@inproceedings{chauhan-nusrat-2026-ucsc,
    title = "{UCSC}-{NLP} at {S}em{E}val-2026 Task 13: Multi-View Generalization and Diagnostic Analysis of Machine-Generated Code Detection",
    author = "Chauhan, Kargi  and
      Nusrat, Sadiba",
    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-1.311/",
    pages = "2461--2468",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper presents the system for SemEval-2026 Task 13, addressing both binary detection (Subtask A) and multi-class attribution (Subtask B). For Subtask A, we propose a robust multi-view training framework using UniXcoder-base, incorporating domain-specific structural prefixes, delexicalization with symmetric KL consistency loss, and token dropout. Our system achieves a high macro F1 of 0.845 on the out-of-distribution test set, demonstrating strong generalization across five unseen languages and two unseen domains. For Subtask B, we provide a rigorous diagnostic analysis of majority-class bias in transformer-based detectors. We reveal a significant performance gap where an 88.4{\%} accuracy masks a near-complete failure in minority-class attribution (0.086 Macro F1), highlighting that standard fine-tuning is insufficient for fine-grained generator identification. Our results expose distinct regimes in code detection and motivate the need for imbalance-aware, structure-focused modeling in future work."
}