@inproceedings{skurla-etal-2026-mcdok,
    title = "mcdok at {S}em{E}val-2026 Task 13: Finetuning {LLM}s for Detection of Machine-Generated Code",
    author = "Skurla, Adam  and
      Macko, Dominik  and
      Simko, Jakub",
    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-1.47/",
    pages = "322--327",
    ISBN = "979-8-89176-414-9",
    abstract = "Multi-domain detection of the machine-generated code snippets in various programming languages is a challenging task. SemEval-2026 Task 13 copes with this challenge in various angles, as a binary detection problem as well as attribution of the source. Specifically, its subtasks also cover generator LLM family detection, as well as a hybrid code co-generated by humans and machines, or adversarially modified codes hiding its origin. Our submitted systems adjusted the existing mdok approach (focused on machine-generated text detection) to these specific kinds of problems by exploring various base models, more suitable for code understanding. The results indicate that the submitted systems are competitive in all three subtasks. However, the margins from the top-performing systems are significant, and thus further improvements are possible."
}