@inproceedings{yoon-etal-2025-team,
    title = "Team {HUMANE} at {AV}eri{T}e{C} 2025: {H}er{O} 2 for Efficient Fact Verification",
    author = "Yoon, Yejun  and
      Jung, Jaeyoon  and
      Yoon, Seunghyun  and
      Park, Kunwoo",
    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-1.16/",
    pages = "224--228",
    ISBN = "978-1-959429-53-1",
    abstract = "This paper presents HerO 2, Team HUMANE{'}s system for the AVeriTeC shared task at the FEVER-25 workshop. HerO 2 is an enhanced version of HerO, the best-performing open-source model from the previous year{'}s challenge. It improves evidence quality through document summarization and answer reformulation, optimizes veracity prediction via post-training quantization under computational constraints, and enhances overall system performance by integrating updated language model (LM) backbones. HerO 2 ranked second on the leaderboard while achieving the shortest runtime among the top three systems, demonstrating both high efficiency and strong potential for real-world fact verification. The code is available at \url{https://github.com/ssu-humane/HerO2}."
}