@inproceedings{rolinger-liu-2025-graph,
    title = "Graph-of-Thoughts for Fact-Checking with Large Language Models",
    author = "Rolinger, Sascha  and
      Liu, Jin",
    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/acl25-workshop-ingestion/2025.fever-1.21/",
    pages = "266--273",
    ISBN = "978-1-959429-53-1",
    abstract = "We present a fact-checking system developed for the 2025 Automated Verification of Textual Claims (AVeriTeC) shared task, leveraging the Graph-of-Thoughts (GoT) prompting scheme. The GoT approach facilitates iterative refinement during fact-checking by conditioningquestion generation on previous answers and enabling the incorporation of multiple evidence documents per question, thereby mitigatingthe impact of factually incorrect evidence. The efficiency requirements of the shared task are addressed by restricting the width and depthof the thought graph. Additionally, an efficient stopping criterion is derived from the dataset{'}s Not Enough Information (NEI) label. Our system utilizes fine-tuned open-source Large Language Models (LLMs) for question generation, question answering, and final verdict prediction. Empirical results demonstrate competitive performance against top-performing systems in the AVeriTeC shared task and improvements over the baseline method. Our code is publicly available."
}