Scaling Synthetic Data Creation with 1,000,000,000 Personas
Authors: name: Xin Chan, name: Xiaoyang Wang, name: Dian Yu, name: Haitao Mi, name: Dong Yu

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.