A Large-scale Empirical Study on Large Language Models for Election Prediction

Can Large Language Models (LLMs) accurately predict election outcomes? While LLMs have demonstrated impressive performance in healthcare, legal analysis, and creative applications, their capabilities in election forecasting remain uncertain. Notably, election prediction poses unique challenges: limited voter-level data, evolving political contexts, and the complexity of modeling human behavior. In the first part of this paper, we explore and introduce a multi-step reasoning framework for election prediction, which systematically integrates demographic, ideological, and time-sensitive factors. Validated on 2016 and 2020 real-world data and extensive synthetic personas, our approach adapts to changing political landscapes, reducing bias and significantly improving predictive accuracy. We further apply our pipeline to the 2024 U.S. presidential election, illustrating its ability to generalize beyond observed historical data. Beyond enhancing accuracy, the second part of the paper provides insights into the broader implications of LLM-based election forecasting. We identify potential political biases embedded in pretrained corpora, examine how demographic patterns can become exaggerated, and suggest strategies for mitigating these issues. Together, this project, a large-scale LLM empirical study, advances the accuracy of election predictions and establishes directions for more balanced, transparent, and context-aware modeling in political science research and practice.

Political-LLM: Large Language Models in Political Science

In recent years, large language models (LLMs) have been widely adopted in political science tasks such as election prediction, sentiment analysis, policy impact assessment, and misinformation detection. Meanwhile, the need to systematically understand how LLMs can further revolutionize the field also becomes urgent. In this work, we--a multidisciplinary team of researchers spanning computer science and political science--present the first principled framework termed Political-LLM to advance the comprehensive understanding of integrating LLMs into computational political science. Specifically, we first introduce a fundamental taxonomy classifying the existing explorations into two perspectives: political science and computational methodologies. In particular, from the political science perspective, we highlight the role of LLMs in automating predictive and generative tasks, simulating behavior dynamics, and improving causal inference through tools like counterfactual generation; from a computational perspective, we introduce advancements in data preparation, fine-tuning, and evaluation methods for LLMs that are tailored to political contexts. We identify key challenges and future directions, emphasizing the development of domain-specific datasets, addressing issues of bias and fairness, incorporating human expertise, and redefining evaluation criteria to align with the unique requirements of computational political science. Political-LLM seeks to serve as a guidebook for researchers to foster an informed, ethical, and impactful use of Artificial Intelligence in political science. Our online resource is available at: http://political-llm.org/.