@inproceedings{malaguti-etal-2025-orqa,
    title = "{O}r{QA} {--} Open Data Retrieval for Question Answering dataset generation",
    author = "Malaguti, Giovanni  and
      Mozzillo, Angelo  and
      Simonini, Giovanni",
    editor = "Chang, Shuaichen  and
      Hulsebos, Madelon  and
      Liu, Qian  and
      Chen, Wenhu  and
      Sun, Huan",
    booktitle = "Proceedings of the 4th Table Representation Learning Workshop",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.trl-1.14/",
    pages = "172--181",
    ISBN = "979-8-89176-268-8",
    abstract = "We present OrQA, a novel agentic framework to generate large-scale tabular question-answering (TQA) datasets based on real-world open data.Such datasets are needed to overcome the limitations of existing benchmark datasets, which rely on synthetic questions or limited web tables.OrQA employs LLM agents to retrieve related open data tables, generate natural questions, and synthesize executable $\texttt{SQL}$ queries{---}involving joins, unions, and other non-trivial operations.By leveraging hundreds of GPU hours on four NVIDIA A100, we applied OrQA to Canadian and UK government open data to produce 1,000 question-tables{--}SQL triples, a representative sample of which has been human{-}validated.This open{-}source dataset is now publicly available to drive transparency, reproducibility, and progress in table{-}based question answering."
}