DependencyAI: Detecting AI Generated Text through Dependency Parsing
This addresses the need for reliable detection methods to mitigate risks from LLMs, though it is incremental as it builds on existing detection approaches.
The paper tackled the problem of detecting AI-generated text by introducing DependencyAI, a method using linguistic dependency relations, achieving competitive performance across various settings.
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.