@inproceedings{yamaguchi-etal-2026-enhancing,
    title = "Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks",
    author = "Yamaguchi, Atsuki  and
      Mi, Maggie  and
      Aletras, Nikolaos",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-short.27/",
    pages = "316--336",
    ISBN = "979-8-89176-391-3",
    abstract = "Language models (LMs) are pre-trained on raw text datasets to generate text sequences token-by-token. While this approach facilitates the learning of world knowledge and reasoning, it does not explicitly optimize for linguistic competence. To bridge this gap, we propose L2T, a pre-training framework integrating Language Learning Tasks alongside standard next-token prediction. Inspired by human language acquisition, L2T transforms raw text into structured input-output pairs to provide explicit linguistic stimulation. Pre-training LMs on a mixture of raw text and L2T data not only improves overall performance on linguistic competence benchmarks but accelerates their acquisition, while maintaining competitive performance on general reasoning tasks."
}