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A Comprehensive Review on Large Language Models: Exploring Applications, Challenges, Limitations, and Future Prospects
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Author(s): Asmita Yadav (Jaypee Institute of Information Technology, India)
Copyright: 2024
Pages: 23
Source title:
Advancing Software Engineering Through AI, Federated Learning, and Large Language Models
Source Author(s)/Editor(s): Avinash Kumar Sharma (Sharda University, India), Nitin Chanderwal (University of Cincinnati, USA), Amarjeet Prajapati (Jaypee Institute of Information Technology, India), Pancham Singh (Ajay Kumar Garg Engineering College, Ghaziabad, India)and Mrignainy Kansal (Netaji Subhas University of Technology (NSUT), Delhi, India)
DOI: 10.4018/979-8-3693-3502-4.ch002
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Abstract
In the realm of computer science and language, large language models (LLMs) stand out as remarkable tools of artificial intelligence (AI). Proficient in deciphering intricate language nuances, LLMs offer sensible responses and find applications in natural language understanding, language translation, and question answering. This chapter delves into the history, creation, training, and multifaceted applications of LLMs. It explores the basics of generative AI, focusing on generative pre-trained transformers (GPT). Examining the evolution of LLMs and their diverse applications in medicine, education, finance, and engineering, the chapter addresses real-world challenges, including ethical concerns, biases, comprehensibility, and computational requirements. It serves as an informative guide for researchers, practitioners, and enthusiasts, elucidating the potential, challenges, and future of LLMs in AI.
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