[2605.04157] FMI_SU_Yotkova_Kastreva at SemEval-2026 Task 13: Lightweight Detection of LLM-Generated Code via Stylometric Signals
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Computer Science > Computation and Language
arXiv:2605.04157 (cs)
[Submitted on 5 May 2026]
Title:FMI_SU_Yotkova_Kastreva at SemEval-2026 Task 13: Lightweight Detection of LLM-Generated Code via Stylometric Signals
Authors:Elitsa Yotkova, Violeta Kastreva, Dimitar Dimitrov, Ivan Koychev, Preslav Nakov
View a PDF of the paper titled FMI_SU_Yotkova_Kastreva at SemEval-2026 Task 13: Lightweight Detection of LLM-Generated Code via Stylometric Signals, by Elitsa Yotkova and Violeta Kastreva and Dimitar Dimitrov and Ivan Koychev and Preslav Nakov
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Abstract:SemEval-2026 Task 13 investigates machine-generated code detection across multiple programming languages and application scenarios, asking participating systems to generalize to unseen languages and domains. This paper describes our participation in Subtask A (binary classification) and explores both pretrained code encoders and lightweight feature-based methods. We design ratio-based features that are less sensitive to snippet length. To support the extraction of descriptiveness-related signals, we use parsing engines and a programming-language classifier. Additionally, we train a separate code-vs-text line classifier to identify raw natural language segments embedded within samples. We combine a shallow decision tree with heuristic rules derived from data analysis to produce the final predictions. Our approach is computationally efficient, requires only CPU resources for training, and achieves near-instant inference time, offering a lightweight alternative to large pretrained models.
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Computation and Language (cs.CL)
Cite as: arXiv:2605.04157 [cs.CL]
(or arXiv:2605.04157v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.04157
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arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Violeta Kastreva Ms. [view email] [v1] Tue, 5 May 2026 18:00:19 UTC (480 KB)
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