@inproceedings{zhou-etal-2025-gqc,
    title = "{GQC}: {LLM}-Based Grouped {QA} Consolidation for Open-Domain Fact Verification at {AV}eri{T}e{C}",
    author = "Zhou, Dongzhuoran  and
      Pop, Roxana  and
      Zhu, Yuqicheng  and
      Kharlamov, Evgeny",
    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-1.11/",
    pages = "151--161",
    ISBN = "978-1-959429-53-1",
    abstract = "Structured fact verification benchmarks like AVeriTeC decompose claims into QA pairs to support fine-grained reasoning. However, current systems generate QA pairs independently for each evidence sentence, leading to redundancy, drift, and noise. We introduce a modular LLM-based QA consolidation module that jointly filters, clusters, and rewrites QA pairs at the claim level. Experiments show that this method improves evidence quality and veracity prediction accuracy. Our analysis also highlights the impact of model scale and alignment on downstream performance."
}