@inproceedings{xu-etal-2025-character,
title = "Character is Destiny: Can Persona-assigned Language Models Make Personal Choices?",
author = "Xu, Rui and
Wang, Xintao and
Chen, Jiangjie and
Yuan, Siyu and
Yuan, Xinfeng and
Liang, Jiaqing and
Chen, Zulong and
Xiaoqingdong and
Xiao, Yanghua",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.813/",
doi = "10.18653/v1/2025.findings-emnlp.813",
pages = "15038--15059",
ISBN = "979-8-89176-335-7",
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 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."
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<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 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.</abstract>
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%0 Conference Proceedings
%T Character is Destiny: Can Persona-assigned Language Models Make Personal Choices?
%A Xu, Rui
%A Wang, Xintao
%A Chen, Jiangjie
%A Yuan, Siyu
%A Yuan, Xinfeng
%A Liang, Jiaqing
%A Chen, Zulong
%A Xiao, Yanghua
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%A Xiaoqingdong
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F xu-etal-2025-character
%X 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.
%R 10.18653/v1/2025.findings-emnlp.813
%U https://aclanthology.org/2025.findings-emnlp.813/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.813
%P 15038-15059
Markdown (Informal)
[Character is Destiny: Can Persona-assigned Language Models Make Personal Choices?](https://aclanthology.org/2025.findings-emnlp.813/) (Xu et al., Findings 2025)
ACL
- Rui Xu, Xintao Wang, Jiangjie Chen, Siyu Yuan, Xinfeng Yuan, Jiaqing Liang, Zulong Chen, Xiaoqingdong, and Yanghua Xiao. 2025. Character is Destiny: Can Persona-assigned Language Models Make Personal Choices?. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 15038–15059, Suzhou, China. Association for Computational Linguistics.