@article{zeng-gao-2024-justilm,
    title = "{J}usti{LM}: Few-shot Justification Generation for Explainable Fact-Checking of Real-world Claims",
    author = "Zeng, Fengzhu  and
      Gao, Wei",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "12",
    year = "2024",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.tacl-1.19/",
    doi = "10.1162/tacl_a_00649",
    pages = "334--354",
    abstract = "Justification is an explanation that supports the veracity assigned to a claim in fact-checking. However, the task of justification generation has been previously oversimplified as summarization of a 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 Explainable fact-checking of real-world Claims), and introduce JustiLM, a novel few-shot Justification generation based on retrieval-augmented Language Model by using fact-check articles as an 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.1 Code and dataset are released at https://github.com/znhy1024/JustiLM."
}