Applied and Computational Engineering
- The Open Access Proceedings Series for Conferences
Proceedings of the 6th International Conference on Computing and Data Science
Series Vol. 57 , 30 April 2024
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Unlocking the potential: A comprehensive exploration of large language models in natural language processing
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Abstract
In recent years, large language models (LLMs) have revolutionized natural language processing (NLP) with their transformative architectures and sophisticated training techniques. This paper provides a comprehensive overview of LLMs, focusing on their architecture, training methodologies, and diverse applications. We delve into the transformer architecture, attention mechanisms, and parameter tuning strategies that underpin LLMs' capabilities. Furthermore, we explore training techniques such as self-supervised learning, transfer learning, and curriculum learning, highlighting their roles in empowering LLMs with linguistic proficiency. Additionally, we discuss the wide-ranging applications of LLMs, including text generation, sentiment analysis, and question answering, showcasing their versatility and impact across various domains. Through this comprehensive examination, we aim to elucidate the advancements and potentials of LLMs in shaping the future of natural language understanding and generation.
Keywords
Large Language Models, Natural Language Processing, Transformer Architecture, Training Techniques, Self-Supervised Learning
References
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 6th International Conference on Computing and Data Science
- ISBN (Print)
- 978-1-83558-393-7
- ISBN (Online)
- 978-1-83558-394-4
- Published Date
- 30 April 2024
- Series
- Applied and Computational Engineering
- ISSN (Print)
- 2755-2721
- ISSN (Online)
- 2755-273X
- DOI
- 10.54254/2755-2721/57/20241341
- Copyright
- 30 April 2024
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited