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.

翻译：大语言模型（LLMs）领域已取得显著进展，其知识存储能力正接近人类水平。此外，提示学习和强化学习等先进技术正被用于解决与大语言模型相关的伦理问题和幻觉问题，使其更接近与人类价值观对齐。这一现状自然引发了疑问：具备类人能力的大语言模型是否也拥有人类似的人格？本文旨在探究使用迈尔斯-布里格斯类型指标（MBTI）这一广泛使用的人类人格评估工具作为大语言模型评估指标的可行性。具体而言，我们将通过大量实验探索：1）不同大语言模型的人格类型；2）通过提示工程改变人格类型的可能性；3）训练数据集如何影响模型的人格。尽管MBTI并非严谨的评估工具，但它仍能反映大语言模型与人类人格之间的相似性。在实践中，MBTI有潜力作为粗略的指标使用。我们的代码可在https://github.com/HarderThenHarder/transformers_tasks/tree/main/LLM/llms_mbti获取。