  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.
