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.
To facilitate research in persona-driven data synthesis, we are initially releasing following synthetic data samples we created with various personas, including:
- 50,000 math problems
- 50,000 logical reasoning problems
- 50,000 instructions
- 10,000 knowledge-rich texts
- 10,000 game NPCs
- 5,000 tools (functions)
We also release a subset of our PERSONA HUB, including:
- 200,000 personas
One can also quickly preview these data at huggingface.
If you find our work useful, please consider citing our paper:
@article{tencent2024personahub,
title={Scaling Synthetic Data Creation with 1,000,000,000 Personas},
author={{Tencent AI Lab Seattle}},
journal={arXiv preprint arXiv:2406.20094},
year={2024}
}
Please email [email protected] or [email protected]
PERSONA HUB can facilitate synthetic data creation at a billion-scale to simulate diverse inputs (i.e., use cases) from a wide variety of real-world users. If this data is used as input to query a target LLM to obtain its outputs at scale, there is a high risk that the LLM's knowledge, intelligence and capabilities will be dumped and easily replicated, thereby challenging the leading position of the most powerful LLMs. It is crucial to avoid misuse and ensure ethical and responsible application to prevent privacy violations and other ethical concerns.
The released data is all generated by public available models (GPT-4, Llama-3 and Qwen), and is intended for research purposes only. It may contain inaccuracies, unsafe content, or biases, for which we cannot be held responsible. Please evaluate its accuracy and suitability before use. Tencent and its licensors provide the data AS-IS, without warranty of any kind, express or implied. The views and opinions expressed in the data do not necessarily reflect those of Tencent.