@inproceedings{ali-fatima-2026-nust,
    title = "{NUST} {C}ode{I}ntel at {S}em{E}val-2026 Task 13: Cross-Domain Detection of Machine-Generated Code via Stylometric Features and Transformer Models",
    author = "Ali, Azher  and
      Fatima, Mehwish",
    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-1.61/",
    pages = "426--433",
    ISBN = "979-8-89176-414-9",
    abstract = "We present our submission to SemEval-2026 Task 13 on cross-language and cross-domain detection of machine-generated code. We compare TF-IDF-based models with stylometric features against LoRA-tuned transformer encoders. While transformers achieve near-perfect in-distribution performance, they degrade sharply on unseen languages and domains. In contrast, a TF-IDF + Logistic Regression model attains the best test Macro-F1 and shows greater robustness. These results highlight the limitations of neural models under distribution shift and the strength of lexical baselines for cross-domain generalization."
}