@inproceedings{li-etal-2025-longtablebench,
    title = "{L}ong{T}able{B}ench: Benchmarking Long-Context Table Reasoning across Real-World Formats and Domains",
    author = "Li, Liyao  and
      Tian, Jiaming  and
      Chen, Hao  and
      Ye, Wentao  and
      Ye, Chao  and
      Wang, Haobo  and
      Wang, Ningtao  and
      Fu, Xing  and
      Chen, Gang  and
      Zhao, Junbo",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.638/",
    doi = "10.18653/v1/2025.findings-emnlp.638",
    pages = "11927--11965",
    ISBN = "979-8-89176-335-7",
    abstract = "We introduce **LongTableBench**, a benchmark for evaluating long-context reasoning over semi-structured tables across diverse formats, tasks, and domains. It comprises 5,950 QA instances spanning 7 table formats (e.g., Markdown, HTML, SQL), 18 domains, and input lengths up to 128K tokens, including multi-turn and multi-table settings. To ensure data quality, we combine symbolic supervision, cross-model validation, and human review. Evaluating 52 LLMs{---}including general-purpose, table-specific, and reasoning-enhanced models{---}reveals that only the strongest models maintain robust performance under increasing context lengths and format diversity. We further show that end-to-end models outperform compression-based approaches, especially on tasks requiring semantic integration. LongTableBench provides a rigorous, scalable testbed for advancing long-context tabular understanding and highlights key limitations in current LLMs' structural and reasoning capabilities."
}