Towards LLM-based Fact Verification on News Claims with a Hierarchical
  Step-by-Step Prompting Method
Xuan Zhang Wei Gao
While large pre-trained language models (LLMs) have shown their impressive
capabilities in various NLP tasks, they are still under-explored in the
misinformation domain. In this paper, we examine LLMs with in-context learning
(ICL) for news claim verification, and find that only with 4-shot demonstration
examples, the performance of several prompting methods can be comparable with
previous supervised models. To further boost performance, we introduce a
Hierarchical Step-by-Step (HiSS) prompting method which directs LLMs to
separate a claim into several subclaims and then verify each of them via
multiple questions-answering steps progressively. Experiment results on two
public misinformation datasets show that HiSS prompting outperforms
state-of-the-art fully-supervised approach and strong few-shot ICL-enabled
baselines.