Unraveling the Inner Workings of Massive Language Models: Architecture, Training, and Linguistic Capacities

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Author(s): C. V. Suresh Babu (Hindustan Institute of Technology and Science, India), C. S. Akkash Anniyappa (Sri Sivasubramaniya Nadar College of Engineering, India)and Dharma Sastha B. (Hindustan Institute of Technology and Science, India)
Copyright: 2024
Pages: 41
Source title: Challenges in Large Language Model Development and AI Ethics
Source Author(s)/Editor(s): Brij Gupta (Asia University, Taichung City, Taiwan)
DOI: 10.4018/979-8-3693-3860-5.ch008

Keywords: Artificial Intelligence / Data Modeling Languages / Engineering Science Reference / Science & Engineering

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Abstract

This study explores the evolution of language models, emphasizing the shift from traditional statistical methods to advanced neural networks, particularly the transformer architecture. It aims to understand the impact of these advancements on natural language processing (NLP). The study examines the core concepts of language models, including neural networks, attention, and self-attention mechanisms, and evaluates their performance on various NLP tasks. The findings demonstrate significant improvements in language modeling, especially in dialogue generation and translation. Despite these advancements, the study highlights the need to address ethical issues such as bias, fairness, privacy, and security for responsible AI deployment.

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Unraveling the Inner Workings of Massive Language Models: Architecture, Training, and Linguistic Capacities

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