{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/does-role-playing-chatbots-capture-the","title":"InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviews","arxiv_id":"2310.17976","date":"2023-10-27","proceeding":null,"authors":["Xintao Wang","Yunze Xiao","Jen-tse Huang","Siyu Yuan","Rui Xu","Haoran Guo","Quan Tu","Yaying Fei","Ziang Leng","Wei Wang","Jiangjie Chen","Cheng Li","Yanghua Xiao"],"abstract":"Role-playing agents (RPAs), powered by large language models, have emerged as a flourishing field of applications. However, a key challenge lies in assessing whether RPAs accurately reproduce the personas of target characters, namely their character fidelity. Existing methods mainly focus on the knowledge and linguistic patterns of characters. This paper, instead, introduces a novel perspective to evaluate the personality fidelity of RPAs with psychological scales. Overcoming drawbacks of previous self-report assessments on RPAs, we propose InCharacter, namely Interviewing Character agents for personality tests. Experiments include various types of RPAs and LLMs, covering 32 distinct characters on 14 widely used psychological scales. The results validate the effectiveness of InCharacter in measuring RPA personalities. 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