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        <title>HyperparameterOmens at SemEval-2026 Task 13: Various approaches to detecting machine- generated code</title>
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            <namePart type="given">Ekaterina</namePart>
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    <abstract>We present our systems for SemEval-2026 Task 13, built on the Droid resource suite and benchmark setting. For Subtask A (binary classification of human-written vs. machine-generated code), lexical baselines such as TF–IDF and character n-grams transferred poorly from the LeetCode training distribution to the production-code evaluation split. After correcting pipeline errors that obscured true performance and selecting stable AST features under domain shift, our final system uses 5 uncorrelated features and achieves 0.57 macro F1 on the public test set.For Subtask C (4-way authorship classification of human, AI, hybrid, and adversarial) lexical baselines performed poorly under a significant vocabulary shift. Deep semantic models proved more promising, and a per-class weighted ensemble which included these models achieved 0.57 macro F1 on the public test set</abstract>
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%0 Conference Proceedings
%T HyperparameterOmens at SemEval-2026 Task 13: Various approaches to detecting machine- generated code
%A Sukhotin, Dmitry
%A Yu, How
%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 sukhotin-yu-2026-hyperparameteromens
%X We present our systems for SemEval-2026 Task 13, built on the Droid resource suite and benchmark setting. For Subtask A (binary classification of human-written vs. machine-generated code), lexical baselines such as TF–IDF and character n-grams transferred poorly from the LeetCode training distribution to the production-code evaluation split. After correcting pipeline errors that obscured true performance and selecting stable AST features under domain shift, our final system uses 5 uncorrelated features and achieves 0.57 macro F1 on the public test set.For Subtask C (4-way authorship classification of human, AI, hybrid, and adversarial) lexical baselines performed poorly under a significant vocabulary shift. Deep semantic models proved more promising, and a per-class weighted ensemble which included these models achieved 0.57 macro F1 on the public test set
%U https://aclanthology.org/2026.semeval-1.177/
%P 1372-1378

Markdown (Informal)

[HyperparameterOmens at SemEval-2026 Task 13: Various approaches to detecting machine- generated code](https://aclanthology.org/2026.semeval-1.177/) (Sukhotin & Yu, SemEval 2026)

• HyperparameterOmens at SemEval-2026 Task 13: Various approaches to detecting machine- generated code (Sukhotin & Yu, SemEval 2026)

ACL

• Dmitry Sukhotin and How Yu. 2026. HyperparameterOmens at SemEval-2026 Task 13: Various approaches to detecting machine- generated code. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 1372–1378, San Diego, California, USA. Association for Computational Linguistics.