@inproceedings{zhu-etal-2024-lc4ee,
title = "{LC}4{EE}: {LLM}s as Good Corrector for Event Extraction",
author = "Zhu, Mengna and
Zeng, Kaisheng and
JibingWu, JibingWu and
Liu, Lihua and
Huang, Hongbin and
Hou, Lei and
Li, Juanzi",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.715",
doi = "10.18653/v1/2024.findings-acl.715",
pages = "12028--12038",
abstract = "Event extraction (EE) is a critical task in natural language processing, yet deploying a practical EE system remains challenging. On one hand, powerful large language models (LLMs) currently show poor performance because EE task is more complex than other tasks. On the other hand, state-of-the-art (SOTA) small language models (SLMs) for EE tasks are typically developed through fine-tuning, lack flexibility, and have considerable room for improvement. We propose an approach, **L**LMs-as-**C**orrector for **E**vent **E**xtraction (**LC4EE**), aiming to leverage the superior extraction capability of SLMs and the instruction-following ability of LLMs to construct a robust and highly available EE system. By utilizing LLMs to identify and correct errors of SLMs predictions based on automatically generated feedback information, EE performances can be improved significantly. Experimental results on the representative datasets ACE2005 and MAVEN-Arg for Event Detection (ED) and EE tasks validated the effectiveness of our method.",
}
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<abstract>Event extraction (EE) is a critical task in natural language processing, yet deploying a practical EE system remains challenging. On one hand, powerful large language models (LLMs) currently show poor performance because EE task is more complex than other tasks. On the other hand, state-of-the-art (SOTA) small language models (SLMs) for EE tasks are typically developed through fine-tuning, lack flexibility, and have considerable room for improvement. We propose an approach, **L**LMs-as-**C**orrector for **E**vent **E**xtraction (**LC4EE**), aiming to leverage the superior extraction capability of SLMs and the instruction-following ability of LLMs to construct a robust and highly available EE system. By utilizing LLMs to identify and correct errors of SLMs predictions based on automatically generated feedback information, EE performances can be improved significantly. Experimental results on the representative datasets ACE2005 and MAVEN-Arg for Event Detection (ED) and EE tasks validated the effectiveness of our method.</abstract>
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%0 Conference Proceedings
%T LC4EE: LLMs as Good Corrector for Event Extraction
%A Zhu, Mengna
%A Zeng, Kaisheng
%A JibingWu, JibingWu
%A Liu, Lihua
%A Huang, Hongbin
%A Hou, Lei
%A Li, Juanzi
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics: ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F zhu-etal-2024-lc4ee
%X Event extraction (EE) is a critical task in natural language processing, yet deploying a practical EE system remains challenging. On one hand, powerful large language models (LLMs) currently show poor performance because EE task is more complex than other tasks. On the other hand, state-of-the-art (SOTA) small language models (SLMs) for EE tasks are typically developed through fine-tuning, lack flexibility, and have considerable room for improvement. We propose an approach, **L**LMs-as-**C**orrector for **E**vent **E**xtraction (**LC4EE**), aiming to leverage the superior extraction capability of SLMs and the instruction-following ability of LLMs to construct a robust and highly available EE system. By utilizing LLMs to identify and correct errors of SLMs predictions based on automatically generated feedback information, EE performances can be improved significantly. Experimental results on the representative datasets ACE2005 and MAVEN-Arg for Event Detection (ED) and EE tasks validated the effectiveness of our method.
%R 10.18653/v1/2024.findings-acl.715
%U https://aclanthology.org/2024.findings-acl.715
%U https://doi.org/10.18653/v1/2024.findings-acl.715
%P 12028-12038
Markdown (Informal)
[LC4EE: LLMs as Good Corrector for Event Extraction](https://aclanthology.org/2024.findings-acl.715) (Zhu et al., Findings 2024)
ACL
- Mengna Zhu, Kaisheng Zeng, JibingWu JibingWu, Lihua Liu, Hongbin Huang, Lei Hou, and Juanzi Li. 2024. LC4EE: LLMs as Good Corrector for Event Extraction. In Findings of the Association for Computational Linguistics: ACL 2024, pages 12028–12038, Bangkok, Thailand. Association for Computational Linguistics.