title: OpenCharacter: Training Customizable Role-Playing LLMs with Large-Scale
    Synthetic Personas
summary: 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.
paper_authors._id: 67984dfa6e816a0edaa8d7b1
paper_authors.name: Xiaoyang Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-28T19:41:12.925Z
paper_authors._id: 657cd228138b7e391444a65d
paper_authors.avatarUrl: /avatars/c7c984ae483144fab627aa2c54d91d0f.svg
paper_authors.fullname: Xiaoyang Wang
paper_authors.type: user
paper_authors.user: xywang1
paper_authors._id: 67984dfa6e816a0edaa8d7b2
paper_authors.name: Hongming Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-28T19:41:33.772Z
paper_authors._id: 64ed478bec06efeb03034933
paper_authors.avatarUrl: /avatars/cd7dc3165831e90cb36d39d41c3c8157.svg
paper_authors.fullname: Hongming Zhang
paper_authors.type: user
paper_authors.user: Hongming98
paper_authors._id: 67984dfa6e816a0edaa8d7b3
paper_authors.name: Tao Ge
paper_authors._id: 67984dfa6e816a0edaa8d7b4
paper_authors.name: Wenhao Yu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-28T19:42:23.986Z
paper_authors._id: 5feab3a28a3201f8e554c969
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1660795228685-5feab3a28a3201f8e554c969.png
paper_authors.fullname: Wenhao Yu
paper_authors.type: user
paper_authors.user: wyu1
paper_authors._id: 67984dfa6e816a0edaa8d7b5
paper_authors.name: Dian Yu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-12T20:46:21.556Z
paper_authors._id: 62d58fd53bf5e059f7cc3245
paper_authors.avatarUrl: /avatars/7a4f3ee4a37245f67efd26749d66a706.svg
paper_authors.fullname: Dian Yu
paper_authors.type: user
paper_authors.user: yudian
paper_authors._id: 67984dfa6e816a0edaa8d7b6
paper_authors.name: Dong Yu
paper_title: OpenCharacter: Training Customizable Role-Playing LLMs with Large-Scale
    Synthetic Personas
paper_summary: 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.
paper_ai_keywords: customizable role-playing character generalization large-scale data synthesis character profiles personas Persona Hub response rewriting response generation character-aligned instructional responses supervised fine-tuning (SFT) LLaMA-3 8B model role-playing dialogue GPT-4o models