OpenCharacter: Training Customizable Role-Playing LLMs with Large-Scale
  Synthetic Personas
Authors: name: Xiaoyang Wang, name: Hongming Zhang, name: Tao Ge, name: Wenhao Yu, name: Dian Yu, name: Dong Yu

Customizable role-playing in large language models (LLMs), also known as
character generalization, is gaining increasing attention for its versatility
and cost-efficiency in developing and deploying role-playing dialogue agents.
This study explores a large-scale data synthesis approach to equip LLMs with
character generalization capabilities. We begin by synthesizing large-scale
character profiles using personas from Persona Hub and then explore two
strategies: response rewriting and response generation, to create
character-aligned instructional responses. To validate the effectiveness of our
synthetic instruction tuning data for character generalization, we perform
supervised fine-tuning (SFT) using the LLaMA-3 8B model. Our best-performing
model strengthens the original LLaMA-3 8B Instruct model and achieves
performance comparable to GPT-4o models on role-playing dialogue. We release
our synthetic characters and instruction-tuning dialogues to support public
research.