TY - GEN
T1 - Acting Technique
T2 - 32nd International Conference on Neural Information Processing, ICONIP 2025
AU - Zhang, Baohua
AU - Huang, Yongyi
AU - Cui, Wen Yao
AU - Zhang, Huaping
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
PY - 2026
Y1 - 2026
N2 - The goal of role-playing is to ensure that LLMs generate responses that fully embody the specified character, adhering to the role’s background, speaking style, personality, and typical behaviors. Various methods aim to improve LLMs’ character simulation capabilities, such as leveraging specialized prompts and fine-tuning with character dialogues. However, no research has yet investigated the relationship between LLMs’ capabilities and their role-playing performance. Understanding this relationship could provide insights into selecting the most suitable base model for mimicking realistic characters and improving LLM performance from the outset of the task. In this paper, we investigate three main factors influencing role-playing performance: LLMs’ personalities and those of the target characters, LLMs’ familiarity with the character, and their conversational abilities. We conduct experiments with 11 models (including both prompt-based and fine-tuning methods) across 7 characters with varying personalities and backgrounds. The results indicate that both LLMs’ and characters’ personalities are primary factors affecting performance, while insufficient familiarity leads to poor outcomes. Importantly, we find no evidence that sharing the same personality with a character directly correlates with enhanced role-playing performance.
AB - The goal of role-playing is to ensure that LLMs generate responses that fully embody the specified character, adhering to the role’s background, speaking style, personality, and typical behaviors. Various methods aim to improve LLMs’ character simulation capabilities, such as leveraging specialized prompts and fine-tuning with character dialogues. However, no research has yet investigated the relationship between LLMs’ capabilities and their role-playing performance. Understanding this relationship could provide insights into selecting the most suitable base model for mimicking realistic characters and improving LLM performance from the outset of the task. In this paper, we investigate three main factors influencing role-playing performance: LLMs’ personalities and those of the target characters, LLMs’ familiarity with the character, and their conversational abilities. We conduct experiments with 11 models (including both prompt-based and fine-tuning methods) across 7 characters with varying personalities and backgrounds. The results indicate that both LLMs’ and characters’ personalities are primary factors affecting performance, while insufficient familiarity leads to poor outcomes. Importantly, we find no evidence that sharing the same personality with a character directly correlates with enhanced role-playing performance.
KW - Conversational ability
KW - Personality
KW - Role-playing
UR - https://www.scopus.com/pages/publications/105022052716
U2 - 10.1007/978-981-95-4384-7_12
DO - 10.1007/978-981-95-4384-7_12
M3 - Conference contribution
AN - SCOPUS:105022052716
SN - 9789819543830
T3 - Lecture Notes in Computer Science
SP - 158
EP - 171
BT - Neural Information Processing - 32nd International Conference, ICONIP 2025, Proceedings
A2 - Taniguchi, Tadahiro
A2 - Leung, Chi Sing Andrew
A2 - Kozuno, Tadashi
A2 - Yoshimoto, Junichiro
A2 - Mahmud, Mufti
A2 - Doborjeh, Maryam
A2 - Doya, Kenji
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 20 November 2025 through 24 November 2025
ER -