@inproceedings{dredze-etal-2024-academics,
    title = "Academics Can Contribute to Domain-Specialized Language Models",
    author = "Dredze, Mark  and
      Winata, Genta Indra  and
      Kambadur, Prabhanjan  and
      Wu, Shijie  and
      Irsoy, Ozan  and
      Lu, Steven  and
      Dabravolski, Vadim  and
      Rosenberg, David S  and
      Gehrmann, Sebastian",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.emnlp-main.293/",
    doi = "10.18653/v1/2024.emnlp-main.293",
    pages = "5100--5110",
    abstract = "Commercially available models dominate academic leaderboards. While impressive, this has concentrated research on creating and adapting general-purpose models to improve NLP leaderboard standings for large language models. However, leaderboards collect many individual tasks and general-purpose models often underperform in specialized domains; domain-specific or adapted models yield superior results. This focus on large general-purpose models excludes many academics and draws attention away from areas where they can make important contributions. We advocate for a renewed focus on developing and evaluating domain- and task-specific models, and highlight the unique role of academics in this endeavor."
}