Issue |
E3S Web Conf.
Volume 491, 2024
International Conference on Environmental Development Using Computer Science (ICECS’24)
|
|
---|---|---|
Article Number | 01026 | |
Number of page(s) | 14 | |
Section | Energy Management for Sustainable Environment | |
DOI | https://doi.org/10.1051/e3sconf/202449101026 | |
Published online | 21 February 2024 |
Exploring the roles of AI-Assisted ChatGPT in the field of data science
1 Vice President, Standard Chartered Global Business Services Sdn Bhd., Kuala Lumpur, Malaysia, latha.nv@gmail.com.
2 Associate Professor, PG and Research Department of Computer Science, Sri Meenakshi Government Arts College for Women, Madurai, Tamil Nadu, India, sujamurugan@gmail.com
3 Student – M.Sc. Cyber Security, School of Computer Science, University of Birmingham, Birmingham, United Kingdom, mukulmech99@gmail.com.
4 Student – M.S Data Science, School of Engineering and Applied Sciences, University at Buffalo, The state University of New York, United States of America, vslokesh10@gmail.com
In this study, we explore the roles of AI-assisted ChatGPT (Generative Pre-trained Transformer) in the field of data science. AI-assisted ChatGPT, a powerful language model, is fine-tuned using domain-specific data for specialised data science tasks, such as sentiment analysis and named entity recognition (NER). The results reveal significant reductions in model size and memory usage with minor trade-offs in inference time, providing valuable resource-efficient deployment. Various data augmentation methods, including back-translation, synonym replacement, and contextual word embeddings, are employed to augment the training dataset. The study's results are subjected to rigorous statistical analysis, including paired t-tests and ANOVA tests, to determine the significance of the findings. The research concludes with insightful suggestions and future scope, including advanced fine-tuning strategies, model optimization techniques, and ethical considerations.
Key words: AI-assisted ChatGPT / Data Science / Fine-tuning / Optimization / Data Augmentation / Natural Language Processing / Transfer Learning / Resource Efficiency / Performance Metrics / Statistical Analysis
© The Authors, published by EDP Sciences, 2024
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