Related papers: Capturing Minds, Not Just Words: Enhancing Role-Playing Language Models with Personality-Indicative Data

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

URL: http://arxiv.org/abs/2406.18921v2
Date: Sat, 29 Jun 2024 05:58:28 GMT
Title: Capturing Minds, Not Just Words: Enhancing Role-Playing Language Models with Personality-Indicative Data
Authors: Yiting Ran, Xintao Wang, Rui Xu, Xinfeng Yuan, Jiaqing Liang, Yanghua Xiao, Deqing Yang,
Abstract summary: We propose to enhance role-playing language models (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.
Score: 58.92110996840019
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
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. Code and data are available at \href{https://github.com/alienet1109/RolePersonality}{this URL}.

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