Character is Destiny: Can Persona-assigned Language Models Make Personal Choices?

Can Large Language Models (LLMs) simulate humans in making important decisions?Recent research has unveiled the potential of using LLMs to develop role-playing language agents (RPLAs), mimicking mainly the knowledge and tones of various characters.However, imitative decision-making necessitates 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 by the preceding stories in high-quality novels.Leveraging character analyses written by literary experts, we construct a dataset LIFECHOICE comprising 2,512 characters' decision points from 470 books.Then, we conduct comprehensive experiments on LIFECHOICE, with various LLMs and RPLA methodologies.The results demonstrate that state-of-the-art LLMs exhibit promising capabilities in this task, yet substantial room for improvement remains.Hence, we further propose the CHARMAP method, which adopts persona-based memory retrieval and significantly advances RPLAs on this task.Resources are available at https://github. com/airaer1998/LifeChoice.