ACL 2025finding0 citations
Instruction-Tuning LLMs for Event Extraction with Annotation Guidelines
Saurabh Srivastava, Sweta Pati, Ziyu Yao
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.
BibTeX
@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://aclanthology.org/2025.findings-acl.677/",
doi = "10.18653/v1/2025.findings-acl.677",
pages = "13055--13071",
ISBN = "979-8-89176-256-5"
}