@inproceedings{kindji-etal-2025-cross,
    title = "Cross-table Synthetic Tabular Data Detection",
    author = "Kindji, G. Charbel N.  and
      Rojas Barahona, Lina M.  and
      Fromont, Elisa  and
      Urvoy, Tanguy",
    editor = "Alam, Firoj  and
      Nakov, Preslav  and
      Habash, Nizar  and
      Gurevych, Iryna  and
      Chowdhury, Shammur  and
      Shelmanov, Artem  and
      Wang, Yuxia  and
      Artemova, Ekaterina  and
      Kutlu, Mucahid  and
      Mikros, George",
    booktitle = "Proceedings of the 1stWorkshop on GenAI Content Detection (GenAIDetect)",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "International Conference on Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.genaidetect-1.5/",
    pages = "78--84",
    abstract = "Detecting synthetic tabular data is essential to prevent the distribution of false or manipulated datasets that could compromise data-driven decision-making. This study explores whether synthetic tabular data can be reliably identified ``in the wild''{---}meaning across different generators, domains, and table formats. This challenge is unique to tabular data, where structures (such as number of columns, data types, and formats) can vary widely from one table to another. We propose three cross-table baseline detectors and four distinct evaluation protocols, each corresponding to a different level of ``wildness''. Our very preliminary results confirm that cross-table adaptation is a challenging task."
}