@inproceedings{yeom-etal-2025-tagged,
    title = "Tagged Span Annotation for Detecting Translation Errors in Reasoning {LLM}s",
    author = "Yeom, Taemin  and
      Ryu, Yonghyun  and
      Choi, Yoonjung  and
      Bak, Jinyeong",
    editor = "Haddow, Barry  and
      Kocmi, Tom  and
      Koehn, Philipp  and
      Monz, Christof",
    booktitle = "Proceedings of the Tenth Conference on Machine Translation",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.wmt-1.62/",
    pages = "878--886",
    ISBN = "979-8-89176-341-8",
    abstract = "We present the AIP team{'}s submission to the WMT 2025 Unified MT Evaluation SharedTask, focusing on the span-level error detection subtask. Our system emphasizes response format design to better harness the capabilities of OpenAI{'}s o3, the state-of-the-art reasoning LLM. To this end, we introduce Tagged SpanAnnotation (TSA), an annotation scheme designed to more accurately extract span-level information from the LLM. On our refined version of WMT24 ESA dataset, our reference-free method achieves an F1 score of approximately 27 for character-level label prediction, outperforming the reference-based XCOMET-XXL at approximately 17."
}