@inproceedings{brandao-filho-etal-2026-akcit,
    title = "{AKCIT} at {S}em{E}val-2026 Task 13: A Lightweight {L}ight{GBM} Baseline for Cross-Language Detection of {LLM}-Generated Code",
    author = "Brandao Filho, Rone  and
      Rezende Rios, Walcy Santos  and
      Neves, Lucas  and
      Fleury Oliveira, Jose Ricardo  and
      Fernandes, Diogo  and
      Galv{\~a}o Filho, Arlindo",
    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-1.417/",
    pages = "3357--3362",
    ISBN = "979-8-89176-414-9",
    abstract = "The widespread use of LLMs in software development has made the detection of machine-generated code a pressing challenge, particularly when models must generalize across programming languages and domains. We present a lightweight, LLM-free pipeline that combines stylometric feature extraction with a LightGBM classifier and explicitly prioritizes structural generalization over deep semantic modeling. Despite its simplicity, the method achieves a Macro F1 of 0.70{--}0.72, more than doubling the CodeBERT baseline (0.30) in SemEval-2026 Task 13 Subtask A, while operating without GPUs or any fine-tuning."
}