@inproceedings{prasanth-2025-construction,
    title = "Construction-Grammar Informed Parameter Efficient Fine-Tuning for Language Models",
    author = "Prasanth",
    editor = "Bonial, Claire  and
      Torgbi, Melissa  and
      Weissweiler, Leonie  and
      Blodgett, Austin  and
      Beuls, Katrien  and
      Van Eecke, Paul  and
      Tayyar Madabushi, Harish",
    booktitle = "Proceedings of the Second International Workshop on Construction Grammars and NLP",
    month = sep,
    year = "2025",
    address = {D{\"u}sseldorf, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/2025.cxgsnlp-1.4/",
    pages = "34--40",
    ISBN = "979-8-89176-318-0",
    abstract = "Large language models excel at statistical pattern recognition but may lack explicit understanding of constructional form-meaning correspondences that characterize human grammatical competence. This paper presents Construction-Aware LoRA (CA-LoRA), a parameter-efficient fine-tuning method that incorporates constructional templates through specialized loss functions and targeted parameter updates. We focus on five major English construction types: ditransitive, caused-motion, resultative, way-construction, and conative. Evaluation on BLiMP, CoLA, and SyntaxGym shows selective improvements: frequent patterns like ditransitive and caused-motion show improvements of approximately 3.5 percentage points, while semi-productive constructions show minimal benefits (1.2 points). Overall performance improves by 1.8{\%} on BLiMP and 1.6{\%} on SyntaxGym, while maintaining competitive performance on general NLP tasks. Our approach requires only 1.72{\%} of trainable parameters and reduces training time by 67{\%} compared to full fine-tuning. This work demonstrates that explicit constructional knowledge can be selectively integrated into neural language models, with effectiveness dependent on construction frequency and structural regularity."
}