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

2024-10-21

arXiv.org

AI Summary

This study investigates the feasibility of large language models (LLMs) for U.S. presidential election forecasting under low-data, high-dynamic conditions. Key challenges include modeling volatile voter behavior, rapidly evolving political contexts, and scarcity of ground-truth data. To address these, we propose a multi-step reasoning framework tailored for political analysis: it integrates demographic attributes, ideological positioning, and temporal dynamics; introduces, for the first time, a time-series modeling of candidate policy stances and biographical information; and enables scalable evaluation via hybrid data—combining ANES survey data with controllably generated synthetic voter personas. Our method unifies chain-of-thought prompting, domain-specific political text embeddings, and temporal feature fusion. Evaluated on 2016 and 2020 U.S. election data, it achieves a 12.3 percentage-point accuracy improvement over strong baselines, demonstrating LLMs’ effectiveness and novel utility in complex, real-world political forecasting tasks.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Voting
• Natural Language Processing: (Large) Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Predict US election outcomes using LLMs
• Address challenges in voter behavior modeling
• Incorporate dynamic political and demographic factors

Innovation

• Multi-step reasoning framework for election prediction
• Incorporates policy positions and biographical details
• Uses Chain of Thought prompting for integration