title: Scaling Synthetic Data Creation with 1,000,000,000 Personas
summary: We propose a novel persona-driven data synthesis methodology that leverages
  various perspectives within a large language model (LLM) to create diverse
  synthetic data. To fully exploit this methodology at scale, we introduce
  Persona Hub -- a collection of 1 billion diverse personas automatically curated
  from web data. These 1 billion personas (~13% of the world's total population),
  acting as distributed carriers of world knowledge, can tap into almost every
  perspective encapsulated within the LLM, thereby facilitating the creation of
  diverse synthetic data at scale for various scenarios. By showcasing Persona
  Hub's use cases in synthesizing high-quality mathematical and logical reasoning
  problems, instructions (i.e., user prompts), knowledge-rich texts, game NPCs
  and tools (functions) at scale, we demonstrate persona-driven data synthesis is
  versatile, scalable, flexible, and easy to use, potentially driving a paradigm
  shift in synthetic data creation and applications in practice, which may have a
  profound impact on LLM research and development.
paper_authors._id: 6681fd246bc332049a92efcb
paper_authors.name: Xin Chan
paper_authors._id: 6681fd246bc332049a92efcc
paper_authors.name: Xiaoyang Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-01T07:46:55.022Z
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: 6681fd246bc332049a92efcd
paper_authors.name: Dian Yu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-02T07:39:18.355Z
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: 6681fd246bc332049a92efce
paper_authors.name: Haitao Mi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-01T07:49:08.422Z
paper_authors._id: 65147a1426fbd558dbd08f1b
paper_authors.avatarUrl: /avatars/86574ee2d5c22e940be1c4e50be88675.svg
paper_authors.fullname: Haitao Mi
paper_authors.type: user
paper_authors.user: haitaominlp
paper_authors._id: 6681fd246bc332049a92efcf
paper_authors.name: Dong Yu
paper_title: Scaling Synthetic Data Creation with 1,000,000,000 Personas
paper_summary: We propose a novel persona-driven data synthesis methodology that leverages
  various perspectives within a large language model (LLM) to create diverse
  synthetic data. To fully exploit this methodology at scale, we introduce
  Persona Hub -- a collection of 1 billion diverse personas automatically curated
  from web data. These 1 billion personas (~13% of the world's total population),
  acting as distributed carriers of world knowledge, can tap into almost every
  perspective encapsulated within the LLM, thereby facilitating the creation of
  diverse synthetic data at scale for various scenarios. By showcasing Persona
  Hub's use cases in synthesizing high-quality mathematical and logical reasoning
  problems, instructions (i.e., user prompts), knowledge-rich texts, game NPCs
  and tools (functions) at scale, we demonstrate persona-driven data synthesis is
  versatile, scalable, flexible, and easy to use, potentially driving a paradigm
  shift in synthetic data creation and applications in practice, which may have a
  profound impact on LLM research and development.
paper_ai_keywords: large language model (LLM) persona-driven data synthesis Persona Hub distributed carriers of world knowledge synthetic data high-quality mathematical and logical reasoning problems instructions (i.e., user prompts) knowledge-rich texts game NPCs tools (functions)