this theme, the preliminary research presented in this paper correlates events and interactions in an online social network against user behaviour, focusing on personality traits. Emotional context and tone is analysed and modelled based on varying types of sentiments that users …
IUSS Institute for Advanced Study（IUSS高等研究院）；IMT Institute for Advanced Studies（IMT高等研究院）
In online social networks, users tend to select information that adhere to their system of beliefs and to form polarized groups of like minded people. Polarization as well as its effects on online social interactions have been extensively investigated. Still, the relation between group formation and personality traits remains unclear. A better understanding of the cognitive and psychological determinants of online social dynamics might help to design more efficient communication strategies and to challenge the digital misinformation threat. In this work, we focus on users commenting posts publ…
Personality traits such as Need for Cognition, Locus of Control, Mindset and Self-efficacy could impact the perception, acceptance and appreciation of recommendations provided to support configuration tasks in the End User Development (EUD) context. In this paper we describe the user model services we have developed to measure such traits. These services can be accessed by users through a simple web interface and can be queried by EUD applications by means of REST API.
