@inproceedings{ding-etal-2026-survey,
title = "A Survey on {MLLM}-based Visually Rich Document Understanding: Methods, Challenges, and Emerging Trends",
author = "Ding, Yihao and
Luo, Siwen and
Dai, Yue and
Jiang, Yanbei and
Li, Zechuan and
Sun, Qiang and
Martin, Geoffrey and
Liu, Wei and
Peng, Yifan",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.652/",
pages = "13319--13340",
ISBN = "979-8-89176-395-1",
abstract = "Visually Rich Document Understanding (VRDU) has become a pivotal area of research, driven by the need to automatically interpret documents that contain intricate visual, textual, and structural elements. Recently, Multimodal Large Language Models (MLLMs) have demonstrated significant promise in this domain, including both OCR-based and OCR-free approaches for information extraction from document images. This survey reviews recent advances in MLLM-based VRDU, highlighting emerging trends and promising research directions with a focus on two key aspects: (1) techniques for representing and integrating textual, visual, and layout features; (2) training paradigms, including pretraining, instruction tuning, and training strategies. Moreover, we address challenges such as data scarcity, handling multi-page and multilingual documents, and integrating emerging trends such as Retrieval-Augmented Generation and agentic frameworks. Our analysis offers a roadmap for advancing MLLM-based VRDU toward more scalable, reliable, and adaptable systems."
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<abstract>Visually Rich Document Understanding (VRDU) has become a pivotal area of research, driven by the need to automatically interpret documents that contain intricate visual, textual, and structural elements. Recently, Multimodal Large Language Models (MLLMs) have demonstrated significant promise in this domain, including both OCR-based and OCR-free approaches for information extraction from document images. This survey reviews recent advances in MLLM-based VRDU, highlighting emerging trends and promising research directions with a focus on two key aspects: (1) techniques for representing and integrating textual, visual, and layout features; (2) training paradigms, including pretraining, instruction tuning, and training strategies. Moreover, we address challenges such as data scarcity, handling multi-page and multilingual documents, and integrating emerging trends such as Retrieval-Augmented Generation and agentic frameworks. Our analysis offers a roadmap for advancing MLLM-based VRDU toward more scalable, reliable, and adaptable systems.</abstract>
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%0 Conference Proceedings
%T A Survey on MLLM-based Visually Rich Document Understanding: Methods, Challenges, and Emerging Trends
%A Ding, Yihao
%A Luo, Siwen
%A Dai, Yue
%A Jiang, Yanbei
%A Li, Zechuan
%A Sun, Qiang
%A Martin, Geoffrey
%A Liu, Wei
%A Peng, Yifan
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F ding-etal-2026-survey
%X Visually Rich Document Understanding (VRDU) has become a pivotal area of research, driven by the need to automatically interpret documents that contain intricate visual, textual, and structural elements. Recently, Multimodal Large Language Models (MLLMs) have demonstrated significant promise in this domain, including both OCR-based and OCR-free approaches for information extraction from document images. This survey reviews recent advances in MLLM-based VRDU, highlighting emerging trends and promising research directions with a focus on two key aspects: (1) techniques for representing and integrating textual, visual, and layout features; (2) training paradigms, including pretraining, instruction tuning, and training strategies. Moreover, we address challenges such as data scarcity, handling multi-page and multilingual documents, and integrating emerging trends such as Retrieval-Augmented Generation and agentic frameworks. Our analysis offers a roadmap for advancing MLLM-based VRDU toward more scalable, reliable, and adaptable systems.
%U https://aclanthology.org/2026.findings-acl.652/
%P 13319-13340
Markdown (Informal)
[A Survey on MLLM-based Visually Rich Document Understanding: Methods, Challenges, and Emerging Trends](https://aclanthology.org/2026.findings-acl.652/) (Ding et al., Findings 2026)
ACL
- Yihao Ding, Siwen Luo, Yue Dai, Yanbei Jiang, Zechuan Li, Qiang Sun, Geoffrey Martin, Wei Liu, and Yifan Peng. 2026. A Survey on MLLM-based Visually Rich Document Understanding: Methods, Challenges, and Emerging Trends. In Findings of the Association for Computational Linguistics: ACL 2026, pages 13319–13340, San Diego, California, United States. Association for Computational Linguistics.