@inproceedings{s-etal-2026-semeval,
    title = "{S}em{E}val-2026 Task 13: Fine-tuned {C}ode{BERT} with Stratified Balancing, Dynamic Threshold Optimization, and Logit Bias Correction for Robust Multi-Language {AI} Code Detection",
    author = "S, Udaythalavesh  and
      Sivanaiah, Rajalakshmi  and
      S, Angel Deborah",
    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-1.132/",
    pages = "958--963",
    ISBN = "979-8-89176-414-9",
    abstract = "We present a CodeBERT-based system for detecting AI-generated code in SemEval-2026 Task 13 Subtask A. To address class imbalance and model overconfidence, we apply stratified balanced subsampling, dynamic per-epoch F1-macro threshold optimization, and label-flip bias correction. The model is trained using TPU-accelerated fine-tuning and achieves a validation F1-macro of 0.874 and a private leaderboard F1-macro of 0.53. Ablation studies confirm the effectiveness of our balancing and calibration strategies under distribution shift."
}