Fact-checking real-world claims often requires collecting multiple pieces of
evidence and applying complex multi-step reasoning. In this paper, we present
Program-Guided Fact-Checking (ProgramFC), a novel fact-checking model that
decomposes complex claims into simpler sub-tasks that can be solved using a
shared library of specialized functions. We first leverage the in-context
learning ability of large language models to generate reasoning programs to
guide the verification process. Afterward, we execute the program by delegating
each sub-task to the corresponding sub-task handler. This process makes our
model both explanatory and data-efficient, providing clear explanations of its
reasoning process and requiring minimal training data. We evaluate ProgramFC on
two challenging fact-checking datasets and show that it outperforms seven
fact-checking baselines across different settings of evidence availability,
with explicit output programs that benefit human debugging. Our codes and data
are publicly available at this https URL

本文提出了一种 Program-Guided Fact-Checking（ProgramFC）模型，通过生成推理程序指导验证过程并将每个子任务委托给相应的子任务处理程序，将复杂的索赔分解为更简单的子任务，从而使我们的模型具有解释性和数据效率，并在两个具有挑战性的事实核查数据集上进行评估，展现了优于七种事实核查基线的表现，通过显式输出程序有利于人工调试。