In this study, we investigate the reliability of Large Language Models (LLMs) in professing human-like personality traits through responses to personality questionnaires. Our goal is to evaluate the consistency between LLMs' professed personality inclinations and their actual "behavior", examining the extent to which these models can emulate human-like personality patterns. Through a comprehensive analysis of LLM outputs against established human benchmarks, we seek to understand the cognition-action divergence in LLMs and propose hypotheses for the observed results based on psychological theories and metrics.

翻译：在本研究中，我们通过大语言模型对个性问卷的回答，探究其在表达类人个性特质方面的可靠性。我们的目标是评估LLMs所声称的个性倾向与其实际"行为"之间的一致性，考察这些模型能在多大程度上模拟类人的个性模式。通过对LLMs输出与已建立的人类基准数据的全面分析，我们试图理解LLMs的认知-行为差异，并基于心理学理论和度量标准对观察到的结果提出假设。