用语言特征区分真人与AI写作，效果优于现有方法。
SKDU at De-Factify 4.0: Natural Language Features for AI-Generated Text-Detection
- 结合提示重写和内容特征提取，构建文本检测流水线。
- NELA特征在两类任务中均显著优于RAIDAR特征。
- XGBoost表现最佳，适合需要高精度识别的场景。
大型语言模型的快速发展带来了区分人工撰写与人工智能生成内容的新挑战。本文提出一种分步式AI生成文本检测方法，包含特征提取（基于RAIDAR的提示重写特征与来自NELA工具包的内容特征）和分类模块。在Defactify4.0数据集上进行的全面实验评估了两类任务：二分类任务（区分人工与AI文本）和多分类任务（识别具体生成模型）。结果表明，NELA特征在两项任务中均显著优于RAIDAR特征，展现出对语言、风格及内容差异的敏感捕捉能力。结合两者特征仅带来微弱提升，说明低判别力特征存在冗余。测试中XGBoost表现最优，能有效利用丰富特征实现高准确率与良好泛化性能。
原文摘要 · Abstract (English)
The rapid advancement of large language models (LLMs) has introduced new challenges in distinguishing human-written text from AI-generated content. In this work, we explored a pipelined approach for AI-generated text detection that includes a feature extraction step (i.e. prompt-based rewriting features inspired by RAIDAR and content-based features derived from the NELA toolkit) followed by a classification module. Comprehensive experiments were conducted on the Defactify4.0 dataset, evaluating two tasks: binary classification to differentiate human-written and AI-generated text, and multi-class classification to identify the specific generative model used to generate the input text. Our findings reveal that NELA features significantly outperform RAIDAR features in both tasks, demonstrating their ability to capture nuanced linguistic, stylistic, and content-based differences. Combining RAIDAR and NELA features provided minimal improvement, highlighting the redundancy introduced by less discriminative features. Among the classifiers tested, XGBoost emerged as the most effective, leveraging the rich feature sets to achieve high accuracy and generalisation.
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