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
this https URL

使用大型语言模型进行个性检测，通过模拟心理测验的方式，引入连续思路过程，并借助历史评分结果来得出确切的个性偏好，证明 PsyCoT 方法在提高 GPT-3.5 的性能和稳健性方面具有显著作用。