Recent advances in machine learning have significantly impacted the field of information extraction, with Large Language Models (LLMs) playing a pivotal role in extracting structured information from unstructured text. This paper explores the challenges and limitations of current methodologies in structured entity extraction and introduces a novel approach to address these issues. We contribute to the field by first introducing and formalizing the task of Structured Entity Extraction (SEE), followed by proposing Approximate Entity Set OverlaP (AESOP) Metric designed to appropriately assess model performance on this task. Later, we propose a new model that harnesses the power of LLMs for enhanced effectiveness and efficiency through decomposing the entire extraction task into multiple stages. Quantitative evaluation and human side-by-side evaluation confirm that our model outperforms baselines, offering promising directions for future advancements in structured entity extraction.

翻译：近年来，机器学习的最新进展显著影响了信息抽取领域，其中大型语言模型在从非结构化文本中提取结构化信息方面发挥了关键作用。本文探讨了当前结构化实体抽取方法中的挑战和局限性，并提出了一种新颖的方法来解决这些问题。我们首先引入并形式化了结构化实体抽取任务，随后提出了近似实体集合重叠指标，旨在适当评估模型在此任务上的性能。接着，我们提出了一种新模型，通过将整个抽取任务分解为多个阶段，利用大型语言模型的强大能力来提升效果和效率。定量评估和人工并行评估证实，我们的模型优于基线方法，为结构化实体抽取的未来进展提供了有前景的方向。