On the Decision-Making Abilities in Role-Playing using Large Language Models
<|reference_start|>On the Decision-Making Abilities in Role-Playing using Large Language Models: Large language models (LLMs) are now increasingly utilized for role-playing tasks, especially in impersonating domain-specific experts, primarily through role-playing prompts. When interacting in real-world scenarios, the decision-making abilities of a role significantly shape its behavioral patterns. In this paper, we concentrate on evaluating the decision-making abilities of LLMs post role-playing thereby validating the efficacy of role-playing. Our goal is to provide metrics and guidance for enhancing the decision-making abilities of LLMs in role-playing tasks. Specifically, we first use LLMs to generate virtual role descriptions corresponding to the 16 personality types of Myers-Briggs Type Indicator (abbreviated as MBTI) representing a segmentation of the population. Then we design specific quantitative operations to evaluate the decision-making abilities of LLMs post role-playing from four aspects: adaptability, exploration$\&$exploitation trade-off ability, reasoning ability, and safety. Finally, we analyze the association between the performance of decision-making and the corresponding MBTI types through GPT-4. Extensive experiments demonstrate stable differences in the four aspects of decision-making abilities across distinct roles, signifying a robust correlation between decision-making abilities and the roles emulated by LLMs. These results underscore that LLMs can effectively impersonate varied roles while embodying their genuine sociological characteristics.<|reference_end|>
@article{shen2024on,
    title={On the Decision-Making Abilities in Role-Playing using Large Language
  Models},
    author={Chenglei Shen and Guofu Xie and Xiao Zhang and Jun Xu},
    journal={arXiv preprint arXiv:2402.18807},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.18807},
    primaryClass={cs.CL cs.AI}
}