@inproceedings{swaminatha-rao-etal-2026-segmentation,
    title = "Segmentation Fault at {S}em{E}val-2026 Task 13: A Regularization-First Approach with Generator-Based Out-of-Distribution Splits for Detecting {AI}-Generated Code",
    author = "Swaminatha Rao, Lakshmi Priya  and
      Santhakumari Madhavan, Dhannya  and
      Kodeswaran, Sreya  and
      R, Nithila  and
      R, Kanmani",
    editor = "Kochmar, Ekaterina  and
      Ghosh, Debanjan  and
      North, Kai  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.266/",
    pages = "2108--2113",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper describes our submission to SemEval-2026 Task 13 (Subtask A) on detecting AI-generated code. We fine-tune CodeBERT-base using a generator-aware out-of-distribution (OOD) validation split to better simulate unseen test generators. Strong regularization techniques, including stochastic data augmentation, dropout, weight decay, and label smoothing, are applied to prevent overfitting to generator-specific patterns. Experiments with logistic regression, UniXcoder, and vanilla CodeBERT reveal that evaluation design has a larger impact on generalization than model scale or training data volume. Our final system achieves a macro F1 score of 0.439 on the hidden test set, representing a 62{\%} relative improvement over unregularized baselines."
}