Distinguishing ChatGPT(-3.5, -4)-generated and human-written papers through Japanese stylometric analysis
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Abstract (arXiv)
In the first half of 2023, text-generative artificial intelligence (AI), including ChatGPT, equipped with GPT-3.5 and GPT-4, from OpenAI, has attracted considerable attention worldwide. In this study, first, we compared Japanese stylometric features of texts generated by GPT (-3.5 and -4) and those written by humans. In this work, we performed multi-dimensional scaling (MDS) to confirm the distributions of 216 texts of three classes (72 academic papers written by 36 single authors, 72 texts generated by GPT-3.5, and 72 texts generated by GPT-4 on the basis of the titles of the aforementioned papers) focusing on the following stylometric features: (1) bigrams of parts-of-speech, (2) bigram of postpositional particle words, (3) positioning of commas, and (4) rate of function words. MDS revealed distinct distributions at each stylometric feature of GPT (-3.5 and -4) and human. Although GPT-4 is more powerful than GPT-3.5 because it has more parameters, both GPT (-3.5 and -4) distributions are likely to overlap. These results indicate that although the number of parameters may increase in the future, GPT-generated texts may not be close to that written by humans in terms of stylometric features. Second, we verified the classification performance of random forest (RF) for two classes (GPT and human) focusing on Japanese stylometric features. This study revealed the high performance of RF in each stylometric feature: The RF classifier focusing on the rate of function words achieved 98.1% accuracy. Furthermore the RF classifier focusing on all stylometric features reached 100% in terms of all performance indexes (accuracy, recall, precision, and F1 score). This study concluded that at this stage we human discriminate ChatGPT from human limited to Japanese language.
Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). Builds on 4 of its 12 references that are in the map. First posted on arXiv 11 Apr 2023.
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References · 4
- 2023How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detectioncited by 481
- 2021All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Textcited by 287
- 2023ChatGPT or Human? Detect and Explain. Explaining Decisions of Machine Learning Model for Detecting Short ChatGPT-generated Textcited by 85
- 2023AI vs. Human -- Differentiation Analysis of Scientific Content Generationcited by 21
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Citing papers per year: 2023: 3; 2024: 4; 2025: 7; 2026: 2.
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- 2025Missing the human touch? A computational stylometry analysis of GPT-4 translations of online Chinese literature16 Jun 2025
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- 2024Fine-tuning Large Language Models for Multigenerator, Multidomain, and Multilingual Machine-Generated Text Detection22 Jan 2024
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- 2023Classification of Human- and AI-Generated Texts: Investigating Features for ChatGPT10 Aug 2023
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- 2025Idiosyncrasies in Large Language Modelscited by 33
- 2023Classification of Human- and AI-Generated Texts: Investigating Features for ChatGPTcited by 19
- 2025Linguistic Characteristics of AI-Generated Text: A Surveycited by 7
- 2024GenAI Content Detection Task 2: AI vs. Human -- Academic Essay Authenticity Challengecited by 6
- 2025A Lightweight Approach to Detection of AI-Generated Texts Using Stylometric Featurescited by 5
- 2023Classification of Human- and AI-Generated Texts for English, French, German, and Spanishcited by 4
- 2024Using Machine Learning to Distinguish Human-written from Machine-generated Creative Fictioncited by 4
- 2024Fine-tuning Large Language Models for Multigenerator, Multidomain, and Multilingual Machine-Generated Text Detectioncited by 3
- 2025On the Detectability of LLM-Generated Text: What Exactly Is LLM-Generated Text?cited by 3
- 2026AI-Associated Lexical Shifts Across 34 Languages: Cross-Lingual Convergence and Diachronic Uptake in News Writingcited by 2
- 2025Integrated ensemble of BERT- and features-based models for authorship attribution in Japanese literary workscited by 1
12 most cited of 13 All 13 (JSON)
- 2026AI-Associated Lexical Shifts Across 34 Languages: Cross-Lingual Convergence and Diachronic Uptake in News Writing25 May 2026
- 2025A Lightweight Approach to Detection of AI-Generated Texts Using Stylometric Features22 Nov 2025
- 2025On the Detectability of LLM-Generated Text: What Exactly Is LLM-Generated Text?23 Oct 2025
- 2025Linguistic Characteristics of AI-Generated Text: A Survey1 Oct 2025
- 2025Missing the human touch? A computational stylometry analysis of GPT-4 translations of online Chinese literature16 Jun 2025
- 2025Integrated ensemble of BERT- and features-based models for authorship attribution in Japanese literary works11 Apr 2025
- 2025Idiosyncrasies in Large Language Models17 Feb 2025
- 2024GenAI Content Detection Task 2: AI vs. Human -- Academic Essay Authenticity Challenge24 Dec 2024
- 2024Using Machine Learning to Distinguish Human-written from Machine-generated Creative Fiction15 Dec 2024
- 2024Your Large Language Models Are Leaving Fingerprints22 May 2024
- 2024Fine-tuning Large Language Models for Multigenerator, Multidomain, and Multilingual Machine-Generated Text Detection22 Jan 2024
- 2023Classification of Human- and AI-Generated Texts for English, French, German, and Spanish8 Dec 2023
12 newest of 13 All 13 (JSON)