Towards More Accurate US Presidential Election via Multi-step Reasoning with Large Language Models

TL;DR

This paper presents a multi-step reasoning framework using Large Language Models to improve the accuracy of US presidential election predictions by modeling complex voter behavior and political dynamics.

Contribution

It introduces a novel multi-step reasoning approach with Chain of Thought prompting for election prediction, incorporating temporal and demographic factors, validated on real and synthetic data.

Findings

Enhanced prediction accuracy with multi-step reasoning

Effective modeling of voter behavior and political dynamics

Scalable approach using synthetic and real-world data

Abstract

Can Large Language Models (LLMs) accurately predict election outcomes? While LLMs have demonstrated impressive performance in various domains, including healthcare, legal analysis, and creative tasks, their ability to forecast elections remains unknown. Election prediction poses unique challenges, such as limited voter-level data, rapidly changing political landscapes, and the need to model complex human behavior. To address these challenges, we introduce a multi-step reasoning framework designed for political analysis. Our approach is validated on real-world data from the American National Election Studies (ANES) 2016 and 2020, as well as synthetic personas generated by the leading machine learning framework, offering scalable datasets for voter behavior modeling. To capture temporal dynamics, we incorporate candidates' policy positions and biographical details, ensuring that the model adapts to evolving political contexts. Drawing on Chain of Thought prompting, our multi-step reasoning pipeline systematically integrates demographic, ideological, and time-dependent factors, enhancing the model's predictive power.