  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.
