Predictive model on detecting ChatGPT responses against human responses

Research Article
Open access

Predictive model on detecting ChatGPT responses against human responses

Zhaokai He 1 , Ruolong Mao 2 , Yu Liu 3*
  • 1 School of Information Studies, Syracuse University, Syracuse, 13244, United states    
  • 2 Morrissey College of Arts & Sciences, Boston College, Boston, MA 02467, United States    
  • 3 Faculty of science, The University of British Columbia, Vancouver, v6t 1z4, Canada    
  • *corresponding author andrewl02y@gmail.com
ACE Vol.44
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-327-2
ISBN (Online): 978-1-83558-328-9

Abstract

The paper investigates the critical differences between AI-generated text and human responses in terms of linguistic patterns, structure, and content. The research makes use of datasets from HC3, collected in 2023. Our results are that ChatGPT with GPT-3.5 is more likely to use words like conjunctions and combinations of words in conversations compared to humans systematically. Our model has high accuracy in identifying AI-generated answers.

Keywords:

ChatGPT, Artificial intelligence, linguistic pattern, predictive models, natural language processing

He,Z.;Mao,R.;Liu,Y. (2024). Predictive model on detecting ChatGPT responses against human responses. Applied and Computational Engineering,44,18-25.
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References

[1]. Chan, A. (2023) GPT-3 and InstructGPT: technological dystopian, utopianism, and "Contextual" perspectives in AI ethics and industry. AI and Ethics., 3(1): 53-64.

[2]. Saparov, A., He, H. (2022) Language models are greedy reasoners: A systematic formal analysis of chain-of-thought. arXiv preprint arXiv:2210.01240.

[3]. Lund, B.D., Wang, T. (2023) Chatting about ChatGPT: how may AI and GPT impact academia and libraries? Library Hi Tech News., 40(3): 26-9.

[4]. Biswas, S.S. (2023) Potential use of chat gpt in global warming. Annals of Biomedical Engineering., 51(6): 1126-1127.

[5]. Biswas, S.S. (2023) Role of Chat GPT in Public Health. Annals of Biomedical Engineering., 51(5): 868-9.

[6]. Pardos, Z.A., Bhandari, S. (2023) Learning gain differences between ChatGPT and human tutor generated algebra hints. arXiv preprint arXiv:2302.06871.

[7]. Alafnan, M.A., Dishari, S., Jovic, M., Lomidze, K. (2023) ChatGPT as an Educational Tool: Opportunities, Challenges, and Recommendations for Communication, Business Writing, and Composition Courses. Journal of Artificial Intelligence and Technology., 3(2): 60-68.

[8]. Hulman, A., Dollerup, O.L., Mortensen, J.F., Fenech, M., Norman, K., Støvring, H., Hansen, T.K. (2023) ChatGPT- versus human-generated answers to frequently asked questions about diabetes: a Turing test-inspired survey among employees of a Danish diabetes center., medRxiv. 2023: 2023-02.

[9]. The Programming Foundation RSS. (n.d.) Module 4 - logistic regression: The Programming Foundation. https://learn.theprogrammingfoundation.org/getting_started/intro_data_science/module4/?gclid=Cj0KCQjwlumhBhClARIsABO6p-yoBFGaaW2dK6HMNlKxCMbP2_pu71NylAw2gmUMA_2g-qogZP1l_D0aAnCqEALw_wcB

[10]. LightGBM 3.3.2. (n.d.) LightGBM's documentation. https://lightgbm.readthedocs.io/en/v3.3.2/index.html

[11]. Ray, S. (2023) Naive Bayes Classifier Explained: Applications and Practice Problems of Naive Bayes Classifier. https://www.analyticsvidhya.com/blog/2017/09/naive-bayes-explained/

Cite this article

He,Z.;Mao,R.;Liu,Y. (2024). Predictive model on detecting ChatGPT responses against human responses. Applied and Computational Engineering,44,18-25.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-327-2(Print) / 978-1-83558-328-9(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.44
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Chan, A. (2023) GPT-3 and InstructGPT: technological dystopian, utopianism, and "Contextual" perspectives in AI ethics and industry. AI and Ethics., 3(1): 53-64.

[2]. Saparov, A., He, H. (2022) Language models are greedy reasoners: A systematic formal analysis of chain-of-thought. arXiv preprint arXiv:2210.01240.

[3]. Lund, B.D., Wang, T. (2023) Chatting about ChatGPT: how may AI and GPT impact academia and libraries? Library Hi Tech News., 40(3): 26-9.

[4]. Biswas, S.S. (2023) Potential use of chat gpt in global warming. Annals of Biomedical Engineering., 51(6): 1126-1127.

[5]. Biswas, S.S. (2023) Role of Chat GPT in Public Health. Annals of Biomedical Engineering., 51(5): 868-9.

[6]. Pardos, Z.A., Bhandari, S. (2023) Learning gain differences between ChatGPT and human tutor generated algebra hints. arXiv preprint arXiv:2302.06871.

[7]. Alafnan, M.A., Dishari, S., Jovic, M., Lomidze, K. (2023) ChatGPT as an Educational Tool: Opportunities, Challenges, and Recommendations for Communication, Business Writing, and Composition Courses. Journal of Artificial Intelligence and Technology., 3(2): 60-68.

[8]. Hulman, A., Dollerup, O.L., Mortensen, J.F., Fenech, M., Norman, K., Støvring, H., Hansen, T.K. (2023) ChatGPT- versus human-generated answers to frequently asked questions about diabetes: a Turing test-inspired survey among employees of a Danish diabetes center., medRxiv. 2023: 2023-02.

[9]. The Programming Foundation RSS. (n.d.) Module 4 - logistic regression: The Programming Foundation. https://learn.theprogrammingfoundation.org/getting_started/intro_data_science/module4/?gclid=Cj0KCQjwlumhBhClARIsABO6p-yoBFGaaW2dK6HMNlKxCMbP2_pu71NylAw2gmUMA_2g-qogZP1l_D0aAnCqEALw_wcB

[10]. LightGBM 3.3.2. (n.d.) LightGBM's documentation. https://lightgbm.readthedocs.io/en/v3.3.2/index.html

[11]. Ray, S. (2023) Naive Bayes Classifier Explained: Applications and Practice Problems of Naive Bayes Classifier. https://www.analyticsvidhya.com/blog/2017/09/naive-bayes-explained/

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