Role-playing agents (RPA) have been a popular application area for large language models (LLMs), attracting significant interest from both industry and academia.While existing RPAs well portray the characters' knowledge and tones, they face challenges in capturing their minds, especially for small role-playing language models (RPLMs). In this paper, we propose to enhance RPLMs via personality-indicative data. Specifically, we leverage questions from psychological scales and distill advanced RPAs to generate dialogues that grasp the minds of characters. Experimental results validate that RPLMs trained with our dataset exhibit advanced role-playing capabilities for both general and personality-related evaluations. Code and data are available at \href{https://github.com/alienet1109/RolePersonality}{this URL}.

翻译：角色扮演智能体（RPA）已成为大型语言模型（LLM）的一个重要应用领域，引起了工业界和学术界的广泛关注。尽管现有的RPA能够较好地刻画角色的知识与语调，但在捕捉其心智方面仍面临挑战，对于小型角色扮演语言模型（RPLM）而言尤为如此。本文提出通过人格指示性数据来增强RPLM。具体而言，我们利用心理学量表中的问题，并通过蒸馏高级RPA来生成能够把握角色心智的对话。实验结果验证，使用我们构建的数据集训练的RPLM，在通用评估和人格相关评估中均展现出更优的角色扮演能力。代码与数据可通过 \href{https://github.com/alienet1109/RolePersonality}{此链接} 获取。