Recent advances in large language models (LLMs), such as ChatGPT, have showcased remarkable zero-shot performance across various NLP tasks. However, the potential of LLMs in personality detection, which involves identifying an individual's personality from their written texts, remains largely unexplored. Drawing inspiration from Psychological Questionnaires, which are carefully designed by psychologists to evaluate individual personality traits through a series of targeted items, we argue that these items can be regarded as a collection of well-structured chain-of-thought (CoT) processes. By incorporating these processes, LLMs can enhance their capabilities to make more reasonable inferences on personality from textual input. In light of this, we propose a novel personality detection method, called PsyCoT, which mimics the way individuals complete psychological questionnaires in a multi-turn dialogue manner. In particular, we employ a LLM as an AI assistant with a specialization in text analysis. We prompt the assistant to rate individual items at each turn and leverage the historical rating results to derive a conclusive personality preference. Our experiments demonstrate that PsyCoT significantly improves the performance and robustness of GPT-3.5 in personality detection, achieving an average F1 score improvement of 4.23/10.63 points on two benchmark datasets compared to the standard prompting method. Our code is available at https://github.com/TaoYang225/PsyCoT.

翻译：近期，以ChatGPT为代表的大语言模型（LLMs）在各种自然语言处理任务中展现出卓越的零样本性能。然而，LLMs在人格检测（即通过书面文本识别个体人格特质）中的潜力尚未得到充分探索。受心理学家精心设计、通过系列针对性项目评估个体人格特质的心理问卷启发，我们认为这些项目可被视为结构化的思维链过程集合。通过整合这些过程，LLMs能够增强从文本输入中对人格做出更合理推断的能力。基于此，我们提出了一种新颖的人格检测方法PsyCoT，该方法模拟个体以多轮对话方式完成心理问卷的过程。具体而言，我们使用LLM作为专精于文本分析的AI助手，引导其在每轮对话中对单个项目进行评分，并利用历史评分结果推导出最终的人格偏好。实验表明，与标准提示方法相比，PsyCoT显著提升了GPT-3.5在人格检测中的性能与鲁棒性，在两个基准数据集上的平均F1分数分别提升了4.23分和10.63分。我们的代码开源在https://github.com/TaoYang225/PsyCoT。