Characterizing the ability of LLMs to recapitulate Americans' distributional responses to public opinion polling questions across political issues
Traditional survey-based political issue polling is becoming less tractable due to increasing costs and risk of bias associated with growing non-response rates and declining coverage of key demographic groups. With researchers and pollsters seeking alternatives, Large Language Models have drawn attention for their potential to augment human population studies in polling contexts. We propose and implement a new framework for anticipating human responses on multiple-choice political issue polling questions by directly prompting an LLM to predict a distribution of responses. By comparison to a large and high quality issue poll of the US population, the Cooperative Election Study, we evaluate...
| Authors | Eric Gong, Nathan E. Sanders, Bruce Schneier |
|---|---|
| Primary category | cs.CY |
| Published | 6 March 2026 |
| Last updated at source | 6 March 2026 |
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