UNIKIE-BENCH

Key Information Extraction (KIE) from real-world documents remains challenging due to substantial variations in layout structures, visual quality, and task-specific information requirements. Recent Large Multimodal Models (LMMs) have shown promising potential for performing end-to-end KIE directly from document images. To enable a comprehensive and systematic evaluation across realistic and diverse application scenarios, we introduce UniKIE-BENCH, a unified benchmark designed to rigorously evaluate the KIE capabilities of LMMs. UniKIE-BENCH consists of two complementary tracks: a constrained-category KIE track with scenario-predefined schemas that reflect practical application needs, and an open-category KIE track that extracts any key information that is explicitly present in the document. Experiments on 15 state-of-the-art LMMs reveal substantial performance degradation under diverse schema definitions, long-tail key fields, and complex layouts, along with pronounced performance disparities across different document types and scenarios. These findings underscore persistent challenges in grounding accuracy and layout-aware reasoning for LMM-based KIE.

License

The code in the official GitHub repository is released under the MIT License. The dataset is provided for academic research purposes only. Please refer to the official repository and dataset release notes for detailed license and usage terms.

Citation

If you find UNIKIE-BENCH useful for your research, please cite our paper:

@inproceedings{ji-etal-2026-unikie,
    title = "{UNIKIE}-{BENCH}: Benchmarking Large Multimodal Models for Key Information Extraction in Visual Documents",
    author = "Ji, Yifan  and
      Xu, Zhipeng  and
      Liu, Zhenghao  and
      Chen, Zulong  and
      Zhang, Qian  and
      Yang, Zhibo  and
      Lin, Junyang  and
      Gu, Yu  and
      Yu, Ge  and
      Sun, Maosong",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.acl-long.287/",
    pages = "6331--6352",
    ISBN = "979-8-89176-390-6"
}