A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models
A large-scale empirical study assesses 284 interpretable linguistic features across 27 LLMs and ten text domains, finding that classifiers using only linguistic features can reliably distinguish AI-generated from human text, but many features are context-dependent while lexical richness remains robust across models and domains.
[2606.04177] A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models
[Submitted on 2 Jun 2026]
Title:A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models
View a PDF of the paper titled A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models, by Yassir El Attar and 3 other authors
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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.
Comments: preprint
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.04177 [cs.CL]
(or arXiv:2606.04177v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.04177
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Esra Dönmez [view email] [v1] Tue, 2 Jun 2026 19:46:22 UTC (8,530 KB)
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