让AI读懂含图表的PDF，精准回答跨模态问题。
PDF Retrieval Augmented Question Answering
- 将图文混排的PDF内容整合进RAG框架，提升多模态信息提取能力。
- 在多个数据类型混合的复杂问题上，答案准确率显著优于传统方法。
- 适合需要解析技术文档、报告等复杂PDF的科研与工程人员。
本文提出一种基于检索增强生成（RAG）框架的问答系统，旨在提升从PDF文件中提取信息的能力。考虑到PDF内容丰富多样，包括文本、图像、矢量图、图表和表格，现有问答系统主要针对纯文本设计，难以应对此类复杂场景。本研究致力于构建一个全面的RAG式问答系统，以有效处理涉及多种数据类型的复合型问题。核心在于优化非文本元素（如图像、图表）的处理与融合方式，并对大语言模型进行微调，使其更好地适配本系统。通过深入的实验评估，验证了该方案在不同内容类型的PDF中均能准确提取信息。本工作不仅拓展了检索增强问答系统的边界，也为多模态数据集成与处理提供了研究基础。
原文摘要 · Abstract (English)
This paper presents an advancement in Question-Answering (QA) systems using a Retrieval Augmented Generation (RAG) framework to enhance information extraction from PDF files. Recognizing the richness and diversity of data within PDFs--including text, images, vector diagrams, graphs, and tables--poses unique challenges for existing QA systems primarily designed for textual content. We seek to develop a comprehensive RAG-based QA system that will effectively address complex multimodal questions, where several data types are combined in the query. This is mainly achieved by refining approaches to processing and integrating non-textual elements in PDFs into the RAG framework to derive precise and relevant answers, as well as fine-tuning large language models to better adapt to our system. We provide an in-depth experimental evaluation of our solution, demonstrating its capability to extract accurate information that can be applied to different types of content across PDFs. This work not only pushes the boundaries of retrieval-augmented QA systems but also lays a foundation for further research in multimodal data integration and processing.
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