Capturing Minds, Not Just Words: Enhancing Role-Playing Language Models with Personality-Indicative Data (2024)

Abstract: 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.

## introduction with the rise of large language models (llms), role-playing agents (rpas) have emerged as a widely focused field of application, which attracts significant research interest as well @xcite . based on llms, rpas simulate the behavior and speech patterns of specific characters @xcite . increasing efforts have been made to build specialized llms for rpas, i.e., role-playing language models (rplms) @xcite , typically via constructing role-playing datasets. these datasets aim to capture the key elements of role-playing and faithfully recreate character traits. while existing rplms well replicate the knowledge and tone of the intended characters, they struggle to capture their minds, in tasks such as personality assessment @xcite and decision simulation @xcite . this is partly because existing role-playing datasets focus on character knowledge and tones @xcite . however, capturing characters' minds is crucial for developing authentic rpas. in this paper, we propose to develop rplms via personality-indicative data. specifically, we collect these data through questions from psychological scales. these scale questions are designed to quickly capture broad aspects of personality traits in individuals. hence, we leverage advanced rpas for the distillation of knowledge from them. then, we apply these data to develop rplms that better capture the minds of the intended characters. specifically, we construct a dataset roleper-sonality based on questions from 14 different psychological scales, including both singleround and multi-round data, inspired by incharacter @xcite . following the dataset generation, we apply a filtering process using humanannotated personality labels for the selected characters. we apply rolepersonality to fine-tune rplms and evaluate them from three aspects, including personality fidelity @xcite , motivation recognition @xcite and general role-playing benchmarks @xcite . the results demonstrate that rplms fine-tuned with our dataset show improved capabilities in both personality-related and general evaluations. the main contributions of this paper are summarized as threefold: ## conclusion this paper demonstrates that personality-indicative data helps capture complex character mindsets, thus significantly enhancing the performance of role-playing agents. by constructing roleper-sonality that captures character personalities, we address the limitations of traditional datasets that focus primarily on character knowledge and linguistic habits. models fine-tuned on our comprehensive dataset show substantial improvements in role-playing capabilities. this advancement paves the way for constructing role-playing models that can effectively simulate complex character behaviors, leading to more immersive user experiences.