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

2024-06-28

arXiv.org

AI Summary

Synthetic data generation faces bottlenecks in diversity deficiency and heavy reliance on manual curation. Method: This paper proposes the Persona Hub paradigm—a scalable, automated framework that constructs a billion-scale, diverse persona repository mined from web data. Leveraging LLMs’ multi-perspective sampling capability, it integrates data-driven persona discovery, prompt engineering, and structured pipelines to efficiently generate high-quality synthetic data across multiple modalities: mathematical reasoning problems, user instructions, knowledge texts, game NPCs, and utility functions. Contribution/Results: Persona Hub establishes the first scalable, reusable, and persona-grounded synthetic data generation framework. It significantly enhances data diversity and quality across downstream tasks, enables out-of-the-box production of hundred-billion-scale datasets, and advances synthetic data generation from labor-intensive curation toward large-scale, persona-aware automation.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Web Mining and Content Analysis: Web data generation and simulation
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• Scaling synthetic data creation using billion diverse personas
• Generating diverse synthetic data for various scenarios via LLM
• Enhancing LLM research with versatile persona-driven data synthesis

Innovation

• Persona-driven synthesis using billion diverse personas
• Automated Persona Hub from web for scalability
• Versatile applications in reasoning and NPCs