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        <title>mcdok at SemEval-2026 Task 13: Finetuning LLMs for Detection of Machine-Generated Code</title>
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        <namePart type="given">Adam</namePart>
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            <title>Proceedings of the 20th International Workshop on Semantic Evaluation (2026)</title>
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            <namePart type="given">Debanjan</namePart>
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            <namePart type="given">Kai</namePart>
            <namePart type="family">North</namePart>
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    <abstract>Multi-domain detection of the machine-generated code snippets in various programming languages is a challenging task. SemEval-2026 Task 13 copes with this challenge in various angles, as a binary detection problem as well as attribution of the source. Specifically, its subtasks also cover generator LLM family detection, as well as a hybrid code co-generated by humans and machines, or adversarially modified codes hiding its origin. Our submitted systems adjusted the existing mdok approach (focused on machine-generated text detection) to these specific kinds of problems by exploring various base models, more suitable for code understanding. The results indicate that the submitted systems are competitive in all three subtasks. However, the margins from the top-performing systems are significant, and thus further improvements are possible.</abstract>
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%0 Conference Proceedings
%T mcdok at SemEval-2026 Task 13: Finetuning LLMs for Detection of Machine-Generated Code
%A Skurla, Adam
%A Macko, Dominik
%A Simko, Jakub
%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 skurla-etal-2026-mcdok
%X Multi-domain detection of the machine-generated code snippets in various programming languages is a challenging task. SemEval-2026 Task 13 copes with this challenge in various angles, as a binary detection problem as well as attribution of the source. Specifically, its subtasks also cover generator LLM family detection, as well as a hybrid code co-generated by humans and machines, or adversarially modified codes hiding its origin. Our submitted systems adjusted the existing mdok approach (focused on machine-generated text detection) to these specific kinds of problems by exploring various base models, more suitable for code understanding. The results indicate that the submitted systems are competitive in all three subtasks. However, the margins from the top-performing systems are significant, and thus further improvements are possible.
%U https://aclanthology.org/2026.semeval-1.47/
%P 322-327

Markdown (Informal)

[mcdok at SemEval-2026 Task 13: Finetuning LLMs for Detection of Machine-Generated Code](https://aclanthology.org/2026.semeval-1.47/) (Skurla et al., SemEval 2026)

• mcdok at SemEval-2026 Task 13: Finetuning LLMs for Detection of Machine-Generated Code (Skurla et al., SemEval 2026)

ACL

• Adam Skurla, Dominik Macko, and Jakub Simko. 2026. mcdok at SemEval-2026 Task 13: Finetuning LLMs for Detection of Machine-Generated Code. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 322–327, San Diego, California, USA. Association for Computational Linguistics.