GPT-assisted writing shows higher lexical diversity, richness, and noun/non-stopword ratios, while independently authored writing shows higher adverb ratio and sentence-length variability¶
Claim · All claims
Evidence · 1 study · causalr1·q2·i3large
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q2 i3 Across 90 participants, paired t-tests showed GPT-assisted documents had higher TTR (r = .43), Hapax Ratio (r = .46), Word Entropy (r = .42), NSR (r = .50), Noun Ratio (r = .47), Verb Ratio, and Adjective Ratio, while independently authored documents had higher Adverb Ratio (r = -.34) and Sentence-Length Variability (r = -.13). → Rajesh Kumar 2026
Evidence¶
Rajesh Kumar 2026¶
Rajesh Kumar, Nabeel Siddiqui, Alexander Fuchsberger. (2026). Detecting GPT-Assisted Writing Using Interpretable Stylometric Features. https://arxiv.org/abs/2609.26687 doi:10.48550/arxiv.2609.26687
q2 · i3 · causal · r1
Paired t-tests and point-biserial correlations on document-level median feature values across 90 participants (Table 2). NSR showed the strongest positive association (r = 0.50) and Adverb Ratio the strongest negative (r = -0.34), described as "the strongest negative association".
"GPT-assisted documents have higher TTR, Hapax Ratio, Word Entropy, NSR, Noun Ratio, Verb Ratio, and Adjective Ratio, while independently authored documents have higher Adverb Ratio and Sentence-Length Variability."
Discussion¶
Related Claims¶
- SHAP analysis identifies Hapax Ratio as the most influential stylometric feature in Random Forest predictions, followed by NSR, Noun Ratio, Adverb Ratio, and TTR — related
- Random Forest using nine interpretable stylometric features detects GPT-assisted student writing with held-out ROC-AUC of 0.870 and F1 of 0.842 — related
- AI-assisted journal entries show higher lexical diversity and more emotion-word usage than unassisted entries — possibly the same claim (merge candidate)
- Humanization works by systematically suppressing detector-salient surface cues — long-token ratio, AWL density, and lexical diversity — rather than fragmenting syntax — related
- Type-token ratio showed a non-monotonic relationship with predicted difficulty, with both very low and very high lexical diversity increasing predicted text difficulty — related
- Elevated detector scores track long-token and Academic Word List density, with numeric and non-alphabetic token ratios correlating negatively — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
stylometric-feature-differences-gpt-vs-human |
title |
GPT-assisted writing shows higher lexical diversity, richness, and noun/non-stopword ratios, while independently authored writing shows higher adverb ratio and sentence-length variability |
description |
GPT-assisted writing shows higher lexical diversity, richness, and noun/non-stopword ratios, while independently authored writing shows higher adverb ratio and sentence-length variability |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2026 |
generated |
by process:wiki-ingest on 2026-10-10 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — rajesh-kumar-2026 |
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