@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",
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."
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%0 Conference Proceedings
%T Instruction-Tuning LLMs for Event Extraction with Annotation Guidelines
%A Srivastava, Saurabh
%A Pati, Sweta
%A Yao, Ziyu
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F srivastava-etal-2025-instruction
%X 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.
%R 10.18653/v1/2025.findings-acl.677
%U https://aclanthology.org/2025.findings-acl.677/
%U https://doi.org/10.18653/v1/2025.findings-acl.677
%P 13055-13071
Markdown (Informal)
[Instruction-Tuning LLMs for Event Extraction with Annotation Guidelines](https://aclanthology.org/2025.findings-acl.677/) (Srivastava et al., Findings 2025)
ACL