Papers › Towards LLM-based Fact Verification on News Claims with a Hierarchical Step-by-Step...
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Fake News Detection | RAWFC | HiSS | F1 | 53.9 | #2 of 6 | Archive leaderboard | report |
| Fake News Detection | RAWFC | ReAct | F1 | 49.8 | #4 of 6 | Archive leaderboard | report |
| Fake News Detection | RAWFC | Standard prompting with articles | F1 | 47.9 | #5 of 6 | Archive leaderboard | report |
| Fake News Detection | RAWFC | CoT | F1 | 44.4 | #6 of 6 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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