@inproceedings{foroutan-etal-2026-conlid,
    title = "{C}on{LID}: Supervised Contrastive Learning for Low-Resource Language Identification",
    author = "Foroutan, Negar  and
      Saydaliev, Jakhongir  and
      Kim, Grace  and
      Bosselut, Antoine",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/manual-author-scripts/2026.eacl-long.315/",
    pages = "6693--6708",
    ISBN = "979-8-89176-380-7",
    abstract = "Language identification (LID) is a critical step in curating multilingual LLM pretraining corpora from web crawls. While many studies on LID model training focus on collecting diverse training data to improve performance, low-resource languages {--} often limited to single-domain data, such as the Bible {--} continue to perform poorly. To resolve these class imbalance and bias issues, we propose a novel supervised contrastive learning (SCL) approach to learn domain-invariant representations for low-resource languages. We show that our approach improves LID performance on out-of-domain data for low-resource languages by 3.2 percentage points, while maintaining its performance for the high-resource languages."
}