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Distinguishing ChatGPT(-3.5, -4)-generated and human-written papers through Japanese stylometric analysis

In the first half of 2023, text-generative artificial intelligence (AI), including ChatGPT from OpenAI, has attracted considerable attention worldwide. In this study, first, we compared Japanese stylometric features of texts generated by ChatGPT, equipped with GPT-3.5 and GPT-4, and those written by...

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Autores principales: Zaitsu, Wataru, Jin, Mingzhe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10411719/
https://www.ncbi.nlm.nih.gov/pubmed/37556434
http://dx.doi.org/10.1371/journal.pone.0288453
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author Zaitsu, Wataru
Jin, Mingzhe
author_facet Zaitsu, Wataru
Jin, Mingzhe
author_sort Zaitsu, Wataru
collection PubMed
description In the first half of 2023, text-generative artificial intelligence (AI), including ChatGPT from OpenAI, has attracted considerable attention worldwide. In this study, first, we compared Japanese stylometric features of texts generated by ChatGPT, equipped with GPT-3.5 and GPT-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 overlapping. 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) classifier 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.
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spelling pubmed-104117192023-08-10 Distinguishing ChatGPT(-3.5, -4)-generated and human-written papers through Japanese stylometric analysis Zaitsu, Wataru Jin, Mingzhe PLoS One Research Article In the first half of 2023, text-generative artificial intelligence (AI), including ChatGPT from OpenAI, has attracted considerable attention worldwide. In this study, first, we compared Japanese stylometric features of texts generated by ChatGPT, equipped with GPT-3.5 and GPT-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 overlapping. 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) classifier 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. Public Library of Science 2023-08-09 /pmc/articles/PMC10411719/ /pubmed/37556434 http://dx.doi.org/10.1371/journal.pone.0288453 Text en © 2023 Zaitsu, Jin https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Zaitsu, Wataru
Jin, Mingzhe
Distinguishing ChatGPT(-3.5, -4)-generated and human-written papers through Japanese stylometric analysis
title Distinguishing ChatGPT(-3.5, -4)-generated and human-written papers through Japanese stylometric analysis
title_full Distinguishing ChatGPT(-3.5, -4)-generated and human-written papers through Japanese stylometric analysis
title_fullStr Distinguishing ChatGPT(-3.5, -4)-generated and human-written papers through Japanese stylometric analysis
title_full_unstemmed Distinguishing ChatGPT(-3.5, -4)-generated and human-written papers through Japanese stylometric analysis
title_short Distinguishing ChatGPT(-3.5, -4)-generated and human-written papers through Japanese stylometric analysis
title_sort distinguishing chatgpt(-3.5, -4)-generated and human-written papers through japanese stylometric analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10411719/
https://www.ncbi.nlm.nih.gov/pubmed/37556434
http://dx.doi.org/10.1371/journal.pone.0288453
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