{"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/justilm-few-shot-justification-generation-for","title":"JustiLM: Few-shot Justification Generation for Explainable Fact-Checking of Real-world Claims","arxiv_id":"2401.08026","date":"2024-01-16","proceeding":null,"authors":["Fengzhu Zeng","Wei Gao"],"abstract":"Justification is an explanation that supports the veracity assigned to a claim in fact-checking. However, the task of justification generation is previously oversimplified as summarization of fact-check article authored by fact-checkers. Therefore, we propose a realistic approach to generate justification based on retrieved evidence. We present a new benchmark dataset called ExClaim for \\underline{Ex}plainable fact-checking of real-world \\underline{Claim}s, and introduce JustiLM, a novel few-shot \\underline{Justi}fication generation based on retrieval-augmented \\underline{L}anguage \\underline{M}odel by using fact-check articles as auxiliary resource during training only. Experiments show that JustiLM achieves promising performance in justification generation compared to strong baselines, and can also enhance veracity classification with a straightforward extension.","url_abs":"https://arxiv.org/abs/2401.08026v1","url_pdf":"https://arxiv.org/pdf/2401.08026v1.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":"justilm-few-shot-justification-generation-for","repo_url":"https://github.com/znhy1024/justilm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"articles","task_name":"Articles"},{"task_slug":"fact-checking","task_name":"Fact Checking"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"veracity-classification","task_name":"Veracity Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2401.08026","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}