Do LLMs Possess a Personality? Making the MBTI Test an Amazing Evaluation for Large Language Models
This work addresses the problem of evaluating personality traits in LLMs for researchers and developers, but it is incremental as it applies an existing human assessment tool to a new context without major methodological breakthroughs.
The paper investigates whether large language models (LLMs) have human-like personalities by using the Myers-Briggs Type Indicator (MBTI) as an evaluation metric, exploring personality types, the impact of prompt engineering, and training dataset effects, with results suggesting MBTI can serve as a rough indicator of similarity to human personality.
The field of large language models (LLMs) has made significant progress, and their knowledge storage capacity is approaching that of human beings. Furthermore, advanced techniques, such as prompt learning and reinforcement learning, are being employed to address ethical concerns and hallucination problems associated with LLMs, bringing them closer to aligning with human values. This situation naturally raises the question of whether LLMs with human-like abilities possess a human-like personality? In this paper, we aim to investigate the feasibility of using the Myers-Briggs Type Indicator (MBTI), a widespread human personality assessment tool, as an evaluation metric for LLMs. Specifically, extensive experiments will be conducted to explore: 1) the personality types of different LLMs, 2) the possibility of changing the personality types by prompt engineering, and 3) How does the training dataset affect the model's personality. Although the MBTI is not a rigorous assessment, it can still reflect the similarity between LLMs and human personality. In practice, the MBTI has the potential to serve as a rough indicator. Our codes are available at https://github.com/HarderThenHarder/transformers_tasks/tree/main/LLM/llms_mbti.