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

• URL: http://arxiv.org/abs/2310.00305v1
• Date: Sat, 30 Sep 2023 08:33:04 GMT
• Title: Towards LLM-based Fact Verification on News Claims with a Hierarchical Step-by-Step Prompting Method
• Authors: Xuan Zhang and Wei Gao
• Abstract summary: In this paper, we examine large pre-trained language models (LLMs) with in-context learning (ICL) for news claim verification. 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.
• Score: 9.099277246096861
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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