OpenCharacter: Training Customizable Role-Playing LLMs with Large-Scale
  Synthetic Personas
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.
67984dfa6e816a0edaa8d7b1
Xiaoyang Wang
admin_assigned
2025-01-28T19:41:12.925Z
657cd228138b7e391444a65d
/avatars/c7c984ae483144fab627aa2c54d91d0f.svg
Xiaoyang Wang
user
xywang1
67984dfa6e816a0edaa8d7b2
Hongming Zhang
admin_assigned
2025-01-28T19:41:33.772Z
64ed478bec06efeb03034933
/avatars/cd7dc3165831e90cb36d39d41c3c8157.svg
Hongming Zhang
user
Hongming98
67984dfa6e816a0edaa8d7b3
Tao Ge
67984dfa6e816a0edaa8d7b4
Wenhao Yu
admin_assigned
2025-01-28T19:42:23.986Z
5feab3a28a3201f8e554c969
https://cdn-avatars.huggingface.co/v1/production/uploads/1660795228685-5feab3a28a3201f8e554c969.png
Wenhao Yu
user
wyu1
67984dfa6e816a0edaa8d7b5
Dian Yu
claimed_verified
2025-03-12T20:46:21.556Z
62d58fd53bf5e059f7cc3245
/avatars/7a4f3ee4a37245f67efd26749d66a706.svg
Dian Yu
user
yudian
67984dfa6e816a0edaa8d7b6
Dong Yu
OpenCharacter: Training Customizable Role-Playing LLMs with Large-Scale
  Synthetic Personas
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.
A large-scale data synthesis approach equips LLMs with character generalization capabilities through supervised fine-tuning, achieving performance comparable to GPT-4 on role-playing dialogue.
large language models customizable role-playing character generalization Persona Hub response rewriting response generation supervised fine-tuning SFT LLaMA-3 8B GPT-4