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The exponential growth of digital documents, particularly PDFs, presents significant challenges in efficient information retrieval and extraction. Traditional methods often struggle with the complexity and variability inherent in large PDF documents. Recent advancements in Natural Language Processing (NLP), especially Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), offer promising solutions. This paper presents a comprehensive system for efficient information extraction from large PDFs using RAG and LLMs. We propose a robust and scalable pipeline addressing challenges such as document segmentation, dynamic retrieval, and response contextualization. Through extensive experiments across multiple domains—including legal analysis, technical documentation, and scientific literature—we demonstrate that the proposed methodology significantly outperforms existing approaches in terms of accuracy, scalability, and efficiency. Our research lays the groundwork for integrating RAG and LLMs in various domains, offering a valuable tool for extracting knowledge from complex documents.

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Efficient Information Extraction from Large PDFs Using Retrieval-Augmented Generation and Large Language Models

  • 会议论文

  • 发表日期：

    2026年

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  • Sandeep Shinde,
  • Samarveer Moray,
  • Aditya Sakhare,
  • Prathamesh Salokhe,
  • Kedar Sathe

摘要

The exponential growth of digital documents, particularly PDFs, presents significant challenges in efficient information retrieval and extraction. Traditional methods often struggle with the complexity and variability inherent in large PDF documents. Recent advancements in Natural Language Processing (NLP), especially Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), offer promising solutions. This paper presents a comprehensive system for efficient information extraction from large PDFs using RAG and LLMs. We propose a robust and scalable pipeline addressing challenges such as document segmentation, dynamic retrieval, and response contextualization. Through extensive experiments across multiple domains—including legal analysis, technical documentation, and scientific literature—we demonstrate that the proposed methodology significantly outperforms existing approaches in terms of accuracy, scalability, and efficiency. Our research lays the groundwork for integrating RAG and LLMs in various domains, offering a valuable tool for extracting knowledge from complex documents.