Decompose, Enrich, and Extract! Schema-aware Event Extraction using LLMs

• URL: http://arxiv.org/abs/2406.01045v1
• Date: Mon, 3 Jun 2024 06:55:10 GMT
• Title: Decompose, Enrich, and Extract! Schema-aware Event Extraction using LLMs
• Authors: Fatemeh Shiri, Van Nguyen, Farhad Moghimifar, John Yoo, Gholamreza Haffari, Yuan-Fang Li,
• Abstract summary: This work focuses on harnessing Large Language Models for automated Event Extraction. It introduces a new method to address hallucination by decomposing the task into Event Detection and Event Argument Extraction.
• Score: 45.83950260830323
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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