RoleCraft-GLM: Advancing Personalized Role-Playing in Large Language Models

Anonymous

Abstract: The development of large language models(LLMs) has initiated a new chapter in complex tasks such as role-playing, enhancing user interaction experiences by enabling models to imitate various characters.However, LLMs are somewhat lacking in their ability to portray lesser-known characters, especially in aspects of dialogue delivery and scriptwriting skills. To this end, we aim to swiftly acquire essential language skills for character development, greatly enhancing role-playing comfort. In this work, we present RoleCraft, an innovative framework designed to enrich personalized role-playing experiences. Central to this framework is RoleInstruct, a distinctive dataset featuring emotional annotations, transitioning from traditional celebrity-focused roles to more authentic, daily non-celebrity roles,each accompanied by carefully crafted character descriptions. We combined RoleInstruct with open-source instructions from the general domain, employing a hybrid instruction tuning strategy to create RoleCraft-GLM. Experiments in role-playing demonstrate that our model excels in generating dialogue that accurately reflects character traits and emotions, outperforming most mainstream LLMs, including GPT-4.

Paper Type: long

Research Area: Dialogue and Interactive Systems

Contribution Types: Model analysis & interpretability, Data analysis, Theory

Languages Studied: English, Chinese