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Recent advances in large language models (LLMs), such as ChatGPT, haveshowcased remarkable zero-shot performance across various NLP tasks. However,the potential of LLMs in personality detection, which involves identifying anindividual's personality from their written texts, remains largely unexplored.Drawing inspiration from Psychological Questionnaires, which are carefullydesigned by psychologists to evaluate individual personality traits through aseries of targeted items, we argue that these items can be regarded as acollection of well-structured chain-of-thought (CoT) processes. Byincorporating these processes, LLMs can enhance their capabilities to make morereasonable inferences on personality from textual input. In light of this, wepropose a novel personality detection method, called PsyCoT, which mimics theway individuals complete psychological questionnaires in a multi-turn dialoguemanner. In particular, we employ a LLM as an AI assistant with a specializationin text analysis. We prompt the assistant to rate individual items at each turnand leverage the historical rating results to derive a conclusive personalitypreference. Our experiments demonstrate that PsyCoT significantly improves theperformance and robustness of GPT-3.5 in personality detection, achieving anaverage F1 score improvement of 4.23/10.63 points on two benchmark datasetscompared to the standard prompting method. Our code is available athttps://github.com/TaoYang225/PsyCoT.

PsyCoT: Psychological Questionnaire as Powerful Chain-of-Thought for Personality Detection