Reasoning Does Not Necessarily Improve Role-Playing Ability

• URL: http://arxiv.org/abs/2502.16940v1
• Date: Mon, 24 Feb 2025 08:08:41 GMT
• Title: Reasoning Does Not Necessarily Improve Role-Playing Ability
• Authors: Xiachong Feng, Longxu Dou, Lingpeng Kong,
• Abstract summary: The application of role-playing large language models (LLMs) is rapidly expanding in both academic and commercial domains.<n>We compare the effectiveness of direct zero-shot role-playing, role-playing with Chain-of-Thought (CoT), and role-playing using reasoning-optimized LLMs.<n>Our findings reveal that CoT may reduce role-playing performance, reasoning-optimized LLMs are unsuitable for role-playing, and Chinese role-playing performance surpasses English role-playing performance.
• Score: 46.441264660062195
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The application of role-playing large language models (LLMs) is rapidly expanding in both academic and commercial domains, driving an increasing demand for high-precision role-playing models. Simultaneously, the rapid advancement of reasoning techniques has continuously pushed the performance boundaries of LLMs. This intersection of practical role-playing demands and evolving reasoning capabilities raises an important research question: "Can reasoning techniques enhance the role-playing capabilities of LLMs?" To address this, we conduct a comprehensive study using 6 role-playing benchmarks, 24 LLMs, and 3 distinct role-playing strategies, comparing the effectiveness of direct zero-shot role-playing, role-playing with Chain-of-Thought (CoT), and role-playing using reasoning-optimized LLMs. Our findings reveal that CoT may reduce role-playing performance, reasoning-optimized LLMs are unsuitable for role-playing, reasoning ability disrupts the role-playing scaling law, large models still lack proficiency in advanced role-playing, and Chinese role-playing performance surpasses English role-playing performance. Furthermore, based on extensive experimental results, we propose two promising future research directions: Role-aware CoT for improving role-playing LLMs and Reinforcement Learning for role-playing LLMs, aiming to enhance the adaptability, consistency, and effectiveness of role-playing LLMs for both research and real-world applications.

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