Character is Destiny: Can Large Language Models Simulate Persona-Driven Decisions in Role-Playing?
<|reference_start|>Character is Destiny: Can Large Language Models Simulate Persona-Driven Decisions in Role-Playing?: Can Large Language Models substitute humans in making important decisions? Recent research has unveiled the potential of LLMs to role-play assigned personas, mimicking their knowledge and linguistic habits. However, imitative decision-making requires a more nuanced understanding of personas. In this paper, we benchmark the ability of LLMs in persona-driven decision-making. Specifically, we investigate whether LLMs can predict characters' decisions provided with the preceding stories in high-quality novels. Leveraging character analyses written by literary experts, we construct a dataset LIFECHOICE comprising 1,401 character decision points from 395 books. Then, we conduct comprehensive experiments on LIFECHOICE, with various LLMs and methods for LLM role-playing. The results demonstrate that state-of-the-art LLMs exhibit promising capabilities in this task, yet there is substantial room for improvement. Hence, we further propose the CHARMAP method, which achieves a 6.01% increase in accuracy via persona-based memory retrieval. We will make our datasets and code publicly available.<|reference_end|>
@article{xu2024character,
    title={Character is Destiny: Can Large Language Models Simulate Persona-Driven
  Decisions in Role-Playing?},
    author={Rui Xu, Xintao Wang, Jiangjie Chen, Siyu Yuan, Xinfeng Yuan, Jiaqing
  Liang, Zulong Chen, Xiaoqing Dong, Yanghua Xiao},
    journal={arXiv preprint arXiv:2404.12138},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.12138},
    primaryClass={cs.AI}
}