@inproceedings{chen-xiao-2026-sysupporter,
    title = "{SYSU}pporter at {S}em{E}val-2026 Task 13: Leveraging Stylistic Signals and Language-Aware Truncation for Machine-Generated Code Detection",
    author = "Chen, Longfeng  and
      Xiao, Zheng",
    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-1.364/",
    pages = "2903--2909",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper describes our system for SemEval-2026 Task 13 Subtask B, which requires attributing source code to either a human author or one of 10 LLM families. Guided by dataset analysis, we identify three practical challenges: formatting fingerprints discarded by tokenizers, heterogeneous code lengths, and extreme class imbalance. We build on unixcoder-base with Explicit Stylistic Prompting, Language-Aware Truncation, and imbalance-aware training (Focal Loss, GeM pooling, multi-sample dropout, and bucket batching). Our system achieves 0.434 Macro F1 on the official hidden test set, ranking 4th out of 34 teams with only 125M parameters. Controlled 5-fold cross-validation confirms that each component contributes to the final system, and a formatting-normalization study quantifies the model{'}s reliance on formatting cues."
}