Feature-based detection of automated language models: tackling GPT-2, GPT-3 and Grover
Open accessAbstract
The recent improvements of language models have drawn much attention to potential cases of use and abuse of automatically generated text. Great effort is put into the development of methods to detect machine generations among human-written text in order to avoid scenarios in which the large-scale generation of text with minimal cost and effort undermines the trust in human interaction and factual information online. While most of the current approaches rely on the availability of expensive language models, we propose a simple feature-based classifier for the detection problem, using carefully crafted features that attempt to model intrinsic differences between human and machine text. Our research contributes to the field in producing a detection method that achieves performance competitive with far more expensive methods, offering an accessible "first line-of-defense" against the abuse of language models. Furthermore, our experiments show that different sampling methods lead to different types of flaws in generated text.
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Quantitative comparisons have looked mainly at basic grammar and syntax or features such as word choice, punctuation, and sentence length, finding evidence of some differences between human- and LLM-written text.
“So far, quantitative comparisons have looked mainly at basic grammar and syntax (5) or features such as word choice, punctuation, sentence length, and so on, finding evidence of some differences between human- and LLM-written text (10–14).”
From Do LLMs write like humans? Variation in grammatical and rhetorical styles (2025) · cites this paper as [11]
There are structural differences between human- and LLM-written text.
“Though not definitive, these results suggest there are indeed structural differences between human- and LLM-written text.”
From Do LLMs write like humans? Variation in grammatical and rhetorical styles (2025) · cites this paper as [11]
Detectors for machine-created text may potentially discriminate against EFL students by incorrectly identifying human created text as machine written.
“highlight the ethical challenges of deploying any such detectors which may potentially discriminate against EFL students by incorrectly identifying human created text as machine written”
From Academic Integrity considerations of AI Large Language Models in the post-pandemic era: ChatGPT and beyond (2023) · cites this paper as Fröhling & Zubiaga (2021)
AI tools can aid the researcher in collecting relevant research articles, identifying knowledge gaps in the literature, formulating research questions, recommending appropriate statistical methods for the available data, creating a graphical representation of the data, and manuscript writing.
“AI tools can aid the researcher in collecting relevant research articles, identifying the knowledge gaps in the literature, formulating research questions, recommending appropriate statistical methods for the available data, creating a graphical representation of the data, and manuscript writing [32-35].”
From Artificial Intelligence Revolutionizing the Field of Medical Education (2023) · cites this paper as [34]
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Towards an understanding and explanation for mixed-initiative artificial scientific text detection
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DEMASQ: Unmasking the ChatGPT Wordsmith
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A Novel Approach to Automated Detection of AI-Generated Text
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Towards AI-Generated Essay Classification Using Numerical Text Representation
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The Limits of AI Content Detectors
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MODERN THOUGHTS ON THE DEVELOPMENT OF SCIENCE: IDEAS, TECHNOLOGIES AND THEORIES
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GigaCheck: Detecting LLM-generated ContentPreprint
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Investigating Plagiarism in Second Language Writing
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Detecting authorship between generative AI models and humans: a Burrows’s Delta approach
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Bilingual generated text detection through semantic and statistical analysis
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Unmasking the AI Hand: A Machine Learning Approach to Deciphering Authorship
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Classifying AI-generated summaries And Human Summaries Based on Statistical Features
Mathews, D., Varghese, J. P., & Samuel, L. C. (2024) · International Conference on Trends in Quantum Computing and Emerging Business Technologies (TQCEBT) · doi:10.1109/tqcebt59414.2024.10545131 · Cited by 3 · see citing works
To Write or Not to Write as a Machine? That’s the Question
Sepúlveda-Torres, R., Martínez-Murillo, I., Saquete, E., Lloret, E., & Palomar, M. (2025) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2025.3536938 · Cited by 2 · see citing works
Offsite construction risk management agent – a GPTs based analysis model
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Watermarking Fine-Tuning Datasets for Robust Provenance
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A Survey on Writing Style Change Detection: Current Literature and Future Directions
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Utilizing Latent Diffusion Model to Accelerate Sampling Speed and Enhance Text Generation Quality
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Decoding AI-Generated Content Through Human Heuristics
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Using AI to control AI-assisted plagiarism in L2 writing: a critical review of the literature
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Can we trust the source? A systematic review of watermarking and attribution for AI-generated code
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Foundation Models for Text Generation
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How Generative Models Are Ruining Themselves
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GPT-3: Die Zukunft studentischer Hausarbeiten oder eine Bedrohung der wissenschaftlichen Integrität?
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- Authors
- Fröhling, L., & Zubiaga, A.
- Affiliations
- Leibniz University Hannover (DE)
- Queen Mary University of London (GB)
- Corresponding author
- Fröhling, Leon
- Venue
- PeerJ Computer Science
- Publisher
- PeerJ
- Published
- 2021-04-06
- Volume
- 7
- Pages
- e443
- ISSN
- 2376-5992 (ISSN-L)
- PMC
- PMC8049133
- OpenAlex
- W3139815689
- Cited by
- 106
- References
- 48
- Citations by year
- 2026: 11 · 2025: 34 · 2024: 30 · 2023: 25 · 2022: 6
- FWCI
- 5.37
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PeerJ Computer Science (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Institutional Repository of Leibniz Universität Hannover (Leibniz Universität Hannover) (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Institutional Repository of Leibniz Universität Hannover (Leibniz Universität Hannover) (submittedVersion, cc-by)
- Leibniz Universität Hannover (cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Keywords
- AI-generated text detection, feature-based classifiers, GPT-2, GPT-3, GROVER, sampling methods
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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