This paper introduces distribution-based prediction, a novel approach to using Large Language Models (LLMs) as predictive tools by interpreting output token probabilities as distributions representing the models' learned representation of the world. This distribution-based nature offers an alternative perspective for analyzing algorithmic fidelity, complementing the approach used in silicon sampling. We demonstrate the use of distribution-based prediction in the context of recent United States presidential election, showing that this method can be used to determine task specific bias, prompt noise, and algorithmic fidelity. This approach has significant implications for assessing the reliability and increasing transparency of LLM-based predictions across various domains.

翻译：本文提出一种基于分布预测的新方法，将大语言模型（LLMs）作为预测工具，通过将输出标记概率解释为表示模型所学世界表征的分布。这种基于分布的特性为分析算法保真度提供了替代视角，与硅采样方法形成互补。我们以近期美国总统选举为背景展示基于分布预测的应用，证明该方法可用于确定任务特定偏差、提示噪声及算法保真度。该方法对评估各领域基于大语言模型预测的可靠性及增强透明度具有重要影响。