Version 1
: Received: 18 February 2024 / Approved: 20 February 2024 / Online: 20 February 2024 (11:28:38 CET)
How to cite:
Islam, H.M.A.; Hasan, M.; Ahmed, S.; Fardin, A.I.; Nabil, M.H. PixieGPT: Design and Implementation of a Generative Pre-Trained Transformer for Universities of Bangladesh. Preprints2024, 2024021083. https://doi.org/10.20944/preprints202402.1083.v1
Islam, H.M.A.; Hasan, M.; Ahmed, S.; Fardin, A.I.; Nabil, M.H. PixieGPT: Design and Implementation of a Generative Pre-Trained Transformer for Universities of Bangladesh. Preprints 2024, 2024021083. https://doi.org/10.20944/preprints202402.1083.v1
Islam, H.M.A.; Hasan, M.; Ahmed, S.; Fardin, A.I.; Nabil, M.H. PixieGPT: Design and Implementation of a Generative Pre-Trained Transformer for Universities of Bangladesh. Preprints2024, 2024021083. https://doi.org/10.20944/preprints202402.1083.v1
APA Style
Islam, H.M.A., Hasan, M., Ahmed, S., Fardin, A.I., & Nabil, M.H. (2024). PixieGPT: Design and Implementation of a Generative Pre-Trained Transformer for Universities of Bangladesh. Preprints. https://doi.org/10.20944/preprints202402.1083.v1
Chicago/Turabian Style
Islam, H.M.A., Ariful Islam Fardin and Mehedi Hasan Nabil. 2024 "PixieGPT: Design and Implementation of a Generative Pre-Trained Transformer for Universities of Bangladesh" Preprints. https://doi.org/10.20944/preprints202402.1083.v1
Abstract
In a densely populated country like Bangladesh, universities grapple with the challenge of efficiently addressing myriad queries from a large student body, leading to a heightened workload for university stakeholders. To tackle these challenges, we introduce PixieGPT, a tailor-made Generative Pre-Trained Transformer for Bangladeshi universities. PixieGPT significantly mitigates workload by adeptly handling common university-related queries, thereby enhancing user experience. The hierarchical structure plays a crucial role in managing diverse queries from thousands of students about the university system. The solution introduces a modular hierarchical knowledge base (KB) with simpler complexities, addressing the intricacies of efficiently managing large volumes of queries. PixieGPT is designed in a modular way so that the solution is also adaptable for the implementation of other universities worldwide based on the requirements of a particular administrative system. The modular nature facilitates easy adaptation with minor changes based on specific university requirements, ensuring a seamless integration process. This paper delves into the intricacies of PixieGPT's design, emphasizing its pivotal role in mitigating workload challenges for university stakeholders in Bangladesh. The incorporation of BERT for Natural Language Understanding(NLU) and GPT models for Natural Language Generation(NLG) enhances PixieGPT's capabilities, contributing to the scalability and efficiency of the system. The presented use case underscores the practical benefits of PixieGPT, positioning it as a promising solution for universities globally with similar operational frameworks.
Keywords
NLP; BeRT; PixieGPT
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
