--- title: "JustiLM — paper by Zeng and Gao (2024) — summary and ideas" description: "To support this, they build ExClaim from WatClaimCheck. 1 reference, cited by 17 papers here." authors: ["Fengzhu Zeng", "Wei Gao"] year: 2024 venue: "Transactions of the Association for Computational Linguistics" source: "https://aclanthology.org/2024.tacl-1.19/" doi: "10.1162/tacl_a_00649" licence: "CC BY 4.0 (ACL Anthology)" licence_url: "https://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/zeng-2024-justilm-few-shot-justification-generation/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/zeng-2024-justilm-few-shot-justification-generation/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2024](https://md.research.elamur.ai/free/en/papers/2024/page/6/) / JustiLM: Few-shot Justification Generation for Explainable Fact-Checking of Real-world Claims # JustiLM: Few-shot Justification Generation for Explainable Fact-Checking of Real-world Claims **Authors:** Fengzhu Zeng and [Wei Gao](https://md.research.elamur.ai/free/en/authors/wei-gao/) · **Year:** 2024 · **Venue:** [Transactions of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/tacl/) ([Transactions of the Association for Computational Linguistics (TACL) 2024](https://md.research.elamur.ai/free/en/venues/tacl/2024/)) · **Publisher:** [The MIT Press](https://md.research.elamur.ai/free/en/publishers/the-mit-press/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://aclanthology.org/2024.tacl-1.19/) · [PDF](https://aclanthology.org/2024.tacl-1.19.pdf) · [DOI 10.1162/tacl\_a\_00649](https://doi.org/10.1162/tacl_a_00649) · [OpenAlex](https://openalex.org/W4394742621) **Citations:** 19 citations (OpenAlex, 2026-10-14) · [17](https://md.research.elamur.ai/free/en/papers/zeng-2024-justilm-few-shot-justification-generation/cited-by/) in our library, of the 149,439 papers whose bibliographies we read ## Summary Prior work on explainable fact-checking mostly produced verdict justifications by summarizing the fact-check article a journalist had already written. This is unrealistic, because no such article exists for a new claim, and it skips evidence search and reasoning. The authors instead generate justifications from evidence retrieved out of a large document collection. To support this, they build ExClaim from WatClaimCheck. It contains real-world claims with veracity labels, human-written justifications taken from the concluding "Our ruling"/"Our rating" paragraphs, and a searchable pool of reference documents split into 100-word chunks. Their model, JustiLM, fine-tunes a pre-trained retrieval-augmented generation (RAG) model end-to-end with only a handful of training examples. Fact-check articles supply extra supervision during training but are never seen at inference. JustiLM beats in-context-learning baselines and the few-shot RAG model Atlas, and it comes close to GPT-4. Its outputs tend to be shorter and more focused than GPT-4's. A simple extension that also predicts veracity improves classification substantially. The work matters because it frames justification generation the way it would actually be deployed. ## Key points - ExClaim has 6,951 real-world claims with labels and justifications. Its retrieval corpus contains 957,949 chunk-level documents pooled across all WatClaimCheck claims. - The setup is few-shot: K training instances are sampled at random, and no development set is assumed. - Fact-check articles are used only as auxiliary training signal. At test time, justifications come solely from retrieved evidence. - JustiLM outperforms Flan-T5, Llama2 (both with ICL) and Atlas, and is competitive with GPT-4. Factual consistency is checked automatically with the NLI-based metric SummaCC. - Acknowledged limitations: - There are no passage-level evidence annotations. - The setup assumes gold evidence is present in the corpus. - No human evaluation with professional fact-checkers has been done yet. - Code and data are available at github.com/znhy1024/JustiLM. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* ## Institutions - [Singapore Management University](https://md.research.elamur.ai/free/en/universities/singapore-management-university/) — University, Singapore, Singapore · 27 works · 2,382 citations 📚 Shelves (7): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 16,039 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 15,891 works · [👁️ Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · 2,869 works · [🤝 Social Sciences](https://md.research.elamur.ai/free/en/shelves/social-sciences/) · 1,772 works · [🤝 Sociology and Political Science](https://md.research.elamur.ai/free/en/shelves/social-sciences/sociology-and-political-science/) · 577 works ## Cite Cite as: 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. The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @article{zeng2024justilm, title = {{JustiLM: Few-shot Justification Generation for Explainable Fact-Checking of Real-world Claims}}, author = {Fengzhu Zeng and Wei Gao}, journal = {Transactions of the Association for Computational Linguistics}, year = {2024}, doi = {10.1162/tacl\_a\_00649}, url = {https://doi.org/10.1162/tacl_a_00649} } ``` - [← previous LongEmbed: Extending Embedding Models for Long Context Retrieval](https://md.research.elamur.ai/free/en/papers/zhu-2024-longembed-extending-embedding-models-long/) - [next → Maverick: Efficient and Accurate Coreference Resolution Defying Recent Trends](https://md.research.elamur.ai/free/en/papers/martinelli-2024-maverick-efficient-accurate-coreference-resolution/) - [↑ back to papers of 2024](https://md.research.elamur.ai/free/en/papers/2024/page/6/) ## References 1 references, 0 of them with a card of ours. ↗ leads to the work. 1. Naser Ahmadi, Joohyung Lee, Paolo Papotti, and Mohammed Saeed. 2019. Explainable fact checking with probabilistic answer set programming. In Proceedings of the 2019 Truth and Trust Online Conference, TTO 2019, London, UK. https://doi.org/10.36370/tto .2019.15 — [doi:10.36370/tto](https://doi.org/10.36370/tto) [↗](https://doi.org/10.36370/tto) *Updated 30 October 2026*