Will Trump Win in 2024? Predicting the US Presidential Election via Multi-step Reasoning with Large Language Models

• URL: http://arxiv.org/abs/2411.03321v1
• Date: Mon, 21 Oct 2024 06:18:53 GMT
• Title: Will Trump Win in 2024? Predicting the US Presidential Election via Multi-step Reasoning with Large Language Models
• Authors: Chenxiao Yu, Zhaotian Weng, Zheng Li, Xiyang Hu, Yue Zhao,
• Abstract summary: Election prediction poses unique challenges, such as limited voter-level data, rapidly changing political landscapes, and the need to model complex human behavior. 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. We apply our framework to predict the outcome of the 2024 U.S. presidential election in advance.
• Score: 12.939107088730513
• License:
• 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. Additionally, we apply our framework to predict the outcome of the 2024 U.S. presidential election in advance, demonstrating the adaptability of LLMs to unseen political data.

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