Towards LLM-based fact verification on news claims with a hierarchical step-by-step prompting method - ZHANG, Xuan (Institutional Knowledge at Singapore Management University, 2023)
While large pre-trained language models (LLMs) have shown their impressive capabilities in various NLP tasks, they are still underexplored 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 questionsanswering steps progressively. Experiment results on two public misinformation datasets show that HiSS prompting outperforms stateof-the-art fully-supervised approach and strong few-shot ICL-enabled baselines.