用微调小模型实现高鲁棒性文本生成检测，双赛道夺冠
mdok of KInIT: Robustly Fine-tuned LLM for Binary and Multiclass AI-Generated Text Detection
- 基于小规模语言模型微调，提升生成文本检测鲁棒性
- 在二分类与多分类任务中均获第一名，准确率领先
- 适合需要可靠检测生成内容的平台与审核场景
大型语言模型（LLMs）能生成高质量多语言文本，这些文本常难以被人类识别为机器生成，存在被滥用于抄袭、垃圾信息和虚假传播的风险。自动化检测可辅助识别生成文本，但对分布外数据的鲁棒性仍是挑战。本文提出mdok方法，通过微调小型语言模型进行文本分类，在Voight-Kampff Generative AI Detection 2025竞赛的两个子任务中表现优异：在二分类检测和多类人机协作生成文本分类任务中均获得第一名。
原文摘要 · Abstract (English)
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 (1st rank) in both, the binary detection as well as the multiclass classification of various cases of human-AI collaboration.
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