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        <title>Archaeology at SemEval-2026 Task 13: Fine-Tuning Pre-Trained Code Models for AI-Generated Code Detection</title>
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        <namePart type="given">Jany-Gabriel</namePart>
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            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Kochmar</namePart>
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    <abstract>This paper describes the system submitted by team Archaeology to SemEval-2026 Task~13 on AI-generated code detection. The shared task consists of three subtasks; we participate in Subtask-A (binary classification: human-written vs.\textbackslash AI-generated code) and Subtask-B (11-class attribution of the generating model).Starting from a TF-IDF and Logistic Regression baseline, we fine-tune four pre-trained code models (CodeBERT, GraphCodeBERT, UniXcoder, and CodeT5+) with separate strategies for each subtask.For Subtask-A, we use leave-one-language-out cross-validation, code augmentation, chunked inference with trimmed-mean aggregation, and threshold calibration on a difficult dataset.For Subtask-B, we use sandwich token packing, class-balanced loss, and multi-seed ensembling with test-time augmentation. Our best submissions obtain macro-F1 scores of 0.737 on Subtask-A and 0.422 on Subtask-B.</abstract>
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%0 Conference Proceedings
%T Archaeology at SemEval-2026 Task 13: Fine-Tuning Pre-Trained Code Models for AI-Generated Code Detection
%A Ispas, Jany-Gabriel
%A Nisioi, Sergiu
%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 ispas-nisioi-2026-archaeology
%X This paper describes the system submitted by team Archaeology to SemEval-2026 Task~13 on AI-generated code detection. The shared task consists of three subtasks; we participate in Subtask-A (binary classification: human-written vs.\textbackslash AI-generated code) and Subtask-B (11-class attribution of the generating model).Starting from a TF-IDF and Logistic Regression baseline, we fine-tune four pre-trained code models (CodeBERT, GraphCodeBERT, UniXcoder, and CodeT5+) with separate strategies for each subtask.For Subtask-A, we use leave-one-language-out cross-validation, code augmentation, chunked inference with trimmed-mean aggregation, and threshold calibration on a difficult dataset.For Subtask-B, we use sandwich token packing, class-balanced loss, and multi-seed ensembling with test-time augmentation. Our best submissions obtain macro-F1 scores of 0.737 on Subtask-A and 0.422 on Subtask-B.
%U https://aclanthology.org/2026.semeval-1.383/
%P 3052-3059

Markdown (Informal)

[Archaeology at SemEval-2026 Task 13: Fine-Tuning Pre-Trained Code Models for AI-Generated Code Detection](https://aclanthology.org/2026.semeval-1.383/) (Ispas & Nisioi, SemEval 2026)

• Archaeology at SemEval-2026 Task 13: Fine-Tuning Pre-Trained Code Models for AI-Generated Code Detection (Ispas & Nisioi, SemEval 2026)

ACL

• Jany-Gabriel Ispas and Sergiu Nisioi. 2026. Archaeology at SemEval-2026 Task 13: Fine-Tuning Pre-Trained Code Models for AI-Generated Code Detection. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 3052–3059, San Diego, California, USA. Association for Computational Linguistics.