PsyCoT：心理问卷作为人格检测的强大思维链
PsyCoT: Psychological Questionnaire as Powerful Chain-of-Thought for Personality Detection
AI总结：
本研究针对大语言模型在人格检测领域潜力未充分挖掘的问题，提出模拟心理问卷作答过程的PsyCoT多轮对话方法，显著提升了GPT-3.5人格检测的性能与鲁棒性。
摘要：
大型语言模型（LLM）如ChatGPT的最新进展，已在各类自然语言处理（NLP）任务中展现出卓越的零样本性能。然而，LLM在人格检测（即从书面文本中识别个体人格）领域的潜力仍未得到充分探索。受心理学家精心设计、通过一系列针对性题目评估个体人格特质的心理问卷启发，我们认为这些题目可被视为一组结构完善的思维链（CoT）过程。通过融入这些过程，LLM可增强自身能力，从文本输入中做出更合理的人格推断。基于此，我们提出一种新型人格检测方法PsyCoT，它以多轮对话的方式模拟个体完成心理问卷的过程。具体而言，我们将一个LLM用作专攻文本分析的AI助手。我们提示该助手在每一轮中对单个题目进行评分，并利用历史评分结果得出最终的人格偏好结论。我们的实验表明，PsyCoT显著提升了GPT-3.5在人格检测中的性能和鲁棒性，与标准提示方法相比，在两个基准数据集上的平均F1分数分别提升了4.23/10.63分。我们的代码可在https://github.com/TaoYang225/PsyCoT 获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
