A Comprehensive Review of Transformer-Based Models: ChatGPT and Bard in Focus

被引:0
作者
Illangarathne, Pooja [1 ]
Jayasinghe, Nethari [1 ]
de Lima, A. B. Duweeja [1 ]
机构
[1] Informat Inst Technol, Sch Comp, Colombo, Sri Lanka
来源
2024 7TH INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND BIG DATA, ICAIBD 2024 | 2024年
关键词
Transformer Models; ChatGPT; Bard AI; Artificial Intelligence; Natural Language Processing; Recurrent Neural Networks; Long Short-Term Memory; Generative Pre-trained Transformer; BERT; Language Model Applications; ARTIFICIAL-INTELLIGENCE; ACCURACY;
D O I
10.1109/ICAIBD62003.2024.10604437
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This review paper provides a thorough analysis of transformer-based models, specifically ChatGPT and Bard, exploring their many uses, underlying difficulties, and potential future developments. Through a synthesis of current research findings, it highlights the transformational power and possible drawbacks of these models in a variety of fields. The study delves deeper into the moral issues, societal ramifications, and complex roles these models play in fields including environmental research, digital forensics, healthcare, and education. The changing field of artificial intelligence is also covered, emphasizing the necessity of domain-specific modifications as well as the significance of resolving ethical and technological issues in the ongoing creation and use of increasingly sophisticated AI systems.
引用
收藏
页码:543 / 554
页数:12
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