Large Language Models (LLMs) have recently showcased their remarkable
capacities, not only in natural language processing tasks but also across
diverse domains such as clinical medicine, legal consultation, and education.
LLMs become more than mere applications, evolving into assistants capable of
addressing diverse user requests. This narrows the distinction between human
beings and artificial intelligence agents, raising intriguing questions
regarding the potential manifestation of personalities, temperaments, and
emotions within LLMs. In this paper, we propose a framework, PsychoBench, for
evaluating diverse psychological aspects of LLMs. Comprising thirteen scales
commonly used in clinical psychology, PsychoBench further classifies these
scales into four distinct categories: personality traits, interpersonal
relationships, motivational tests, and emotional abilities. Our study examines
five popular models, namely \texttt{text-davinci-003}, ChatGPT, GPT-4,
LLaMA-2-7b, and LLaMA-2-13b. Additionally, we employ a jailbreak approach to
bypass the safety alignment protocols and test the intrinsic natures of LLMs.
We have made PsychoBench openly accessible via
https://github.com/CUHK-ARISE/PsychoBench.

该研究提出了一个评估 LLMs 不同心理方面的框架 PsychoBench，其中包括了四个明确分类的部分：人格特征、人际关系、动机测试和情绪能力。研究使用了五个常用模型进行测试，并采用了越狱方法绕过安全对齐协议，测试了 LLMs 的内在特性。