仅用语法依存标签就能有效识别AI生成文本。
DependencyAI: Detecting AI Generated Text through Dependency Parsing
- 基于语法依存关系标签构建检测模型，无需神经网络。
- 在单语、多生成器、多语言场景下表现优异。
- 可解释性强，揭示了AI文本的典型句法特征。
随着大语言模型日益普及，可靠的方法来检测AI生成文本对于降低潜在风险至关重要。我们提出DependencyAI，一种仅使用语言依存关系标签的简单且可解释的检测方法。该方法在单语、多生成器和多语言设置下均表现出色。为增强可解释性，我们分析了特征重要性，揭示了区分AI生成与人工撰写文本的句法结构。我们还观察到某些模型在未见领域存在系统性过预测，表明生成器特有的写作风格可能影响跨领域泛化能力。总体而言，我们的结果表明，仅依赖依存关系即可提供稳健的检测信号，使DependencyAI成为一种强健的、基于语言学的、可解释的非神经网络基线。
原文摘要 · Abstract (English)
As large language models (LLMs) become increasingly prevalent, reliable methods for detecting AI-generated text are critical for mitigating potential risks. We introduce DependencyAI, a simple and interpretable approach for detecting AI-generated text using only the labels of linguistic dependency relations. Our method achieves competitive performance across monolingual, multi-generator, and multilingual settings. To increase interpretability, we analyze feature importance to reveal syntactic structures that distinguish AI-generated from human-written text. We also observe a systematic overprediction of certain models on unseen domains, suggesting that generator-specific writing styles may affect cross-domain generalization. Overall, our results demonstrate that dependency relations alone provide a robust signal for AI-generated text detection, establishing DependencyAI as a strong linguistically grounded, interpretable, and non-neural network baseline.
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