  Claim verification plays a crucial role in combating misinformation. While
existing works on claim verification have shown promising results, a crucial
piece of the puzzle that remains unsolved is to understand how to verify claims
without relying on human-annotated data, which is expensive to create at a
large scale. Additionally, it is important for models to provide comprehensive
explanations that can justify their decisions and assist human fact-checkers.
This paper presents First-Order-Logic-Guided Knowledge-Grounded (FOLK)
Reasoning that can verify complex claims and generate explanations without the
need for annotated evidence using Large Language Models (LLMs). FOLK leverages
the in-context learning ability of LLMs to translate the claim into a
First-Order-Logic (FOL) clause consisting of predicates, each corresponding to
a sub-claim that needs to be verified. Then, FOLK performs FOL-Guided reasoning
over a set of knowledge-grounded question-and-answer pairs to make veracity
predictions and generate explanations to justify its decision-making process.
This process makes our model highly explanatory, providing clear explanations
of its reasoning process in human-readable form. Our experiment results
indicate that FOLK outperforms strong baselines on three datasets encompassing
various claim verification challenges. Our code and data are available.

Computation and Language;Artificial Intelligence;Machine Learning