三种方法检测AI生成文本，基于RoBERTa的模型效果最佳。
A Multi-Strategy Approach for AI-Generated Text Detection
- 用微调的RoBERTa、TF-IDF+SVM和自研集成模型检测AI文本。
- RoBERTa模型在开发集和测试集上接近完美准确率。
- 适合关注AI内容检测与自然语言处理的研究者参考。
本文针对M-DAIGT共享任务，提出了三种用于检测新闻文章和学术摘要中AI生成内容的系统：(1) 微调的RoBERTa-base分类器，(2) 经典的TF-IDF + 支持向量机（SVM）分类器，(3) 一种名为Candace的创新集成模型，该模型利用多个Llama-3.2模型提取的概率特征，并通过自定义Transformer编码器进行融合。其中，基于RoBERTa的系统表现最优，在开发集和测试集上均达到近乎完美的检测效果。
原文摘要 · Abstract (English)
This paper presents presents three distinct systems developed for the M-DAIGT shared task on detecting AI generated content in news articles and academic abstracts. The systems includes: (1) A fine-tuned RoBERTa-base classifier, (2) A classical TF-IDF + Support Vector Machine (SVM) classifier , and (3) An Innovative ensemble model named Candace, leveraging probabilistic features extracted from multiple Llama-3.2 models processed by a customTransformer encoder.The RoBERTa-based system emerged as the most performant, achieving near-perfect results on both development and test sets.
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