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2026 SemEval SemEval 2026

CodeHunters at SemEval-2026 Task 13: Detecting Machine-Generated Code with Multiple Programming Languages, Generators, and Application Scenarios

Abstract

AbstractWe participated in Subtasks A and B, where we fine-tuned 3 different pre-trained models (UniXCoder, CodeT5 and codeBERT). The paper describes the detailed approach for both of the subtasks.

🌉 Interdisciplinary Bridge - Artificial Intelligence and Natural Language Processing

Authors

Daniel-Antoniu Dumitru, Simina Lazăr, Nicoleta Danilă (amargheoalei), Daniela Gifu, Diana Trandabat

Topics

Deep Learning > Learning Types > Fine-Tuning Natural Language Processing > Applications > Code Generation Artificial Intelligence > Core AI > Code Generation

Keywords

pre-trained model code language model machine-generated code code generation detection
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