@inproceedings{wang-etal-2026-teleai,
    title = "{T}ele{AI} at {S}em{E}val-2026 Task 13: Data-Centric Full-Parameter Fine-Tuning with Multi-Level Ensembling for Generated Code Detection",
    author = "Wang, Shiquan  and
      Yu, Fang  and
      Song, Shuangyong  and
      Li, Yongxiang  and
      Li, Xuelong",
    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-1.38/",
    pages = "263--269",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper presents our top-ranking system for SemEval-2026 Task 13 on code generation detection under multi-lingual and distribution-shift settings. Our approach achieved 1st place in Subtasks A and B, and 2nd place in Subtask C in the official evaluation.Our framework integrates data-centric analysis, full-parameter model adaptation, and multi-level ensemble learning. We first analyze label and length distributions and apply repeated oversampling to address class imbalance. We then optimize prompts in a data-driven manner to improve inference stability. Based on Qwen3-30B-A3B-Instruct, we conduct full-parameter fine-tuning with diverse training configurations and integrate multiple checkpoints using soft voting, hard voting, logits-based voting, and LightGBM stacking.Experimental results demonstrate substantial improvements over zero-shot baselines and consistent gains from ensemble strategies, validating the effectiveness of systematic adaptation and ensembling for robust code generation detection."
}