Journal of Management Research and Analysis
Official Publication of Innovative Education and Scientific Research Foundation
Official Publication of Innovative Education and Scientific Research Foundation
Print ISSN: 2394-2762
Online ISSN: 2394-2770
CODEN : JMRABX
Journal of Management Research and Analysis (JMRA) open access, peer-reviewed quarterly journal publishing since 2014 and is published under auspices of the Innovative Education and Scientific Research Foundation (IESRF), aim to uplift researchers, scholars, academicians, and professionals in all academic and scientific disciplines. IESRF is dedicated to the transfer of technology and research by publishing scientific journals, research content, providing professional’s membership, and conducting conferences, seminars, and award programs. With more...Review Article
Author Details :
Volume : 10, Issue : 1, Year : 2023
Article Page : 18-20
https://doi.org/10.18231/j.jmra.2023.004
Abstract
Natural Language Processing (NLP) has seen tremendous advancements with the development of Generative Pretrained Transformer (GPT) models and their conversational variant, ChatGPT. These language models have been shown to generate contextually appropriate and coherent responses to natural language prompts, making them highly useful for various NLP applications. However, there are still limitations to their performance and understanding these limitations is crucial for their effective utilization. This paper presents a comprehensive analysis of the capabilities and limitations of GPT and ChatGPT, covering their architecture, training processes, and evaluation metrics. The study also evaluates the performance of these models on various NLP tasks, including language translation, question-answering, and text summarization. The results reveal that while these models excel in certain tasks, they still face challenges in understanding context, generating diverse responses, and handling rare or out-of-domain inputs. The study concludes by discussing potential solutions and future research directions for improving the performance of GPT and ChatGPT in NLP applications.
Keywords: Natural Language Processing, Generative Pretrained Transformer, ChatGPT, their architecture, training processes, evaluation metrics Solutions
How to cite : Jagdishbhai N, Thakkar K Y, Exploring the capabilities and limitations of GPT and Chat GPT in natural language processing. J Manag Res Anal 2023;10(1):18-20
This is an Open Access (OA) journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.
