@inproceedings{pancholi-etal-2025-tabxeval,
    title = "{T}ab{XE}val: Why this is a Bad Table? An e{X}haustive Rubric for Table Evaluation",
    author = "Pancholi, Vihang  and
      Bafna, Jainit Sushil  and
      Anvekar, Tejas  and
      Shrivastava, Manish  and
      Gupta, Vivek",
    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/corrections-2025-08/2025.findings-acl.1176/",
    doi = "10.18653/v1/2025.findings-acl.1176",
    pages = "22913--22934",
    ISBN = "979-8-89176-256-5",
    abstract = "Evaluating tables qualitatively and quantitatively poses a significant challenge, as standard metrics often overlook subtle structural and content-level discrepancies. To address this, we propose a rubric-based evaluation framework that integrates multi-level structural descriptors with fine-grained contextual signals, enabling more precise and consistent table comparison. Building on this, we introduce TabXEval, an eXhaustive and eXplainable two-phase evaluation framework. TabXEval first aligns reference and predicted tables structurally via TabAlign, then performs semantic and syntactic comparison using TabCompare, offering interpretable and granular feedback. We evaluate TabXEval on TabXBench, a diverse, multi-domain benchmark featuring realistic table perturbations and human annotations. A sensitivity-specificity analysis further demonstrates the robustness and explainability of TabXEval across varied table tasks. Code and data are available at https://corallab- asu.github.io/tabxeval/."
}