@inproceedings{yotkova-etal-2026-fmisuyotkovakastreva,
    title = "{FMISUY}otkova{K}astreva at {S}em{E}val-2026 Task 13: Lightweight Detection of {LLM}-Generated Code via Stylometric Signals",
    author = "Yotkova, Elitsa  and
      Kastreva, Violeta  and
      Dimitrov, Dimitar  and
      Koychev, Ivan  and
      Nakov, Preslav",
    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-1.275/",
    pages = "2179--2186",
    ISBN = "979-8-89176-414-9",
    abstract = "SemEval-2026 Task 13 investigates machine-generated code detection across multiple programming languages and application scenarios, asking participating systems to generalize to unseen languages and domains. This paper describes our participation in Subtask A (binary classification) and explores both pretrained code encoders and lightweight feature-based methods.We design ratio-based features that are less sensitive to snippet length. To support the extraction of descriptiveness-related signals, we use parsing engines and a programming-language classifier. Additionally, we train a separate code-vs-text line classifier to identify raw natural language segments embedded within samples. We combine a shallow decision tree with heuristic rules derived from data analysis to produce the final predictions. Our approach is computationally efficient, requires only CPU resources for training, and achieves near-instant inference time, offering a lightweight alternative to large pretrained models."
}