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arxiv: 2606.04177 · v1 · pith:5QCMAQ42new · submitted 2026-06-02 · 💻 cs.CL · cs.AI

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

Yassir El Attar , Esra D\"onmez , Maximilian Maurer , Agnieszka Falenska This is my paper

Pith reviewed 2026-06-28 09:58 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords AI-generated text detectionlinguistic featureslexical richnesscross-domain generalizationcross-model generalizationinterpretable detectionLLM output analysistext classification
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The pith

Classifiers using linguistic features alone can reliably distinguish AI-generated text from human text, with lexical richness measures remaining robust across models and domains.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tests whether a set of 284 linguistic features can consistently separate text written by people from text produced by large language models. It runs this test on outputs from 27 different LLMs across 10 writing domains and checks performance when models or domains are held out from training. The results show that simple classifiers built only on these features succeed at the distinction task. Most individual features change how well they work depending on the model family or the type of text, but measures that track lexical richness stay effective in every setting examined. Readers care because the work isolates which signals are stable enough to support explanations that non-experts can understand.

Core claim

Classifiers based solely on linguistic features can reliably distinguish AI-generated from human-written text across outputs from 27 LLMs and ten text domains under cross-model and cross-domain generalization settings. Many previously proposed indicators prove strongly context-dependent. Measures of lexical richness remain robust signals across model families and text domains. The results identify which linguistic signals generalize across contexts and supply a foundation for more reliable, interpretable analyses of AI-generated language.

What carries the argument

Systematic measurement of 284 interpretable linguistic features under cross-model and cross-domain generalization to test their reliability as indicators of machine-generated text.

If this is right

  • Classifiers that use only linguistic features achieve reliable separation of AI-generated and human text.
  • Most previously suggested linguistic indicators lose effectiveness when the model family or text domain changes.
  • Lexical richness measures continue to function as stable signals across all tested model families and domains.
  • The identified stable features can serve as the basis for more interpretable detection methods.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Detection tools could be simplified by focusing primarily on lexical richness features when generalization to new models is required.
  • The same feature-evaluation approach could be repeated on newer model releases to check whether the robustness pattern persists.
  • Similar large-scale tests on non-English text or on specialized domains such as legal or medical writing would reveal whether the same features remain dominant.

Load-bearing premise

The 10 chosen text domains and 27 LLMs are representative enough of broader model and domain variation to support claims of cross-model and cross-domain generalization.

What would settle it

An experiment on a fresh collection of domains or on LLMs released after the study in which lexical richness measures lose their ability to separate AI-generated from human text would falsify the robustness result.

Figures

Figures reproduced from arXiv: 2606.04177 by Agnieszka Falenska, Esra D\"onmez, Maximilian Maurer, Yassir El Attar.

Figure 1
Figure 1. Figure 1: (left), reveal similar patterns to TB5: clas￾sifiers performing worse for GLM, Eleuther, Big￾Science, and OpenAI (48.1%, 51.0%, 57.6%, and 59.7%), and better for OPT, FLAN, and LLaMA on average (68.4%, 63.7%, 62.8%). Text Domain Effects [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Macro F1 performance changes with cumulative feature area removal. The left pane shows results for [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Ablation results on TB2. The horizontal dashed lines indicate the baseline results (all features) and the bars indicate the performance change in Macro F1 after removing the corresponding feature area. Surface Lexi. Rich. Emotion Psych. Readab. Morpho. POS Depend. Semant. Entities Inform. 0.4 0.5 0.6 0.7 Baseline (0.609) [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Ablation performance ranges on TB5 across the 11 feature areas after removing the corresponding feature area compared to the baseline (dashed red line). shows interesting patterns (see [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Macro F1 results of ablation study, top pane: [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Ablation performance variance across 16 text [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Visualization of results of ablation study on [PITH_FULL_IMAGE:figures/full_fig_p017_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Distribution of F1-Macro scores across feature area ablations for in-distribution (TB3) and out-of [PITH_FULL_IMAGE:figures/full_fig_p019_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Visualization of results of ablation study [PITH_FULL_IMAGE:figures/full_fig_p019_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Change in standard deviation between unseen ( [PITH_FULL_IMAGE:figures/full_fig_p021_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Leave-one-out features ablation performance [PITH_FULL_IMAGE:figures/full_fig_p021_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Results of the standard deviation between ab [PITH_FULL_IMAGE:figures/full_fig_p021_12.png] view at source ↗
Figure 14
Figure 14. Figure 14: The difference in the performance for the [PITH_FULL_IMAGE:figures/full_fig_p022_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Performance difference (F1-Macro) between lexical-richness-only and full-feature classifiers. [PITH_FULL_IMAGE:figures/full_fig_p023_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Heatmap of pairwise Pearson’s r of TTR distributions between selected model family pairs across text domains. The left columns show GLM paired with smaller models, while the right columns show OpenAI paired with models of varying scale. Cell values indicate the Pearson’s r between the TTR distributions of the two model families within each text domain. all 70 pairs (Figure 15a). The worst drops occur with… view at source ↗
Figure 17
Figure 17. Figure 17: Density distributions of lexical richness features (TTR, hapax legomena, lexical density) across (a) [PITH_FULL_IMAGE:figures/full_fig_p024_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Violin plots for the three lexical richness features: TTR (left), hapax legomena (middle), and lexical [PITH_FULL_IMAGE:figures/full_fig_p025_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Parallel human–AI text pairs from the Yelp domain (AI model: Text-davinci-003) illustrating differences [PITH_FULL_IMAGE:figures/full_fig_p028_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Parallel human-AI text pairs from the XSum domain illustrating differences in lexical richness features. [PITH_FULL_IMAGE:figures/full_fig_p029_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: Clustered heatmap of pairwise Wasserstein distances for the top 10 most discriminative linguistic features [PITH_FULL_IMAGE:figures/full_fig_p030_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: Pairwise Pearson’s r of TTR feature distributions across model family pairs, with distributions computed over the ten text domains. Pairs are grouped into three categories: Smaller–Smaller (blue; e.g., FLAN-T5 vs. OPT), Smaller–LLaMA/OpenAI (orange; e.g., Eleuther vs. LLaMA), and LLaMA–OpenAI (green). TTR was selected as the focal feature given its pronounced human–AI distributional separation observed in… view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript presents a large-scale empirical study of 284 linguistic features drawn from outputs of 27 LLMs across 10 text domains. It evaluates their utility for distinguishing AI-generated from human-written text under cross-model and cross-domain generalization settings, concluding that feature-based classifiers can reliably detect AI text overall, but that most previously proposed indicators are context-dependent while measures of lexical richness remain robust across model families and domains.

Significance. If the sampling is representative and the empirical results are statistically substantiated, the work would be significant for consolidating fragmented prior findings on linguistic indicators of machine-generated text and for identifying a small set of generalizable, interpretable features (lexical richness) that could support more reliable detection and explanation.

major comments (2)
  1. [Abstract] Abstract: the headline claim that lexical-richness measures 'remain robust signals across model families and text domains' is load-bearing on the assumption that the chosen 10 domains and 27 LLMs adequately sample the space of possible domains and model families; the manuscript supplies no analysis or justification of domain diversity, genre coverage, or model-family/size distribution, so the observed robustness could be an artifact of the limited sample rather than a general property.
  2. [Experimental setup] Experimental setup (methods description): the abstract states clear empirical findings on classifier reliability and feature robustness, yet provides no information on statistical tests, train/test splits for the cross-model and cross-domain settings, feature-extraction pipelines, or controls for confounds such as domain-topic overlap; without these details the central generalization claims cannot be verified.
minor comments (1)
  1. [Abstract] Abstract: the selection process and categorization of the 284 features are not summarized, which would help readers assess the scope of the feature set.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below and indicate the revisions we will make to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the headline claim that lexical-richness measures 'remain robust signals across model families and text domains' is load-bearing on the assumption that the chosen 10 domains and 27 LLMs adequately sample the space of possible domains and model families; the manuscript supplies no analysis or justification of domain diversity, genre coverage, or model-family/size distribution, so the observed robustness could be an artifact of the limited sample rather than a general property.

    Authors: We agree that the abstract's generalization claim requires explicit support for the sampling choices. In the revised version we will add a dedicated subsection in the Methods that justifies the selection of the 10 domains (spanning news, fiction, academic writing, technical documentation, conversational, and opinion genres) and the 27 LLMs (covering multiple families and parameter scales). We will also insert a Limitations paragraph that acknowledges the finite scope of the sample and the possibility that robustness could be narrower than claimed. These additions will make the headline statement better grounded. revision: yes

  2. Referee: [Experimental setup] Experimental setup (methods description): the abstract states clear empirical findings on classifier reliability and feature robustness, yet provides no information on statistical tests, train/test splits for the cross-model and cross-domain settings, feature-extraction pipelines, or controls for confounds such as domain-topic overlap; without these details the central generalization claims cannot be verified.

    Authors: We will substantially expand the Experimental Setup section to supply the missing methodological details. The revision will explicitly describe the statistical tests performed, the precise train/test partitioning and cross-validation procedures used for the leave-one-model-out and leave-one-domain-out evaluations, the full feature-extraction pipeline (including libraries, preprocessing steps, and parameter settings), and the measures taken to mitigate domain-topic confounds (such as topic-balance checks). These clarifications will render the generalization results reproducible and verifiable. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical study with no derivations or self-referential steps

full rationale

The paper performs a large-scale empirical analysis of 284 linguistic features across 27 LLMs and 10 domains, reporting classifier performance under cross-model and cross-domain settings. No equations, derivations, fitted parameters renamed as predictions, or self-citation chains appear in the abstract or described methodology. The central claims rest on observed experimental outcomes rather than any reduction to inputs by construction. The representativeness of the sampled domains and models is an external validity concern, not a circularity issue.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that the selected linguistic features can be extracted uniformly and that the chosen models and domains capture the relevant variation in AI-generated text.

free parameters (1)
  • selection of 284 linguistic features
    The specific set of features included is a modeling choice that determines which signals are tested.
axioms (1)
  • domain assumption Linguistic features extracted from text are stable and comparable across different models and domains
    Invoked when claiming cross-model and cross-domain robustness in the abstract.

pith-pipeline@v0.9.1-grok · 5696 in / 1214 out tokens · 24321 ms · 2026-06-28T09:58:43.572594+00:00 · methodology

discussion (0)

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