@inproceedings{hejman-etal-2026-generative,
    title = "Generative Multilingual Coreference Resolution at {CRAC} 2026",
    author = "Hejman, Jakub  and
      Prazak, Ondrej  and
      Konop{\'i}k, Miloslav",
    editor = "Braud, Chlo{\'e}  and
      Hardmeier, Christian  and
      Ogrodniczuk, Maciej  and
      Loaiciga, Sharid  and
      Zeldes, Amir  and
      Nov{\'a}k, Michal  and
      Li, Chuyuan  and
      Strube, Michael  and
      Li, Junyi Jessy",
    booktitle = "Proceedings of the 2nd Joint Workshop on Computational Approaches to Discourse, Context and Document-Level Inferences and Computational Models of Reference, Anaphora and Coreference ({CODI}-{CRAC} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.codi-1.22/",
    pages = "162--166",
    ISBN = "979-8-89176-400-2",
    abstract = "Participating again in this year{'}s edition of the CRAC shared task on coreference resolution, we present our upgraded system with an official uplift of 15.46 percentage points in CoNLL-U score. We incorporated the larger Gemma 3 27B IT model, joint pre-training, headword tagging, more efficient training and inference as well as a sliding window to achieve this result. Our system placed second in the LLM track and third overall with a primary score of 73.83. We reached the highest scores on two datasets. Finally, we compare specialized and general LLM approaches."
}