Character is Destiny: Can Role-Playing Language Agents Make Persona-Driven Decisions?

• URL: http://arxiv.org/abs/2404.12138v2
• Date: Mon, 18 Nov 2024 11:29:47 GMT
• Title: Character is Destiny: Can Role-Playing Language Agents Make Persona-Driven Decisions?
• Authors: Rui Xu, Xintao Wang, Jiangjie Chen, Siyu Yuan, Xinfeng Yuan, Jiaqing Liang, Zulong Chen, Xiaoqing Dong, Yanghua Xiao,
• Abstract summary: We benchmark the ability of Large Language Models (LLMs) in persona-driven decision-making. We investigate whether LLMs can predict characters' decisions provided by the preceding stories in high-quality novels. The results demonstrate that state-of-the-art LLMs exhibit promising capabilities in this task, yet substantial room for improvement remains.
• Score: 59.0123596591807
• License:
• Abstract: 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.

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