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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Public Library of Science
2023
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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. |
format | Online Article Text |
id | pubmed-10411719 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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