微调大模型检测代码生成来源，覆盖多语言与对抗性修改。
mcdok at SemEval-2026 Task 13: Finetuning LLMs for Detection of Machine-Generated Code
- 基于代码理解优化的基模型，适配机器生成代码检测任务。
- 在三个子任务中表现竞争力，但与顶尖系统仍有明显差距。
- 适合关注代码溯源、对抗样本防御的研究者参考。
跨领域、多编程语言的机器生成代码片段检测是一项挑战性任务。SemEval-2026 Task 13 从多个角度应对该挑战，包括二分类检测、生成源归属识别，以及针对不同大模型家族生成器的识别，还涵盖人类与机器混合生成或经对抗修改以隐藏来源的代码。我们提交的系统对原有的 mdok 方法（专注机器生成文本检测）进行了调整，探索了更适用于代码理解的多种基础模型。结果表明，所提系统在全部三项子任务中均表现出竞争力。然而，与最优系统相比仍存在显著差距，说明仍有提升空间。
原文摘要 · Abstract (English)
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.
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