@inproceedings{pavelka-2026-landcore,
    title = "Landcore: Coreference Resolution with Language-Specific {LLM}-Enhanced Prompts and {XML}-Inspired Annotation Scheme",
    author = "Pavelka, Jan",
    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-1.24/",
    pages = "184--192",
    ISBN = "979-8-89176-400-2",
    abstract = "This paper presents {\_}Landcore{\_} (LANguage Dependent COference REsolution), our submission to the LLM Track of the CRAC 2026 Shared Task on Multilingual Coreference Resolution. We explore the capabilities of LLMs in coreference resolution across multiple languages and domains, using a few-shot prompting approach. We design a comprehensive prompt that includes detailed instructions and examples and further enhance it using an LLM to produce language-specific prompts. We present an XML-inspired annotation scheme that is more suitable for LLMs than the provided formats. Although our solution is not the best-performing, we show that our ideas improve performance across various settings."
}