A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models

• URL: http://arxiv.org/abs/2606.04177v1
• Date: Tue, 02 Jun 2026 19:46:22 GMT
• Title: A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models
• Abstract summary: Interpretable linguistic features offer a promising approach for explaining why a given text appears machine-generated.<n>We conduct a large-scale empirical study assessing the robustness of linguistic signals for characterizing AI-generated text.
• Score: 5.859949662088513
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Interpretable linguistic features offer a promising approach for explaining why a given text appears machine-generated, particularly for non-expert users. However, existing findings on which features reliably indicate LLM-generated text remain fragmented across feature sets, models, and text domains. To address this gap, we conduct a large-scale empirical study assessing the robustness of linguistic signals for characterizing AI-generated text. Our analysis covers 284 interpretable linguistic features across outputs from 27 LLMs and ten text domains under cross-model and cross-domain generalization settings. We show that classifiers based solely on linguistic features can reliably distinguish AI-generated from human-written text. However, many previously proposed indicators prove strongly context-dependent, with the exception of measures of lexical richness, which remain robust signals across model families and text domains. These results demonstrate which linguistic signals generalize across contexts and provide a foundation for more reliable, interpretable analyses of AI-generated language.

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