The paper introduces a novel persona-driven data synthesis methodology that leverages large language models (LLMs) to create diverse synthetic data. To fully exploit this methodology at scale, the authors introduce Persona Hub, a collection of 1 billion diverse personas automatically curated from web data. These personas, acting as distributed carriers of world knowledge, can tap into almost every perspective encapsulated within the LLM, facilitating the creation of diverse synthetic data for various scenarios. The authors demonstrate the versatility, scalability, and flexibility of persona-driven data synthesis through use cases such as synthesizing high-quality mathematical and logical reasoning problems, instructions (user prompts), knowledge-rich texts, game NPCs, and tools (functions). They showcase that persona-driven data synthesis can be applied to almost any popular LLM and is easy to use. The paper also discusses the potential impact and ethical concerns of Persona Hub, including the risk of accessing the full memory of LLMs, which could lead to the dumping and replication of LLMs' knowledge, intelligence, and capabilities. The authors emphasize the need for ethical and responsible application of this technology to avoid misuse and ensure its positive impact on LLM research and development.The paper introduces a novel persona-driven data synthesis methodology that leverages large language models (LLMs) to create diverse synthetic data. To fully exploit this methodology at scale, the authors introduce Persona Hub, a collection of 1 billion diverse personas automatically curated from web data. These personas, acting as distributed carriers of world knowledge, can tap into almost every perspective encapsulated within the LLM, facilitating the creation of diverse synthetic data for various scenarios. The authors demonstrate the versatility, scalability, and flexibility of persona-driven data synthesis through use cases such as synthesizing high-quality mathematical and logical reasoning problems, instructions (user prompts), knowledge-rich texts, game NPCs, and tools (functions). They showcase that persona-driven data synthesis can be applied to almost any popular LLM and is easy to use. The paper also discusses the potential impact and ethical concerns of Persona Hub, including the risk of accessing the full memory of LLMs, which could lead to the dumping and replication of LLMs' knowledge, intelligence, and capabilities. The authors emphasize the need for ethical and responsible application of this technology to avoid misuse and ensure its positive impact on LLM research and development.