Persona Generators: Generating Diverse Synthetic Personas at Scale
Davide Paglieri, Logan Cross, William A. Cunningham, Joel Z. Leibo, Alexander Sasha Vezhnevets

TL;DR
This paper introduces Persona Generators, a novel method using large language models and iterative optimization to produce diverse synthetic personas that better cover the range of possible human behaviors, especially rare traits.
Contribution
It presents a new approach for generating diverse synthetic populations with high coverage using iterative refinement and LLM-based mutation, improving over existing methods.
Findings
Generated populations outperform baselines on six diversity metrics.
Evolved generators produce rare trait combinations more effectively.
Method scales to arbitrary contexts and enhances diversity coverage.
Abstract
Evaluating AI systems that interact with humans requires understanding their behavior across diverse user populations, but collecting representative human data is often expensive or infeasible, particularly for novel technologies or hypothetical future scenarios. Recent work in Generative Agent-Based Modeling has shown that large language models can simulate human-like synthetic personas with high fidelity, accurately reproducing the beliefs and behaviors of specific individuals. However, most approaches require detailed data about target populations and often prioritize density matching (replicating what is most probable) rather than support coverage (spanning what is possible), leaving long-tail behaviors underexplored. We introduce Persona Generators, functions that can produce diverse synthetic populations tailored to arbitrary contexts. We apply an iterative improvement loop based…
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Taxonomy
TopicsPersona Design and Applications · Social Robot Interaction and HRI · Ethics and Social Impacts of AI
