Large Language Models in Document Intelligence: A Comprehensive Survey, Recent Advances, Challenges, and Future Trends

The rapid proliferation of documents has made document intelligence increasingly critical across various industries. In recent years, Large Language Models (LLMs) have dramatically transformed the field of document intelligence, allowing for more advanced and accurate document processing solutions. Despite these advancements, most existing surveys have failed to focus on these breakthroughs, instead concentrating on traditional methods and earlier machine learning techniques. This survey seeks to fill that gap by offering an in-depth analysis of approximately 300 papers published between 2021 and mid-2025, thus providing a comprehensive overview of the impact of LLMs in document intelligence. The key topics explored include Retrieval-Augmented Generation (RAG), long-context processing, and fine-tuning LLMs for document comprehension. Furthermore, the survey highlights essential datasets, practical applications, current challenges, and future research directions, offering critical insights for both researchers and industry practitioners looking to advance the field.

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Publication Details

Journal
ACM Transactions on Information Systems
Published
2025-09-16
DOI
https://doi.org/10.1145/3768156
Citations
4
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
9.92
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article

Large Language Models in Document Intelligence: A Comprehensive Survey, Recent Advances, Challenges, and Future Trends

Youlan Li, Peng Wang, Dong Nie, Yao He et al.
4 citations
ACM Transactions on Information Systems
Topic Modeling
9.92
article

Large Language Models in Document Intelligence: A Comprehensive Survey, Recent Advances, Challenges, and Future Trends

Youlan Li, Peng Wang, Dong Nie, Yao He, Wenjun Ke, Yifan Zheng, Huahu Xu
article en
4 citations

Abstract

The rapid proliferation of documents has made document intelligence increasingly critical across various industries. In recent years, Large Language Models (LLMs) have dramatically transformed the field of document intelligence, allowing for more advanced and accurate document processing solutions. Despite these advancements, most existing surveys have failed to focus on these breakthroughs, instead concentrating on traditional methods and earlier machine learning techniques. This survey seeks to fill that gap by offering an in-depth analysis of approximately 300 papers published between 2021 and mid-2025, thus providing a comprehensive overview of the impact of LLMs in document intelligence. The key topics explored include Retrieval-Augmented Generation (RAG), long-context processing, and fine-tuning LLMs for document comprehension. Furthermore, the survey highlights essential datasets, practical applications, current challenges, and future research directions, offering critical insights for both researchers and industry practitioners looking to advance the field.

ACM Transactions on Information SystemsVol. 44(1)
Beijing Institute of Technology (CN), Palo Alto University (US), University of Macau (MO), Metrica (United States) (US), Southeast University (CN)
Openalex Percentile: Top 2%
Topic Modeling
9.92
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