Data-Driven Personas for Survey Simulation: Insights into Simulation Alignment Across Data-Access Regimes

Excerpt

arXiv:2610.05828v2 Announce Type: new Abstract: Large language models (LLMs) offer new opportunities for public opinion research by enabling early prediction of survey responses, potentially reducing the cost and time of traditional surveys. However, many existing steering approaches rely on target-domain human data for fine-tuning or prompting that is costly to collect and raises privacy concerns. In this paper, we study demographic group-level survey simulation, where personas induced from het