Abstract
The large language models (LLMs) are able to generate high-quality texts in multiple languages. Such texts are often not recognizable by humans as generated, and therefore present a potential of LLMs for misuse (e.g., plagiarism, spams, disinformation spreading). An automated detection is able to assist humans to indicate the machine-generated texts; however, its robustness to out-of-distribution data is still challenging. This notebook describes our mdok approach in robust detection, based on fine-tuning smaller LLMs for text classification. It is applied to both subtasks of Voight-Kampff Generative AI Detection 2025, providing remarkable performance in binary detection as well as in multiclass (1st rank) classification of various cases of human-AI collaboration.
Abstract (translated)
大型语言模型（LLMs）能够生成多语言的高质量文本。这些文本通常难以被人类识别为机器生成，因此它们存在被滥用的风险（例如抄袭、垃圾信息和虚假信息传播）。自动检测可以辅助人类识别机器生成的文本；然而，它在处理分布外数据时仍然具有挑战性。本笔记本描述了我们基于微调小型LLMs进行文本分类的方法mdok，在鲁棒性检测方面的应用。这种方法被应用于Voight-Kampff 2025生成AI检测竞赛的两个子任务中，并在二元检测和多类（第一名）分类的各种人类-AI合作案例中表现出显著性能。
URL
https://arxiv.org/abs/2506.01702
