Vox Populi, Vox AI? Using Large Language Models to Estimate German Vote Choice

Abstract

“Synthetic samples” generated by large language models (LLMs) have been argued to complement or replace traditional surveys, assuming their training data is grounded in human-generated data that potentially reflects attitudes and behaviors prevalent in the population. Initial US-based studies that have prompted LLMs to mimic survey respondents found that the responses match survey data. However, the relationship between the respective target population and LLM training data might affect the generalizability of such findings. In this paper, we critically evaluate the use of LLMs for public opinion research in a different context, by investigating whether LLMs can estimate vote choice in Germany. We generate a synthetic sample matching the 2017 German Longitudinal Election Study respondents and ask the LLM GPT-3.5 to predict each respondent’s vote choice. Comparing these predictions to the survey-based estimates on the aggregate and subgroup levels, we find that GPT-3.5 exhibits a bias towards the Green and Left parties. While the LLM predictions capture the tendencies of “typical” voters, they miss more complex factors of vote choice. By examining the LLM-based prediction of voting behavior in a non-English speaking context, our study contributes to research on the extent to which LLMs can be leveraged for studying public opinion. The findings point to disparities in opinion representation in LLMs and underscore the limitations in applying them for public opinion estimation.