{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/towards-llm-based-fact-verification-on-news","title":"Towards LLM-based Fact Verification on News Claims with a Hierarchical Step-by-Step Prompting Method","arxiv_id":"2310.00305","date":"2023-09-30","proceeding":null,"authors":["Xuan Zhang","Wei Gao"],"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.","url_abs":"https://arxiv.org/abs/2310.00305v1","url_pdf":"https://arxiv.org/pdf/2310.00305v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"towards-llm-based-fact-verification-on-news","repo_url":"https://github.com/jadecurl/hiss","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"claim-verification","task_name":"Claim Verification"},{"task_slug":"fact-verification","task_name":"Fact Verification"},{"task_slug":"fake-news-detection","task_name":"Fake News Detection"},{"task_slug":"in-context-learning","task_name":"In-Context Learning"},{"task_slug":"misinformation","task_name":"Misinformation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/fake-news-detection-on-rawfc","task":"Fake News Detection","dataset":"RAWFC","model":"HiSS","rank_in_archive_order":2,"of":6,"metrics":{"F1":"53.9"},"uses_additional_data":false},{"leaderboard":"/sota/fake-news-detection-on-rawfc","task":"Fake News Detection","dataset":"RAWFC","model":"ReAct","rank_in_archive_order":4,"of":6,"metrics":{"F1":"49.8"},"uses_additional_data":false},{"leaderboard":"/sota/fake-news-detection-on-rawfc","task":"Fake News Detection","dataset":"RAWFC","model":"Standard prompting with articles","rank_in_archive_order":5,"of":6,"metrics":{"F1":"47.9"},"uses_additional_data":false},{"leaderboard":"/sota/fake-news-detection-on-rawfc","task":"Fake News Detection","dataset":"RAWFC","model":"CoT","rank_in_archive_order":6,"of":6,"metrics":{"F1":"44.4"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2310.00305","atlas_url":"https://app.syntology.ai/?focus=2310.00305","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.00305"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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