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arXiv · 2606.25102

Dream at SemEval-2026 Task 13: SALSA for Single-Pass Machine-Generated Code Detection

Ruslan Berdichevsky·Shai Nahum-Gefen·Elad Ben-Zaken
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Abstract

Large language models have transformed code generation, raising concerns around authorship, assessment integrity, and software trust. SemEval-2026 Task 13 Subtask A operationalizes detection as binary classification over code snippets, with a particular emphasis on out-of-distribution (OOD) generalization across unseen programming languages and application domains. We propose a SALSA-style formulation, Single-pass Autoregressive LLM Structured Classification, that maps each class to a dedicated output token and trains the model to emit a single-token label in a structured response. Rather than engineering hand-crafted features or decision rules, this formulation delegates the authorship decision to the model. To improve OOD robustness, we combine balanced sampling across languages with parameter-efficient fine-tuning and conservative training (low learning rate, single epoch) to avoid overfitting to the training domain. Our best system achieves OOD $F_1 = 0.789$ on the official leaderboard, substantially outperforming the CodeBERT baseline ($F_1 = 0.305$).

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Ruslan Berdichevsky, Shai Nahum-Gefen, Elad Ben-Zaken. 2026-06-23. Dream at SemEval-2026 Task 13: SALSA for Single-Pass Machine-Generated Code Detection. https://arxiv.org/abs/2606.25102

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