A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models
Quick Answer
This paper shows that A large-scale study reveals that 284 linguistic features can effectively distinguish AI-generated text from human-written text across 27 LLMs and ten domains.
Quick Take
While many indicators are context-dependent, measures of lexical richness consistently serve as robust signals, enhancing interpretability for non-experts.
Key Points
- Study assesses 284 linguistic features across 27 and ten text domains.
- Classifiers based on linguistic features reliably distinguish AI and human text.
- Lexical richness measures are robust across different models and domains.
- Findings address gaps in understanding AI-generated text characteristics.
- Results support more reliable analyses of AI-generated language.
Paper Resources
Source Excerpt
From the original publisher, up to about 700 charactersarXiv:2606. 04177v1 Announce Type: new 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 -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. …
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