@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://preview.aclanthology.org/ingest-acl/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."
}