Large Language Models (LLMs) demonstrate exceptional performance in textual
understanding and tabular reasoning tasks. However, their ability to comprehend
and analyze hybrid text, containing textual and tabular data, remains
underexplored. In this research, we specialize in harnessing the potential of
LLMs to comprehend critical information from financial reports, which are
hybrid long-documents. We propose an Automated Financial Information Extraction
(AFIE) framework that enhances LLMs' ability to comprehend and extract
information from financial reports. To evaluate AFIE, we develop a Financial
Reports Numerical Extraction (FINE) dataset and conduct an extensive
experimental analysis. Our framework is effectively validated on GPT-3.5 and
GPT-4, yielding average accuracy increases of 53.94% and 33.77%, respectively,
compared to a naive method. These results suggest that the AFIE framework
offers accuracy for automated numerical extraction from complex, hybrid
documents.

本文提出了一个基于自动化金融信息提取框架的研究，该框架可以提高大型语言模型在理解和提取来自复杂的混合文本数据类型，特别是金融报告中的信息，尤其是数字提取方面的准确性。与一个简单的方法相比，我们在 GPT-3.5 和 GPT-4 上进行了评估和实验证明了该框架的实际效果提升，平均准确性分别提升了 53.94% 和 33.77%。