Fact-Checking Complex Claims with Program-Guided Reasoning
Liangming Pan Xiaobao Wu Xinyuan Lu Anh Tuan Luu William Yang Wang Min-Yen Kan Preslav Nakov
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 https://github.com/mbzuai-nlp/ProgramFC.