PDF检索增强型问答
PDF Retrieval Augmented Question Answering
AI总结：
本文提出基于检索增强生成框架的PDF多模态问答系统，通过整合非文本元素提升复杂查询的准确性与相关性。
摘要：
本文提出了一种基于检索增强生成框架的改进型问答系统，用于增强从PDF文件中提取信息的能力。PDF中包含文本、图像、矢量图、图表和表格等丰富多样的数据，这对传统仅针对文本内容设计的问答系统提出了独特挑战。我们旨在开发一个全面的基于RAG的问答系统，以有效处理复杂多模态问题，其中多种数据类型结合在查询中。这主要通过改进处理和整合PDF中非文本元素的方法，以及微调大语言模型来实现。我们提供了深入的实验评估，证明了该系统能够提取准确的信息，应用于PDF中的不同内容类型。这项工作不仅推动了检索增强问答系统的发展，还为多模态数据整合与处理的进一步研究奠定了基础。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
