Devising an approach to preventing information chaos in chat bots using generative artificial intelligence

Authors

DOI:

https://doi.org/10.15587/1729-4061.2025.324957

Keywords:

Large Language Model, LLM, Copilot, ChatGPT, cyclic class element, attractor

Abstract

The object of this study is the linguistic constructions of queries to chatbots with Large Language Models (LLMs). The area of research is the emergence of information chaos during communication between the user and the chatbot, which leads to errors in forming a response to the query. It is assumed that the user and the chatbot are separate complex systems, the events and actions of which are difficult to predict for a long period. Behavior models of complex systems are subject to the influence of chaos theory. To demonstrate this, the work used one of the simple mathematical problems with a logical component. The Copilot and ChatGPT 4o mini chatbots that were studied gave erroneous results in response to a query for the task. The error occurred when generating a query due to the introduction of a logical component. A similar process was represented by a system of differential equations solving which establishes clear rules for obtaining an accurate answer to the query.

To submit a request from the user, an approach has been proposed that makes it possible to break down the information block of the request by constructing piecewise linear attractors. That is, paired semantic expressions are formed with the formation of a request cleared of information noise. The problem is solved by presenting a methodology for controlling the selection of substitute words, based on the operations of generating the next substitution and calculating the number of the given substitution.

According to the devised approach, the best options for a request to the Copilot chatbot were obtained in 182 characters or 48 words, numbers, and special characters. For the ChatGPT 4o mini chatbot, such a request consisted of 219 characters.

The proposal could be used in practical activities to improve chatbot technologies and form key data sets in artificial intelligence systems, which would further make it possible to avoid errors when solving problems with a logical component

Author Biographies

Olha Kryazhych, Institute of Telecommunications and Global Information Space of the National Academy of Sciences of Ukraine

PhD, Senior Researcher, Associate Professor

Department of Natural Resources

Ivan Ivanov, Institute of Telecommunications and Global Information Space of the National Academy of Sciences of Ukraine

PhD Student

Department of Natural Resources

Kateryna Iushchenko, Institute of Telecommunications and Global Information Space of the National Academy of Sciences of Ukraine

PhD, Junior Researcher

Department of Information and Communication Technologies

Oleksii Kupri, Institute of Telecommunications and Global Information Space of the National Academy of Sciences of Ukraine

PhD, Junior Researcher

Department of Information and Communication Technologies

Oleksandr Vasenko, Hryhorii Skovoroda University in Pereiaslav

Associate Professor, Head of Department

Department of Digital Methods of Teaching

Viacheslav Riznyk, Hryhorii Skovoroda University in Pereiaslav

Doctor of Pedagogical Sciences, Associate Professor, Professor

Department of Digital Methods of Teaching

Oleksandr Ryzhkov, Hryhorii Skovoroda University in Pereiaslav

Doctor of Technical Sciences, Associate Professor

Department of Digital Methods of Teaching

References

  1. Errichiello, O., Wernke, M. (2022). Order in Chaos - Cybernetics of Brand Management. Springer. https://doi.org/10.1007/978-3-662-65958-8
  2. Cappa, F., Oriani, R., Peruffo, E., McCarthy, I. (2020). Big Data for Creating and Capturing Value in the Digitalized Environment: Unpacking the Effects of Volume, Variety, and Veracity on Firm Performance. Journal of Product Innovation Management, 38 (1), 49–67. https://doi.org/10.1111/jpim.12545
  3. Saadia, D. (2021). Integration of Cloud Computing, Big Data, Artificial Intelligence, and Internet of Things. International Journal of Web-Based Learning and Teaching Technologies, 16 (1), 10–17. https://doi.org/10.4018/ijwltt.2021010102
  4. Junaid, M., Ali, S., Siddiqui, I. F., Nam, C., Qureshi, N. M. F., Kim, J., Shin, D. R. (2022). Performance Evaluation of Data-driven Intelligent Algorithms for Big data Ecosystem. Wireless Personal Communications, 126 (3), 2403–2423. https://doi.org/10.1007/s11277-021-09362-7
  5. Fuchs, A. (2013). Nonlinear Dynamics in Complex Systems. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-33552-5
  6. Kryazhych, O., Vasenko, O., Isak, L., Havrylov, I., Gren, Y. (2024). Construction of a model for matching user’s linguistic structures to a chat-bot language model. Eastern-European Journal of Enterprise Technologies, 3 (2 (129)), 34–41. https://doi.org/10.15587/1729-4061.2024.304048
  7. Gravett, W. (2021). Sentenced by an algorithm – Bias and lack of accuracy in risk-assessment software in the United States criminal justice system. South African Journal of Criminal Justice, 34 (1), 31–54. https://doi.org/10.47348/sacj/v34/i1a2
  8. Taghia, J. (2024). Exploring the Synergy of Chaos Theory and AI: Predictive Modeling and Understanding of Complex Systems Through Machine Learning and Deep Neural Networks Review. COJ Robotics & Artificial Intelligence, 3 (4). Available at: https://crimsonpublishers.com/cojra/fulltext/COJRA.000566.php
  9. Elabid, Z., Chakraborty, T., Hadid, A. (2022). Knowledge-based Deep Learning for Modeling Chaotic Systems. 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA), 1203–1209. https://doi.org/10.1109/icmla55696.2022.00194
  10. Wang, H., Fu, T., Du, Y., Gao, W., Huang, K., Liu, Z. et al. (2023). Scientific discovery in the age of artificial intelligence. Nature, 620 (7972), 47–60. https://doi.org/10.1038/s41586-023-06221-2
  11. Demszky, D., Yang, D., Yeager, D. S., Bryan, C. J., Clapper, M., Chandhok, S. et al. (2023). Using large language models in psychology. Nature Reviews Psychology. https://doi.org/10.1038/s44159-023-00241-5
  12. Mirzadeh, I., Alizadeh, K., Shahrokhi, H., Tuzel, O., Bengio, S., Farajtabar, M. (2024). GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models. arXiv. https://doi.org/10.48550/arXiv.2410.05229
  13. Zhao, W., Zhou, K., Junyi, L., Tianyi, T., Wang, X., Hou, Y. et al. (2023). A Survey of Large Language Models. arXiv. https://doi.org/10.48550/arXiv.2303.18223
  14. Ai, J., Cai, Y., Su, Z., Zhang, K., Peng, D., Chen, Q. (2022). Predicting user-item links in recommender systems based on similarity-network resource allocation. Chaos, Solitons & Fractals, 158, 112032. https://doi.org/10.1016/j.chaos.2022.112032
  15. Du, F., Ma, X.-J., Yang, J.-R., Liu, Y., Luo, C.-R., Wang, X.-B. et al. (2024). A Survey of LLM Datasets: From Autoregressive Model to AI Chatbot. Journal of Computer Science and Technology, 39 (3), 542–566. https://doi.org/10.1007/s11390-024-3767-3
  16. Matsumoto, N., Moran, J., Choi, H., Hernandez, M. E., Venkatesan, M., Wang, P., Moore, J. H. (2024). KRAGEN: a knowledge graph-enhanced RAG framework for biomedical problem solving using large language models. Bioinformatics, 40 (6). https://doi.org/10.1093/bioinformatics/btae353
  17. Zhang, Y., Zhang, C. (2024). Extracting problem and method sentence from scientific papers: a context-enhanced transformer using formulaic expression desensitization. Scientometrics, 129 (6), 3433–3468. https://doi.org/10.1007/s11192-024-05048-6
  18. Shen, B.-W., Pielke, R. A., Zeng, X., Baik, J.-J., Faghih-Naini, S., Cui, J. et al. (2021). Is Weather Chaotic? Coexisting Chaotic and Non-chaotic Attractors Within Lorenz Models. 13th Chaotic Modeling and Simulation International Conference, 805–825. https://doi.org/10.1007/978-3-030-70795-8_57
  19. Martines-Arano, H., García-Pérez, B. E., Vidales-Hurtado, M. A., Trejo-Valdez, M., Hernández-Gómez, L. H., Torres-Torres, C. (2019). Chaotic Signatures Exhibited by Plasmonic Effects in Au Nanoparticles with Cells. Sensors, 19 (21), 4728. https://doi.org/10.3390/s19214728
  20. Stankevich, N. V., Kuznetsov, N. V., Leonov, G. A., Chua, L. O. (2017). Scenario of the Birth of Hidden Attractors in the Chua Circuit. International Journal of Bifurcation and Chaos, 27 (12), 1730038. https://doi.org/10.1142/s0218127417300385
  21. Voronka, H. V. (2014). Informatsiynyi shum u dovidkovykh vydanniakh. Obriyi drukarstva, 1, 89–95. Available at: https://horizons.vpi.kpi.ua/article/view/95451
  22. Anh, L. Q. (2021). Various Types of Well-Posedness for Vector Equilibrium Problems with Respect to the Lexicographic Order. Vietnam Journal of Mathematics, 51 (2), 397–414. https://doi.org/10.1007/s10013-021-00530-7
Devising an approach to preventing information chaos in chat bots using generative artificial intelligence

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Published

2025-04-22

How to Cite

Kryazhych, O., Ivanov, I., Iushchenko, K., Kupri, O., Vasenko, O., Riznyk, V., & Ryzhkov, O. (2025). Devising an approach to preventing information chaos in chat bots using generative artificial intelligence. Eastern-European Journal of Enterprise Technologies, 2(2 (134), 84–95. https://doi.org/10.15587/1729-4061.2025.324957

Issue

Vol. 2 No. 2 (134) (2025): Information technology. Industry control systems

Section

Information technology

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Copyright (c) 2025 Olha Kryazhych, Ivan Ivanov, Kateryna Iushchenko, Oleksii Kupri, Oleksandr Vasenko, Viacheslav Riznyk, Oleksandr Ryzhkov

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