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LLM Generated Distribution-Based Prediction of US Electoral Results, Part I
This research paper proposes a new method for using large language models (LLMs) as predictive tools called Distribution Based Prediction. Instead of simulating individuals (Silicon Sampling), this method analyzes the probabilities associated with the LLM's output tokens as a distribution representing the model's understanding of the world. The authors demonstrate this method by using an LLM to predict the outcome of the 2024 U.S. presidential election, showing that it can be used to identify bias, assess the impact of prompt noise, and evaluate the model's algorithmic fidelity. The paper also discusses the potential limitations of LLMs as predictive models, including the impact of training data cutoff and the challenge of measuring bias.
https://arxiv.org/pdf/2411.03486
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By AIPPDThis research paper proposes a new method for using large language models (LLMs) as predictive tools called Distribution Based Prediction. Instead of simulating individuals (Silicon Sampling), this method analyzes the probabilities associated with the LLM's output tokens as a distribution representing the model's understanding of the world. The authors demonstrate this method by using an LLM to predict the outcome of the 2024 U.S. presidential election, showing that it can be used to identify bias, assess the impact of prompt noise, and evaluate the model's algorithmic fidelity. The paper also discusses the potential limitations of LLMs as predictive models, including the impact of training data cutoff and the challenge of measuring bias.
https://arxiv.org/pdf/2411.03486