DeepPersona: Generative Engine for Scaling Deep Synthetic Personas

Simulating human profiles by instilling personas into large language models (LLMs) is rapidly transforming research in personalization, social simulation, and human-AI alignment. However, most existing synthetic personas remain shallow and simplistic, capturing minimal attributes and failing to reflect the rich complexity and diversity of real human identities. We introduce DeepPersona, a scalable generative engine for synthesizing narrative-complete synthetic personas through a two-stage, taxonomy-guided method. First, we algorithmically construct the largest-ever human-attribute taxonomy, comprising over hundreds of hierarchically-organized attributes, by systematically mining thousands of real user-ChatGPT conversations. Second, we progressively sample attributes from this taxonomy, conditionally generating coherent and realistic personas, averaging hundreds of structured attributes and roughly 1 MB of narrative text, two orders of magnitude deeper than prior works. Intrinsic evaluations confirm significant improvements in attribute diversity (32\% higher coverage) and profile uniqueness (44\% greater) compared to state-of-the-art baselines. Extrinsically, our personas enhance GPT-4.1-mini’s personalized Q&A accuracy by 11.6\% average on ten metrics, and substantially narrow (by 32\%) the gap between simulated LLM ``citizens'' and authentic human responses in social surveys. DeepPersona thus provides a rigorous, scalable, and privacy-free platform for high-fidelity human simulation and personalized AI research.