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Predicting the US Presidential Election via Multi-step Reasoning with Large Language Models
This research paper investigates the use of Large Language Models (LLMs) for predicting US presidential election outcomes. The authors introduce a novel multi-step reasoning framework that incorporates voter demographics, candidates' policy positions, and biographical information to improve prediction accuracy. They test their framework on real-world data from the American National Election Studies and synthetic datasets, showcasing the potential and limitations of LLMs in this context. Furthermore, the paper applies their framework to predict the 2024 US presidential election, demonstrating the adaptability of LLMs to unseen political data.
https://arxiv.org/pdf/2411.03321
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By AIPPDThis research paper investigates the use of Large Language Models (LLMs) for predicting US presidential election outcomes. The authors introduce a novel multi-step reasoning framework that incorporates voter demographics, candidates' policy positions, and biographical information to improve prediction accuracy. They test their framework on real-world data from the American National Election Studies and synthetic datasets, showcasing the potential and limitations of LLMs in this context. Furthermore, the paper applies their framework to predict the 2024 US presidential election, demonstrating the adaptability of LLMs to unseen political data.
https://arxiv.org/pdf/2411.03321