@inproceedings{fosse-etal-2025-statistical,
    title = "Statistical Deficiency for Task Inclusion Estimation",
    author = {Fosse, Lo{\"i}c  and
      Bechet, Frederic  and
      Favre, Benoit  and
      Damnati, G{\'e}raldine  and
      Lecorv{\'e}, Gw{\'e}nol{\'e}  and
      Darrin, Maxime  and
      Formont, Philippe  and
      Piantanida, Pablo},
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.18/",
    pages = "382--415",
    ISBN = "979-8-89176-251-0",
    abstract = "Tasks are central in machine learning, as they are the most natural objects to assess the capabilities of current models. The trend is to build general models able to address any task. Even though transfer learning and multitask learning try to leverage the underlying task space, no well-founded tools are available to study its structure. This study proposes a theoretically grounded setup to define the notion of task and to compute the inclusion between two tasks from a statistical deficiency point of view. We propose a tractable proxy as information sufficiency to estimate the degree of inclusion between tasks, show its soundness on synthetic data, and use it to reconstruct empirically the classic NLP pipeline."
}