Matching-LLM: Historically Anchored Synthetic Prediction of Public Opinion and Elections

Abstract

Large language models (LLMs) offer new opportunities for predicting public opinion and electoral behavior, but their synthetic responses may not fully reflect human attitudes and heterogeneity. This study introduces Matching-LLM, a weighted framework that combines LLM-generated responses with historical human behavioral data. Using the World Values Survey, American National Election Studies, and German election survey data, we evaluate its performance in reconstructing survey responses and predicting election outcomes. Results show that Matching-LLM generally improves accuracy relative to LLM-only, including 11 of 12 comparisons on survey questions excluded from calibration, although it does not consistently outperform History-only. In the 2024 U.S. presidential election, Matching-LLM predicts 310 Republican electoral votes, compared with the actual 312, outperforming both single-source benchmarks in electoral-vote accuracy. A supplementary German application extends the framework to a multiparty setting. Overall, the findings highlight the potential of historical data to improve LLM-based predictions while emphasizing the importance of task-specific validation.