TY - GEN
T1 - Large Data Begets Large Data: Studying Large Language Models (LLMs) and Its History, Types, Working, Benefits and Limitations
AU - Naik, Dishita
AU - Naik, Ishita
AU - Naik, Nitin
PY - 2024/12/20
Y1 - 2024/12/20
N2 - 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.
AB - 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.
UR - https://www.techrxiv.org/users/845749/articles/1240126-large-data-begets-large-data-studying-large-language-models-llms-and-its-history-types-working-benefits-and-limitations?commit=8b55500583e549780d17786e79e7ca64d7586e4a
UR - https://link.springer.com/chapter/10.1007/978-3-031-74443-3_18
U2 - 10.1007/978-3-031-74443-3_18
DO - 10.1007/978-3-031-74443-3_18
M3 - Conference publication
SN - 978-3-031-74442-6
T3 - Lecture Notes in Networks and Systems (LNNS)
SP - 293
EP - 314
BT - Contributions Presented at The International Conference on Computing, Communication, Cybersecurity and AI, July 3–4, 2024, London, UK: The C3AI 2024
A2 - Naik, Nitin
A2 - Jenkins, Paul
A2 - Prajapat, Shaligram
A2 - Grace, Paul
ER -