@inproceedings{dharmavaram-hakak-2025-sanctuary,
    title = "{SANCTUARY}: An Efficient Evidence-based Automated Fact Checking System",
    author = "Dharmavaram, Arbaaz  and
      Hakak, Saqib",
    editor = "Akhtar, Mubashara  and
      Aly, Rami  and
      Christodoulopoulos, Christos  and
      Cocarascu, Oana  and
      Guo, Zhijiang  and
      Mittal, Arpit  and
      Schlichtkrull, Michael  and
      Thorne, James  and
      Vlachos, Andreas",
    booktitle = "Proceedings of the Eighth Fact Extraction and VERification Workshop (FEVER)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/2025.fever-1.19/",
    pages = "247--257",
    ISBN = "978-1-959429-53-1",
    abstract = "With the growing volume of misinformation online, automated fact-checking systems are becoming increasingly important. This paper presents SANCTUARY, an efficient pipeline for evidence-based verification of real-world claims. Our approach consists of three stages: Hypothetical Question {\&} Passage Generation, a two-step Retrieval-Augmented Generation (RAG) hybrid evidence retrieval, and structured reasoning and prediction, which leverages two lightweight Large Language Models (LLMs). On the challenging AVeriTeC benchmark, our system achieves 25.27 points on the new AVeriTeC score (Ev2R recall), outperforming the previous state-of-the-art baseline by 5 absolute points (1.25{\texttimes} relative improvement). Sanctuary demonstrates that careful retrieval, reasoning strategies and well-integrated language models can substantially advance automated fact-checking performance."
}