@inproceedings{srivastava-etal-2025-instruction,
    title = "Instruction-Tuning {LLM}s for Event Extraction with Annotation Guidelines",
    author = "Srivastava, Saurabh  and
      Pati, Sweta  and
      Yao, Ziyu",
    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/display_plenaries/2025.findings-acl.677/",
    pages = "13055--13071",
    ISBN = "979-8-89176-256-5",
    abstract = "In this work, we study the effect of annotation guidelines{--}textual descriptions of event types and arguments, when instruction-tuning large language models for event extraction. We conducted a series of experiments with both human-provided and machine-generated guidelines in both full- and low-data settings. Our results demonstrate the promise of annotation guidelines when there is a decent amount of training data and highlight its effectiveness in improving cross-schema generalization and low-frequency event-type performance."
}