Team Duo at SemEval-2026 Task 13: Fine-tuning CodeBERT for Out-of-Distribution AI-Generated Code Detection

Subhiksha G; Sanjai M; Rajalakshmi Sivanaiah; Angel Deborah S

Team Duo at SemEval-2026 Task 13: Fine-tuning CodeBERT for Out-of-Distribution AI-Generated Code Detection

Subhiksha G, Sanjai M, Rajalakshmi Sivanaiah, Angel Deborah S

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Abstract

This paper addresses detecting AI-generated code in out-of-distribution settings by fine-tuning CodeBERT on algorithmic code from C++, Python, and Java. While the model achieves near-perfect performance on training data (F1 = 0.9935), it degrades significantly on unseen languages and domains (F1 = 0.3532). The high recall (0.8789) but low precision (0.2210) indicates over-prediction of machine-generated code. Error analysis reveals three failure modes: domain mismatch, unfamiliar syntax patterns, and insufficient training. Multi-epoch training and domain-specific augmentation are needed to improve OOD generalization.

Anthology ID:: 2026.semeval-1.265
Volume:: Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
Month:: July
Year:: 2026
Address:: San Diego, California, USA
Editors:: Ekaterina Kochmar, Debanjan Ghosh, Kai North, Mamoru Komachi
Venues:: SemEval | WS
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 2104–2107
Language:
URL:: https://aclanthology.org/2026.semeval-1.265/
DOI:
Bibkey:
Cite (ACL):: Subhiksha G, Sanjai M, Rajalakshmi Sivanaiah, and Angel Deborah S. 2026. Team Duo at SemEval-2026 Task 13: Fine-tuning CodeBERT for Out-of-Distribution AI-Generated Code Detection. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 2104–2107, San Diego, California, USA. Association for Computational Linguistics.
Cite (Informal):: Team Duo at SemEval-2026 Task 13: Fine-tuning CodeBERT for Out-of-Distribution AI-Generated Code Detection (G et al., SemEval 2026)
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PDF:: https://aclanthology.org/2026.semeval-1.265.pdf

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@inproceedings{g-etal-2026-team,
    title = "Team Duo at {S}em{E}val-2026 Task 13: Fine-tuning {C}ode{BERT} for Out-of-Distribution {AI}-Generated Code Detection",
    author = "G, Subhiksha  and
      M, Sanjai  and
      Sivanaiah, Rajalakshmi  and
      S, Angel Deborah",
    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://aclanthology.org/2026.semeval-1.265/",
    pages = "2104--2107",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper addresses detecting AI-generated code in out-of-distribution settings by fine-tuning CodeBERT on algorithmic code from C++, Python, and Java. While the model achieves near-perfect performance on training data (F1 = 0.9935), it degrades significantly on unseen languages and domains (F1 = 0.3532). The high recall (0.8789) but low precision (0.2210) indicates over-prediction of machine-generated code. Error analysis reveals three failure modes: domain mismatch, unfamiliar syntax patterns, and insufficient training. Multi-epoch training and domain-specific augmentation are needed to improve OOD generalization."
}

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%0 Conference Proceedings
%T Team Duo at SemEval-2026 Task 13: Fine-tuning CodeBERT for Out-of-Distribution AI-Generated Code Detection
%A G, Subhiksha
%A M, Sanjai
%A Sivanaiah, Rajalakshmi
%A S, Angel Deborah
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F g-etal-2026-team
%X This paper addresses detecting AI-generated code in out-of-distribution settings by fine-tuning CodeBERT on algorithmic code from C++, Python, and Java. While the model achieves near-perfect performance on training data (F1 = 0.9935), it degrades significantly on unseen languages and domains (F1 = 0.3532). The high recall (0.8789) but low precision (0.2210) indicates over-prediction of machine-generated code. Error analysis reveals three failure modes: domain mismatch, unfamiliar syntax patterns, and insufficient training. Multi-epoch training and domain-specific augmentation are needed to improve OOD generalization.
%U https://aclanthology.org/2026.semeval-1.265/
%P 2104-2107

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Markdown (Informal)

[Team Duo at SemEval-2026 Task 13: Fine-tuning CodeBERT for Out-of-Distribution AI-Generated Code Detection](https://aclanthology.org/2026.semeval-1.265/) (G et al., SemEval 2026)

Team Duo at SemEval-2026 Task 13: Fine-tuning CodeBERT for Out-of-Distribution AI-Generated Code Detection (G et al., SemEval 2026)

ACL

Subhiksha G, Sanjai M, Rajalakshmi Sivanaiah, and Angel Deborah S. 2026. Team Duo at SemEval-2026 Task 13: Fine-tuning CodeBERT for Out-of-Distribution AI-Generated Code Detection. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 2104–2107, San Diego, California, USA. Association for Computational Linguistics.