Large Language Models (LLMs) demonstrate significant capabilities in processing natural language data, promising efficient knowledge extraction from diverse textual sources to enhance situational awareness and support decision-making. However, concerns arise due to their susceptibility to hallucination, resulting in contextually inaccurate content. This work focuses on harnessing LLMs for automated Event Extraction, introducing a new method to address hallucination by decomposing the task into Event Detection and Event Argument Extraction. Moreover, the proposed method integrates dynamic schema-aware augmented retrieval examples into prompts tailored for each specific inquiry, thereby extending and adapting advanced prompting techniques such as Retrieval-Augmented Generation. Evaluation findings on prominent event extraction benchmarks and results from a synthesized benchmark illustrate the method's superior performance compared to baseline approaches.
@article{arxiv.2406.01045,
title = {Decompose, Enrich, and Extract! Schema-aware Event Extraction using LLMs},
author = {Fatemeh Shiri and Van Nguyen and Farhad Moghimifar and John Yoo and Gholamreza Haffari and Yuan-Fang Li},
journal= {arXiv preprint arXiv:2406.01045},
year = {2024}
}