@inproceedings{lu-etal-2025-bounding,
    title = "A Bounding Box is Worth One Token - Interleaving Layout and Text in a Large Language Model for Document Understanding",
    author = "Lu, Jinghui  and
      Yu, Haiyang  and
      Wang, Yanjie  and
      Ye, Yongjie  and
      Tang, Jingqun  and
      Yang, Ziwei  and
      Wu, Binghong  and
      Liu, Qi  and
      Feng, Hao  and
      Wang, Han  and
      Liu, Hao  and
      Huang, Can",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.findings-acl.379/",
    pages = "7252--7273",
    ISBN = "979-8-89176-256-5",
    abstract = "Recently, many studies have demonstrated that exclusively incorporating OCR-derived text and spatial layouts with large language models (LLMs) can be highly effective for document understanding tasks. However, existing methods that integrate spatial layouts with text have limitations, such as producing overly long text sequences or failing to fully leverage the autoregressive traits of LLMs. In this work, we introduce Interleaving Layout andText in a Large Language Model (LayTextLLM) for document understanding. LayTextLLM projects each bounding box to a single embedding and interleaves it with text, efficiently avoiding long sequence issues while leveraging autoregressive traits of LLMs. LayTextLLM not only streamlines the interaction of layout and textual data but also shows enhanced performance in KIE and VQA. Comprehensive benchmark evaluations reveal significant improvements of LayTextLLM, with a 15.2{\%} increase on KIE tasks and 10.7{\%} on VQA tasks compared to previous SOTA OCR-based LLMs. All resources are available at URL masked for anonymous review."
}