Character is Destiny: Can Role-Playing Language Agents Make Persona-Driven Decisions?
This addresses the problem of simulating human-like decision-making in AI for applications like interactive storytelling or social simulations, but it is incremental as it builds on existing role-playing language agents.
The paper benchmarks large language models (LLMs) on persona-driven decision-making using a dataset of 1,462 character decisions from novels, finding that state-of-the-art LLMs show promising capabilities but have room for improvement, and proposes a method that increases accuracy by 5.03%.
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 1,462 characters' decision points from 388 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, achieving 5.03% increase in accuracy.