Decompose, Enrich, and Extract! Schema-aware Event Extraction using LLMs
<|reference_start|>Decompose, Enrich, and Extract! Schema-aware Event Extraction using LLMs: 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.<|reference_end|>
@article{shiri2024decompose,,
    title={Decompose, Enrich, and Extract! Schema-aware Event Extraction using LLMs},
    author={Fatemeh Shiri, Van Nguyen, Farhad Moghimifar, John Yoo, Gholamreza
  Haffari, Yuan-Fang Li},
    journal={arXiv preprint arXiv:2406.01045},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.01045},
    primaryClass={cs.CL cs.AI}
}