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.

本研究论文研究了当前结构化实体提取方法的挑战和限制，并提出了一种新的方法来解决这些问题。通过将整个提取任务分解为多个阶段，我们的模型利用大型语言模型的强大能力，提供了更好的效果和效率，从而在结构化实体提取领域取得了突破性的成果。