@inproceedings{gautam-pop-2024-factgenius,
    title = "{F}act{G}enius: Combining Zero-Shot Prompting and Fuzzy Relation Mining to Improve Fact Verification with Knowledge Graphs",
    author = "Gautam, Sushant  and
      Pop, Roxana",
    editor = "Schlichtkrull, Michael  and
      Chen, Yulong  and
      Whitehouse, Chenxi  and
      Deng, Zhenyun  and
      Akhtar, Mubashara  and
      Aly, Rami  and
      Guo, Zhijiang  and
      Christodoulopoulos, Christos  and
      Cocarascu, Oana  and
      Mittal, Arpit  and
      Thorne, James  and
      Vlachos, Andreas",
    booktitle = "Proceedings of the Seventh Fact Extraction and VERification Workshop (FEVER)",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2024.fever-1.30/",
    doi = "10.18653/v1/2024.fever-1.30",
    pages = "297--306",
    abstract = "Fact-checking is a crucial natural language processing (NLP) task that verifies the truthfulness of claims by considering reliable evidence. Traditional methods are labour- intensive, and most automatic approaches focus on using documents as evidence. In this paper, we focus on the relatively understudied fact-checking with Knowledge Graph data as evidence and experiment on the recently introduced FactKG benchmark. We present FactGenius, a novel method that enhances fact- checking by combining zero-shot prompting of large language models (LLMs) with fuzzy text matching on knowledge graphs (KGs). Our method employs LLMs for filtering relevant connections from the graph and validates these connections via distance-based matching. The evaluation of FactGenius on an existing benchmark demonstrates its effectiveness, as we show it significantly outperforms state-of- the-art methods. The code and materials are available at https://github.com/SushantGautam/FactGenius."
}