Construction of a model for matching user's linguistic structures to a chat-bot language model
DOI:
https://doi.org/10.15587/1729-4061.2024.304048Keywords:
generative artificial intelligence, recurrent algorithm, formalization of user request, basic sequenceAbstract
The research object of this work is the linguistic structures of the user when constructing a request to a chatbot with generative artificial intelligence. The study solved the task of improving the communication mediation algorithms of chatbots through the comparison models of linguistic structures by users. Sometimes the user intentionally or due to lack of information forms an inaccurate request. Formally, this is described by logical operations "And" and "And or Not".
As a result of the research, a model was built comparing linguistic structures at the input with the information model of the response at the output. The model was based on an approach with recursive creation of an answer. That has made it possible to determine the basic characteristics of the object of the request and form an answer on this basis. Using this approach improved the accuracy of the chatbot response. It also made it possible to consider the linguistic structure of the user through its formalization. The use of logic algebra made it possible to find typical errors of users during dialogs with generative artificial intelligence.
A feature of the reported advancement is that the comparison of models of linguistic structures of query formation is carried out through a recurrent algorithm. As a result, it makes it possible to compare the query in such a way as to reduce the absolute error of the primary data by 0.02 % and simplify the process of mathematical calculations. At the same time, the received information becomes more accurate – the number of references increases from 2 to 6 sources.
The proposal could be used in practical activities to improve the natural language recognition technologies of users in chatbots with generative artificial intelligence. On this basis, it is possible to devise various applications and services for training and practical activities
References
- Gkinko, L., Elbanna, A. (2023). The appropriation of conversational AI in the workplace: A taxonomy of AI chatbot users. International Journal of Information Management, 69, 102568. https://doi.org/10.1016/j.ijinfomgt.2022.102568
- Hauptmann, C., Krenzer, A., Völkel, J., Puppe, F. (2024). Argumentation effect of a chatbot for ethical discussions about autonomous AI scenarios. Knowledge and Information Systems. https://doi.org/10.1007/s10115-024-02074-x
- Auffarth, B. (2020). Artificial intelligence with python cookbook: proven recipes for applying ai algorithms and deep learning techniques using tensorflow 2.x and pytorch 1.6. PACKT Publishing, 468.
- Working Draft, Standard for Programming Language C++. Available at: https://www.open-std.org/jtc1/sc22/wg21/docs/papers/2017/n4713.pdf
- Zuo, Z., Zeng, Z., Su, W., Huang, Q., Ke, Y., Liu, Z. et al. (2023). Specification transformation method for functional program generation based on partition-recursion refinement rule. Information Sciences, 633, 613–632. https://doi.org/10.1016/j.ins.2023.03.055
- Bian, W., Li, C., Hou, H., Liu, X. (2023). Iterative convolutional enhancing self-attention Hawkes process with time relative position encoding. International Journal of Machine Learning and Cybernetics, 14 (7), 2529–2544. https://doi.org/10.1007/s13042-023-01780-2
- Sannella, D., Fourman, M., Peng, H., Wadler, P. (2021). Introduction to Computation. In Undergraduate Topics in Computer Science. Springer International Publishing. https://doi.org/10.1007/978-3-030-76908-6
- Bossard, A. (2022). The SOF Programming Paradigm. International Journal of Software Innovation, 10 (1), 1–14. https://doi.org/10.4018/ijsi.309965
- Nirala, K. K., Singh, N. K., Purani, V. S. (2022). A survey on providing customer and public administration based services using AI: chatbot. Multimedia Tools and Applications, 81 (16), 22215–22246. https://doi.org/10.1007/s11042-021-11458-y
- Park, G., Chung, J., Lee, S. (2022). Effect of AI chatbot emotional disclosure on user satisfaction and reuse intention for mental health counseling: a serial mediation model. Current Psychology, 42 (32), 28663–28673. https://doi.org/10.1007/s12144-022-03932-z
- Niu, B., Mvondo, G. F. N. (2024). I Am ChatGPT, the ultimate AI Chatbot! Investigating the determinants of users’ loyalty and ethical usage concerns of ChatGPT. Journal of Retailing and Consumer Services, 76, 103562. https://doi.org/10.1016/j.jretconser.2023.103562
- Huang, R. S., Lu, K. J. Q., Meaney, C., Kemppainen, J., Punnett, A., Leung, F.-H. (2023). Assessment of Resident and AI Chatbot Performance on the University of Toronto Family Medicine Residency Progress Test: Comparative Study. JMIR Medical Education, 9, e50514. https://doi.org/10.2196/50514
- Xie, Y., Seth, I., Hunter‐Smith, D. J., Rozen, W. M., Seifman, M. A. (2023). Investigating the impact of innovative AI chatbot on post‐pandemic medical education and clinical assistance: a comprehensive analysis. ANZ Journal of Surgery, 94 (1-2), 68–77. https://doi.org/10.1111/ans.18666
- Kuckelman, I. J., Yi, P. H., Bui, M., Onuh, I., Anderson, J. A., Ross, A. B. (2024). Assessing AI-Powered Patient Education: A Case Study in Radiology. Academic Radiology, 31 (1), 338–342. https://doi.org/10.1016/j.acra.2023.08.020
- Dongbo, M., Miniaoui, S., Fen, L., Althubiti, S. A., Alsenani, T. R. (2023). Intelligent chatbot interaction system capable for sentimental analysis using hybrid machine learning algorithms. Information Processing & Management, 60 (5), 103440. https://doi.org/10.1016/j.ipm.2023.103440
- Noroozi, N., Mironchenko, A., Wirth, F. R. (2022). A relaxed small-gain theorem for discrete-time infinite networks. Automatica, 142, 110363. https://doi.org/10.1016/j.automatica.2022.110363
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Olha Kryazhych, Oleksandr Vasenko, Liudmyla Isak, Ihor Havrylov, Yevhen Gren
This work is licensed under a Creative Commons Attribution 4.0 International License.
The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.
A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.
