PDF Retrieval Augmented Question Answering
On the map: Retrieval-augmented generation → RAG robustness and adaptive retrieval
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation. Builds on 17 of its 22 references that are in the map. First posted on arXiv 22 Jun 2025.
Builds on
17 of 22 references.
Closest ancestors · 2
Same direction, newest first
- 2024Revolutionizing Retrieval-Augmented Generation with Enhanced PDF Structure Recognition23 Jan
- 2022Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering6 Oct
Other references · 15
- 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
- 2023Visual Instruction Tuningcited by 7,869
- 2023Improved Baselines with Visual Instruction Tuningcited by 4,418
- 2023A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivitycited by 894
- 2023Towards General Text Embeddings with Multi-stage Contrastive Learningcited by 723
- 2024RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrievalcited by 532
- 2023Query Rewriting for Retrieval-Augmented Large Language Modelscited by 213
- 2024Corrective Retrieval Augmented Generationcited by 197
- 2024Blended RAG: Improving RAG (Retriever-Augmented Generation) Accuracy with Semantic Search and Hybrid Query-Based Retrieverscited by 84
- 2024RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculturecited by 81
- 2024Fine Tuning vs. Retrieval Augmented Generation for Less Popular Knowledgecited by 50
3 more
- 2023PDFTriage: Question Answering over Long, Structured Documentscited by 29
- 2024Chain-of-Action: Faithful and Multimodal Question Answering through Large Language Modelscited by 27
- 2023Multimodal Multi-Hop Question Answering Through a Conversation Between Tools and Efficiently Finetuned Large Language Modelscited by 6
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- 2026Empirical Evaluation of PDF Parsing and Chunking for Financial Question Answering with RAG
- 2025Conversational Text Extraction with Large Language Models Using Retrieval-Augmented Systems
- 2024Retrieval-Augmented Generation for Domain-Specific Question Answering: A Case Study on Pittsburgh and CMU
- 2026Page-Aware Retrieval-Augmented Generation for EvalLLM 2026: A Five-Variant Study on French PDFs
- 2026From PDF to RAG-Ready: Evaluating Document Conversion Frameworks for Domain-Specific Question Answering
- 2024Searching for Best Practices in Retrieval-Augmented Generation
- 2025T2-RAGBench: Text-and-Table Benchmark for Evaluating Retrieval-Augmented Generation
- 2024DR-RAG: Applying Dynamic Document Relevance to Retrieval-Augmented Generation for Question-Answering