@inproceedings{deng-etal-2025-longdocurl,
    title = "{L}ong{D}oc{URL}: a Comprehensive Multimodal Long Document Benchmark Integrating Understanding, Reasoning, and Locating",
    author = "Deng, Chao  and
      Yuan, Jiale  and
      Bu, Pi  and
      Wang, Peijie  and
      Li, Zhong-Zhi  and
      Xu, Jian  and
      Li, Xiao-Hui  and
      Gao, Yuan  and
      Song, Jun  and
      Zheng, Bo  and
      Liu, Cheng-Lin",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.57/",
    pages = "1135--1159",
    ISBN = "979-8-89176-251-0",
    abstract = "Large vision language models (LVLMs) have improved the document understanding capabilities remarkably, enabling the handling of complex document elements, longer contexts, and a wider range of tasks. However, existing document understanding benchmarks have been limited to handling only a small number of pages and fail to provide a comprehensive analysis of layout elements locating. In this paper, we first define three primary task categories: Long Document Understanding, numerical Reasoning, and cross-element Locating, and then propose a comprehensive benchmark{---}LongDocURL{---}integrating above three primary tasks and comprising 20 sub-tasks categorized based on different primary tasks and answer evidences. Furthermore, we develop a semi-automated construction pipeline and collect 2,325 high-quality question-answering pairs, covering more than 33,000 pages of documents, significantly outperforming existing benchmarks. Subsequently, we conduct comprehensive evaluation experiments on both open-source and closed- source models across 26 different configurations, revealing critical performance gaps in this field. The code and data: https://github.com/dengc2023/LongDocURL."
}