Can LLMs Automate Fact-Checking Article Writing?

Researchers Dhruv Sahnan, David Corney, Irene Larraz, Giovanni Zagni, Ruben Miguez, Zhuohan Xie, Iryna Gurevych, Elizabeth Churchill, Tanmoy Chakraborty and Preslav Nakov published a study in Transactions of the Association for Computational Linguistics Volume 14 (pages 489–509) introducing Qraft, an LLM-based agentic framework that mimics the writing workflow of human fact-checkers to generate full fact-checking articles. Human evaluations with professional fact-checkers found Qraft outperforms several previously proposed text-generation approaches but lags considerably behind expert-written articles. The code is available at https://github.com/mbzuai-nlp/qraft.git.

Abstract Automatic fact-checking aims to support professional fact-checkers by offering tools that can help speed up manual fact-checking. Yet, existing frameworks fail to address the key step of producing output suitable for broader dissemination to the general public: While human fact-checkers communicate their findings through fact-checking articles, automated systems typically produce little or no justification for their assessments. Here, we aim to bridge this gap. In particular, we argue for the need to extend the typical automatic fact-checking pipeline with automatic generation of full fact-checking articles. We first identify key desiderata for such articles through a series of interviews with experts from leading fact-checking organizations. We then develop Qraft, an LLM-based agentic framework that mimics the writing workflow of human fact-checkers. Finally, we assess the practical usefulness of Qraft through human evaluations with professional fact-checkers. Our evaluation shows that while Qraft outperforms several previously proposed text-generation approaches, it lags considerably behind expert-written articles. We hope that our work will enable further research in this new and important direction. The code for our implementation is available at https://github.com/mbzuai-nlp/qraft.git https://github.com/mbzuai-nlp/qraft.git . - Anthology ID: - 2026.tacl-1.23 - Volume: - Transactions of the Association for Computational Linguistics, Volume 14 https://aclanthology.org/volumes/2026.tacl-1/ - Month: - Year: - 2026 - Address: - Cambridge, MA - Venue: - TACL https://aclanthology.org/venues/tacl/ - SIG: - Publisher: - MIT Press - Note: - Pages: - 489–509 - Language: - URL: - https://aclanthology.org/2026.tacl-1.23/ https://aclanthology.org/2026.tacl-1.23/ - DOI: - 10.1162/tacl.a.644 https://doi.org/10.1162/tacl.a.644 - Cite ACL : - Dhruv Sahnan, David Corney, Irene Larraz, Giovanni Zagni, Ruben Miguez, Zhuohan Xie, Iryna Gurevych, Elizabeth Churchill, Tanmoy Chakraborty, and Preslav Nakov. 2026. Can LLMs Automate Fact-Checking Article Writing? https://aclanthology.org/2026.tacl-1.23/ . Transactions of the Association for Computational Linguistics , 14:489–509. - Cite Informal : - Can LLMs Automate Fact-Checking Article Writing? https://aclanthology.org/2026.tacl-1.23/ Sahnan et al., TACL 2026 - PDF: - https://aclanthology.org/2026.tacl-1.23.pdf https://aclanthology.org/2026.tacl-1.23.pdf