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        <title>JustiLM: Few-shot Justification Generation for Explainable Fact-Checking of Real-world Claims</title>
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    <name type="personal">
        <namePart type="given">Fengzhu</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.1162/tacl_a_00649</identifier>
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        <date>2024</date>
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%0 Journal Article
%T JustiLM: Few-shot Justification Generation for Explainable Fact-Checking of Real-world Claims
%A Zeng, Fengzhu
%A Gao, Wei
%J Transactions of the Association for Computational Linguistics
%D 2024
%V 12
%I MIT Press
%C Cambridge, MA
%F zeng-gao-2024-justilm
%X 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.
%R 10.1162/tacl_a_00649
%U https://aclanthology.org/2024.tacl-1.19
%U https://doi.org/10.1162/tacl_a_00649
%P 334-354

Markdown (Informal)

[JustiLM: Few-shot Justification Generation for Explainable Fact-Checking of Real-world Claims](https://aclanthology.org/2024.tacl-1.19) (Zeng & Gao, TACL 2024)

• JustiLM: Few-shot Justification Generation for Explainable Fact-Checking of Real-world Claims (Zeng & Gao, TACL 2024)

ACL

• Fengzhu Zeng and Wei Gao. 2024. JustiLM: Few-shot Justification Generation for Explainable Fact-Checking of Real-world Claims. Transactions of the Association for Computational Linguistics, 12:334–354.