@inproceedings{oses-grijalba-etal-2025-semeval,
    title = "{S}em{E}val-2025 Task 8: Question Answering over Tabular Data",
    author = "Os{\'e}s Grijalba, Jorge  and
      Ure{\~n} - L{\'o}pez, L. Alfonso  and
      Mart{\'i}nez C{\'a}mara, Eugenio  and
      Camacho - Collados, Jose",
    editor = "Rosenthal, Sara  and
      Ros{\'a}, Aiala  and
      Ghosh, Debanjan  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-08/2025.semeval-1.324/",
    pages = "2512--2522",
    ISBN = "979-8-89176-273-2",
    abstract = "We introduce the findings and results of SemEval-2025 Task 8: Question Answering over Tabular Data. We featured two subtasks, DataBench and DataBench Lite. DataBench consists on question answering over tabular data, and DataBench Lite small comprising small datasets that might be easier to manage by current models by for example fitting them into a prompt. The task was open for any approach, but their answer has to conform to a required typing format. In this paper we present the task, analyze a number of system submissions and discuss the results. The results show how approaches leveraging LLMs dominated the task, with larger models exhibiting a considerably superior performance compared to small models."
}