AI RESEARCH
Using Zero-Shot LLM-Generated Survey Data for Geographically Explicit Population Synthesis
arXiv CS.AI
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ArXi:2605.27401v1 Announce Type: cross There is a growing interest in utilizing synthetic populations for a diverse range of applications. At the same time, we are witnessing a tremendous growth in artificial intelligence in all walks of life. This paper evaluates whether zero-shot large language model (LLM)-generated health survey data can serve as inputs to a conventional iterative proportional fitting (IPF) workflow for geographically explicit population synthesis. Using the 2023 Behavioral Risk Factor Surveillance System (BRFSS), we generate synthetic survey records for the U. S.