@inproceedings{jung-etal-2025-optimizing,
    title = "Optimizing Reliability Scoring for {ILSA}s",
    author = "Jung, Ji Yoon  and
      Bezirhan, Ummugul  and
      von Davier, Matthias",
    editor = "Wilson, Joshua  and
      Ormerod, Christopher  and
      Beiting Parrish, Magdalen",
    booktitle = "Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers",
    month = oct,
    year = "2025",
    address = "Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States",
    publisher = "National Council on Measurement in Education (NCME)",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.aimecon-main.6/",
    pages = "43--49",
    ISBN = "979-8-218-84228-4",
    abstract = "This study proposes an innovative method for evaluating cross-country scoring reliability (CCSR) in multilingual assessments, using hyperparameter optimization and a similarity-based weighted majority scoring within a single human scoring framework. Results show that this approach provides a cost-effective and comprehensive assessment of CCSR without the need for additional raters."
}