[2603.20229] Characterizing the ability of LLMs to recapitulate Americans' distributional responses to public opinion polling questions across political issues

Computer Science > Computers and Society arXiv:2603.20229 (cs) [Submitted on 6 Mar 2026] Title:Characterizing the ability of LLMs to recapitulate Americans' distributional responses to public opinion polling questions across political issues Authors:Eric Gong, Nathan E. Sanders, Bruce Schneier View a PDF of the paper titled Characterizing the ability of LLMs to recapitulate Americans' distributional responses to public opinion polling questions across political issues, by Eric Gong and 2 other authors View PDF HTML (experimental) Abstract: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 how the accuracy of this framework varies across a range of demographics and questions on a variety of topics, as well as how this framework compares to previously proposed frameworks where LLMs are repeatedly queried to simulate individual respondents. ...