Scaling Synthetic Data Creation with 1,000,000,000 Personas
By Xin Chan, Xiaoyang Wang, Dian Yu, Haitao Mi, Dong Yu. arXiv.org, 2024
419 citations recorded by the source corpus.
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.
Subfields: Human-Computer Interaction
View Paper: https://www.semanticscholar.org/paper/203c7916b8fd7634f25f257e3a424a5b49d7ab5f
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Oyvind Tafjord
- Yizhong Wang
- Nouha Dziri
- Pradeep Dasigi
- Swaroop Mishra
- Ronan Le Bras
- Nathan Lambert
- Daniel Khashabi
Citations in the corpus
- Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models (2025, neutral): “Additionally, there has been considerable research facilitating the integration of LLMs into processes such as training data synthesis and quality data filtering (Wang et al., 2024; Lee et al., 2024; 2025b).”
- TÜLU 3: Pushing Frontiers in Open Language Model Post-Training (2024, positive): “Specifically, we condition on ∼ 250K personas from Persona Hub [Chan et al., 2024] to generate prompts targeting specific skills such as precise instruction following, math and coding.”
- LLM as a Broken Telephone: Iterative Generation Distorts Information (2025, unknown): “While prior work focuses on emergent problem-solving capabilities (Chan et al., 2024 mechanisms accelerate information distortion, particularly in scenarios where translation ambiguities compound through successive agent handoffs.”