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While large pre-trained language models (LLMs) have shown their impressivecapabilities in various NLP tasks, they are still under-explored in themisinformation domain. In this paper, we examine LLMs with in-context learning(ICL) for news claim verification, and find that only with 4-shot demonstrationexamples, the performance of several prompting methods can be comparable withprevious supervised models. To further boost performance, we introduce aHierarchical Step-by-Step (HiSS) prompting method which directs LLMs toseparate a claim into several subclaims and then verify each of them viamultiple questions-answering steps progressively. Experiment results on twopublic misinformation datasets show that HiSS prompting outperformsstate-of-the-art fully-supervised approach and strong few-shot ICL-enabledbaselines.

Towards LLM-based Fact Verification on News Claims with a Hierarchical Step-by-Step Prompting Method