# Scaling Synthetic Data Creation with 1,000,000,000 Personas

Source: https://arxiv.org/abs/2406.20094
Kiwi News: https://news.kiwistand.com/stories/Scaling-Synthetic-Data-Creation-with-1000000000-Personas?index=0x66855ca4563c613a71ad97664fca2b6affdefba938773b3e9c1fba738e0ad0690fc25e07
Submitted by misha on 2024-07-03 UTC, 2 upvotes, 0 comments

## Summary

Tao Ge and colleagues propose a persona-driven data synthesis methodology that uses various perspectives within a large language model to create diverse synthetic data. They introduce Persona Hub, a collection of 1 billion personas automatically curated from web data, which they describe as about 13% of the world's population. The authors say these personas can tap nearly every perspective encapsulated within the LLM, and they demonstrate use cases including math and logic problems, user prompts, knowledge-rich texts, game NPCs and tools. They claim the approach is versatile, scalable, flexible and easy to use.

## Article

Scaling Synthetic Data Creation with 1,000,000,000 Personas

Authors: Tao Ge, Xin Chan, Xiaoyang Wang, Dian Yu, Haitao Mi, 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.