@inproceedings{buccelli-etal-2026-minds,
    title = "{MINDS} at {S}em{E}val-2026-Task 13: Robust Detection of Machine-Generated Code under Distribution Shift",
    author = "Buccelli, Giorgia Rosalia  and
      Coviello, Antonella  and
      Holota, Alexandra Elena  and
      Scaglione, Marco  and
      Scalora, Simone  and
      Savelli, Claudio  and
      Coppola, Riccardo  and
      Giobergia, Flavio",
    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-1.386/",
    pages = "3080--3088",
    ISBN = "979-8-89176-414-9",
    abstract = "The growing use of large language models for code generation makes distinguishing machine-generated code from human-written code increasingly difficult, especially under distribution shifts in language, domain, and generator family. SemEval-2026 Task 13 targets this challenge through three subtasks: binary detection, multi-class authorship attribution, and hybrid/adversarial code detection.In this paper, we conduct an empirical study across all subtasks, comparing a variety of approaches: frozen encoder representations, feature-based classifiers, fine-tuned transformer models, post-hoc calibration, and probability-level ensembling. Our results show a consistent generalisation gap: strong in-domain validation scores substantially overestimate performance on shifted test conditions.The code is available at https://github.com/AlexandraElena-Holota/SemEval-2026-Task13.git"
}