Document Intelligence in the Era of Large Language Models: A Survey
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866918161000955904 |
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| author | Wang, Weishi Hu, Hengchang Zhang, Zhijie Li, Zhaochen Shao, Hongxin Dahlmeier, Daniel |
| author_facet | Wang, Weishi Hu, Hengchang Zhang, Zhijie Li, Zhaochen Shao, Hongxin Dahlmeier, Daniel |
| contents | Document AI (DAI) has emerged as a vital application area, and is significantly transformed by the advent of large language models (LLMs). While earlier approaches relied on encoder-decoder architectures, decoder-only LLMs have revolutionized DAI, bringing remarkable advancements in understanding and generation. This survey provides a comprehensive overview of DAI's evolution, highlighting current research attempts and future prospects of LLMs in this field. We explore key advancements and challenges in multimodal, multilingual, and retrieval-augmented DAI, while also suggesting future research directions, including agent-based approaches and document-specific foundation models. This paper aims to provide a structured analysis of the state-of-the-art in DAI and its implications for both academic and practical applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_13366 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Document Intelligence in the Era of Large Language Models: A Survey Wang, Weishi Hu, Hengchang Zhang, Zhijie Li, Zhaochen Shao, Hongxin Dahlmeier, Daniel Computation and Language Artificial Intelligence Document AI (DAI) has emerged as a vital application area, and is significantly transformed by the advent of large language models (LLMs). While earlier approaches relied on encoder-decoder architectures, decoder-only LLMs have revolutionized DAI, bringing remarkable advancements in understanding and generation. This survey provides a comprehensive overview of DAI's evolution, highlighting current research attempts and future prospects of LLMs in this field. We explore key advancements and challenges in multimodal, multilingual, and retrieval-augmented DAI, while also suggesting future research directions, including agent-based approaches and document-specific foundation models. This paper aims to provide a structured analysis of the state-of-the-art in DAI and its implications for both academic and practical applications. |
| title | Document Intelligence in the Era of Large Language Models: A Survey |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2510.13366 |