Enterprise AI Analysis
Large Language Models in Document Intelligence: A Comprehensive Survey, Recent Advances, Challenges and Future Trends
Large Language Models (LLMs) have dramatically transformed the field of document intelligence, moving beyond traditional methods. This survey, analyzing approximately 300 papers (2021-mid-2025), provides a comprehensive overview of LLM impact, focusing on Retrieval-Augmented Generation (RAG), long context processing, and fine-tuning for document comprehension. It highlights datasets, applications, challenges, and future trends, offering critical insights for both researchers and industry practitioners.
Executive Impact: Transforming Document Intelligence
The rapid evolution of LLMs has profoundly impacted document intelligence, enabling more advanced and accurate processing solutions across industries. This survey consolidates key findings and future directions for businesses leveraging AI.
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Applications Covered
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Explores methods for converting raw documents into structured representations, from pipeline-based OCR to end-to-end multimodal models that directly map images to outputs. Key to initial data ingestion.
Focuses on fine-tuned multimodal LLMs designed for document understanding tasks, including layout comprehension, high-resolution image processing, multi-page understanding, and table LLMs, showcasing specialized AI.
Covers Retrieval-Augmented Generation strategies, including data cleaning, chunking (simple, rule-based, semantic-based), pre-retrieval, formal retrieval (sparse/dense, iterative/multipath), and post-retrieval reranking for enhanced accuracy.
Addresses the challenges of processing lengthy documents by LLMs, focusing on positional encoding, attention mechanisms, memory management, and prompt compression techniques to maintain context.
Examines practical applications of LLMs in document intelligence across various industries such as Finance, Legal, and Medicine, highlighting domain-specific challenges and solutions.
Functional Landscape of Document Intelligence
Intelligent Document Analysis
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Intelligent Document Editing
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Document Automation
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Intelligent Search and Q&A
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Knowledge Management
15%
Increase in Information Extraction Accuracy with Advanced Chunking
Yepes et al. [298] demonstrated a significant improvement in information extraction accuracy by 15% using extended document chunking methods for financial reports. This highlights the critical role of optimized preprocessing in LLM performance.
Source: Yepes et al. [298]
| Criteria | RAG | Document LLMs |
|---|---|---|
| Core Mechanism | Retrieves relevant document sections and generates answers based on local context. | Performs end-to-end document understanding with multimodal input and task-specific fine-tuning. |
| Performance & Efficiency |
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| Interpretability & Traceability | Outputs are grounded in retrievable text chunks, making source tracking easier. | Lacks inherent source attribution; difficult to trace answers without external alignment. |
| Flexibility & Generalization | Flexible to unseen documents and dynamic queries; can be combined with other models. | Strong for fixed tasks with clear document structures; less adaptive to open-ended scenarios. |
| Context Handling Ability | Mitigates context window limits by selectively retrieving relevant chunks. | Limited by model context length; less effective on multi-page or long-form inputs. |
| Recommended Scenarios |
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Healthcare Applications: Improving Clinical Documentation
In the medical domain, LLMs are transforming electronic health records (EHR) analysis. Goyal et al. [76] developed a specialized medical LLM that significantly improves clinical documentation through domain-optimized training and enhanced prompt engineering. This leads to superior performance in tasks like generating discharge summaries and extracting patient information from various formats, with observed improvements in data extraction accuracy of up to 18%.
This advancement provides efficient and precise data processing tools for modern medical research, accelerating evidence synthesis and improving patient care.
Citation: Goyal et al. [76], Adamson et al. [2]
Calculate Your Potential ROI with Document AI
Estimate the efficiency gains and cost savings your enterprise could achieve by integrating advanced Document Intelligence solutions.
Estimated Annual Savings
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Your Enterprise AI Roadmap
Navigating the future of document intelligence requires a strategic approach. Here’s a potential roadmap based on current challenges and future trends.
Advanced Error Correction Mechanisms
Implement sophisticated error detection and correction mechanisms to address noise in retrieval results, enhancing the quality and reliability of RAG systems.
More Flexible RAG Architectures
Develop recursive and adaptive RAG architectures to iteratively refine retrieval and generation processes, supporting diverse document structures and user preferences.
Cross-Modal Fusion beyond Text
Further integrate non-textual modalities like tables, images, and diagrams more effectively into RAG systems for comprehensive document understanding.
Ethical AI & Bias Mitigation
Focus on rigorous bias mitigation and ethical considerations in LLM development for document intelligence, especially in sensitive domains like healthcare.
Real-time Processing Optimization
Optimize LLM inference speed and memory consumption to enable real-time services for complex, long-context document analysis.
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