Large Data Begets Large Data: Studying Large Language Models (LLMs) and Its History, Types, Working, Benefits and Limitations

Dishita Naik, Ishita Naik, Nitin Naik

Research output: Chapter in Book/Published conference outputConference publication

Abstract

The emergence of Large Language Models (LLMs) transformed the domain of Natural Language Processing (NLP) by enhancing the capability of machines to effectively comprehend and generate natural language. These LLMs are a type of generative AI and the underlying AI model that work behind the scenes of most modern AI chatbots. Generative AI is an umbrella term for all AI technologies which can generate original contents. These LLMs are pre-trained on a large amount of textual data and billions of parameters. This pre-training is normally unsupervised learning, meaning that it processes the unlabelled data for a comprehensive understanding of context, semantics, and grammar of natural language to generate coherent, context-relevant and credible text. In view of the significance of LLMs, this paper aims to perform a comprehensive study of LLMs, which will elucidate the historical journey of language processing and modelling, evolution and types of language models, and working, benefits and limitations of LLMs.
Original languageEnglish
Title of host publicationContributions Presented at The International Conference on Computing, Communication, Cybersecurity and AI, July 3–4, 2024, London, UK: The C3AI 2024
EditorsNitin Naik, Paul Jenkins, Shaligram Prajapat, Paul Grace
Pages293-314
ISBN (Electronic)978-3-031-74443-3
DOIs
Publication statusPublished - 20 Dec 2024

Publication series

NameLecture Notes in Networks and Systems (LNNS)
PublisherSpringer Cham
Volume884
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

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