Who is ChatGPT? Benchmarking LLMs' Psychological Portrayal Using PsychoBench
This addresses the need for standardized psychological assessment of LLMs for researchers and developers, though it is incremental as it builds on existing evaluation methods.
The paper tackles the problem of evaluating psychological aspects in Large Language Models (LLMs) by proposing PsychoBench, a framework with thirteen clinical psychology scales, and finds that models like GPT-4 show more human-like traits while jailbreaking reveals intrinsic natures.
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 text-davinci-003, gpt-3.5-turbo, 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.