DependencyAI: Detecting AI Generated Text through Dependency Parsing

• URL: http://arxiv.org/abs/2602.15514v1
• Date: Tue, 17 Feb 2026 11:42:28 GMT
• Title: DependencyAI: Detecting AI Generated Text through Dependency Parsing
• Authors: Sara Ahmed, Tracy Hammond,
• Abstract summary: We introduce DependencyAI, a simple and interpretable approach for detecting AI-generated text.<n>Our method achieves competitive performance across monolingual, multi-generator, and multilingual settings.
• Score: 10.075606234222963
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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