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A Comparative Study on the Evaluation of ChatGPT and BERT in the Development of Text Classification Systems
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Author(s): Saranya M. (Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology, Chennai, India)and Amutha B. (Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology, Chennai, India)
Copyright: 2025
Pages: 18
Source title:
Generative Artificial Intelligence and Ethics: Standards, Guidelines, and Best Practices
Source Author(s)/Editor(s): Loveleen Gaur (University of South Pacific, Fiji & Taylor's University, Malaysia)
DOI: 10.4018/979-8-3693-3691-5.ch004
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Abstract
A lot of progress has been made in Natural Language Processing (NLP) recently. With the release of powerful new models like BERT and GPT-4, it is now feasible to build high-level applications that could understand and interact with languages. Text classification is one of the ground-level operations of NLP. There are a plethora of uses for this field, such as sentiment analysis and creating chatbots to respond to user inquiries. In Natural Language Processing (NLP), transformer-based models have recently become the de facto norm due to their outstanding performance on various benchmarks. Using a battery of categorical text classification tasks, this study probes the architecture and behavior of the GPT-4 and BERT language models in different contexts. Examining the GPT-4 and BERT language models in different contexts, this study tests them on various categorical concerns to learn about their architecture and performance.
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