@inproceedings{contalbo-etal-2025-gri,
    title = "{GRI}-{QA}: a Comprehensive Benchmark for Table Question Answering over Environmental Data",
    author = "Contalbo, Michele Luca  and
      Pederzoli, Sara  and
      Buono, Francesco Del  and
      Valeria, Venturelli  and
      Guerra, Francesco  and
      Paganelli, Matteo",
    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/display_plenaries/2025.findings-acl.814/",
    pages = "15764--15779",
    ISBN = "979-8-89176-256-5",
    abstract = "Assessing corporate environmental sustainability with Table Question Answering systems is challenging due to complex tables, specialized terminology, and the variety of questions they must handle. In this paper, we introduce GRI-QA, a test benchmark designed to evaluate Table QA approaches in the environmental domain. Using GRI standards, we extract and annotate tables from non-financial corporate reports, generating question-answer pairs through a hybrid LLM-human approach. The benchmark includes eight datasets, categorized by the types of operations required, including operations on multiple tables from multiple documents. Our evaluation reveals a significant gap between human and model performance, particularly in multi-step reasoning, highlighting the relevance of the benchmark and the need for further research in domain-specific Table QA. Code and benchmark datasets are available at https://github.com/softlab-unimore/gri{\_}qa."
}