Deep Learning-driven Enhancement of Chatbot Interaction: A Comprehensive Study on ChatGLM-Reference-Cited by-同舟云学术

Deep Learning-driven Enhancement of Chatbot Interaction: A Comprehensive Study on ChatGLM

Published:2024-09-04 Issue: Volume:12 Page:377-383
ISSN:2415-1521
Container-title:WSEAS TRANSACTIONS ON COMPUTER RESEARCH
language:en
Short-container-title:

Author:

Zeng Zijian¹,Ratnavelu Kurunathan¹

Affiliation:

1. Institute of Computer Science and Digital Innovation, UCSI University, WP Kuala Lumpur 56000, MALAYSIA

Abstract

In the contemporary digital landscape, ChatGLM, powered by advanced artificial intelligence, has risen as a tour de force, particularly excelling in Chinese Q&A scenarios. Its prominence underscores the transformative role of deep learning neural networks in reshaping the chatbot paradigm. This paper offers a holistic exploration of chatbot model designs, building upon seminal research, and delves into the nuances of chatbot development and underlying technologies. We provide incisive analyses poised to guide future advancements in chatbot-related arenas.

Publisher

World Scientific and Engineering Academy and Society (WSEAS)

Reference14 articles.

1. Wang J, On the Limit of Machine Intelligence. International Journal of Intelligence Science, 2013, pp. 170-175. http://dx.doi.org/10.4236/ijis.2013.34018.

2. Naveed S, Sajid U, Imrab S, Faiz Ai, Mrim M, Chat-GPT; validating Technology Acceptance Model (TAM) in education sector via ubiquitous learning mechanism, Computers in Human Behavior, Vol.154, 2024, pp. 0747-5632. https://doi.org/10.1016/j.chb.2023.108097.

3. Zalimkhan N, Olga N, Murat A, Kantemir B, Sultan K, The symbol grounding problem in the system of general artificial intelligence based on multi-agent neurocognitive architecture, Cognitive Systems Research, Vol.79, 2023, pp. 71-84. https://doi.org/10.1016/j.cogsys.2023.01.002.

4. Ruchi G, Kiran N, Mahima M, Blend I, Seema, Adoption and impacts of generative artificial intelligence: Theoretical underpinnings and research agenda, International Journal of Information Management Data Insights, Vol.4, No.1, 2024, pp. 100232. https://doi.org/10.1016/j.jjimei.2024.100232.

5. Evgeny T, Igor P, Machine learning algorithms for teaching AI chat bots, Procedia Computer Science, Vol.190, 2021, pp. 735-744. https://doi.org/10.1016/j.procs.2021.06.086.