@inproceedings{jiang-etal-2025-core,
    title = "Core: Robust Factual Precision with Informative Sub-Claim Identification",
    author = "Jiang, Zhengping  and
      Zhang, Jingyu  and
      Weir, Nathaniel  and
      Ebner, Seth  and
      Wanner, Miriam  and
      Sanders, Kate  and
      Khashabi, Daniel  and
      Liu, Anqi  and
      Van Durme, Benjamin",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.findings-acl.1018/",
    pages = "19833--19856",
    ISBN = "979-8-89176-256-5",
    abstract = "Hallucinations pose a challenge to the application of large language models (LLMs) thereby motivating the development of metrics to evaluate factual precision. We observe that popular metrics using the Decompose-Then-Verify framework, such as FActScore, can be manipulated by adding obvious or repetitive subclaims to artificially inflate scores. This observation motivates our new customizable plug-and-play subclaim selection component called Core, which filters down individual subclaims according to their uniqueness and informativeness. We show that many popular factual precision metrics augmented by Core are substantially more robust on a wide range of knowledge domains. We release an evaluation framework supporting easy and modular use of Core and various decomposition strategies, which we recommend adoption by the community. We also release an expansion of the FActScore biography dataset to facilitate further studies of decomposition-based factual precision evaluation."
}