A Review on Large Language Models: Architectures, Applications, Taxonomies, Open Issues and Challenges

Large Language Models (LLMs) recently demonstrated extraordinary capability, including natural language processing (NLP), language translation, text generation, question answering, etc. Moreover, LLMs are a new and essential part of computerized language processing, having the ability to understand...

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Vydáno v:	IEEE access Ročník 12; s. 1
Hlavní autoři:	Raiaan, Mohaimenul Azam Khan, Mukta, Md. Saddam Hossain, Fatema, Kaniz, Fahad, Nur Mohammad, Sakib, Sadman, Mim, Most. Marufatul Jannat, Ahmad, Jubaer, Ali, Mohammed Eunus, Azam, Sami
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 01.01.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Agriculture Application Artificial intelligence Automatic text generation Biomedicine Challenges Cognition Community research Computerization Evolution Health care Health services Information analysis Language Language modeling Language translation Large Language Models Large language models (LLM) Linguistics Natural language generation Natural language processing natural language processing (NLP) pre-trained models Pretrained models Question answering (information retrieval) Surveys Task analysis Taxonomy Training Transformer Transformers Translation World problems
ISSN:	2169-3536, 2169-3536
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	Large Language Models (LLMs) recently demonstrated extraordinary capability, including natural language processing (NLP), language translation, text generation, question answering, etc. Moreover, LLMs are a new and essential part of computerized language processing, having the ability to understand complex verbal patterns and generate coherent and appropriate replies for the situation. Though this success of LLMs has prompted a substantial increase in research contributions, rapid growth has made it difficult to understand the overall impact of these improvements. Since a lot of new research on LLMs is coming out quickly, it is getting tough to get an overview of all of them in a short note. Consequently, the research community would benefit from a short but thorough review of the recent changes in this area. This article thoroughly overviews LLMs, including their history, architectures, transformers, resources, training methods, applications, impacts, challenges, etc. This paper begins by discussing the fundamental concepts of LLMs with its traditional pipeline of the LLMs training phase. It then provides an overview of the existing works, the history of LLMs, their evolution over time, the architecture of transformers in LLMs, the different resources of LLMs, and the different training methods that have been used to train them. It also demonstrated the datasets utilized in the studies. After that, the paper discusses the wide range of applications of LLMs, including biomedical and healthcare, education, social, business, and agriculture. It also illustrates how LLMs create an impact on society and shape the future of AI and how they can be used to solve real-world problems. Then it also explores open issues and challenges to deploying LLMs in real-world scenario. Our review paper aims to help practitioners, researchers, and experts thoroughly understand the evolution of LLMs, pre-trained architectures, applications, challenges, and future goals.
AbstractList	Large Language Models (LLMs) recently demonstrated extraordinary capability in various natural language processing (NLP) tasks including language translation, text generation, question answering, etc. Moreover, LLMs are new and essential part of computerized language processing, having the ability to understand complex verbal patterns and generate coherent and appropriate replies in a given context. Though this success of LLMs has prompted a substantial increase in research contributions, rapid growth has made it difficult to understand the overall impact of these improvements. Since a plethora of research on LLMs have been appeared within a short time, it is quite impossible to track all of these and get an overview of the current state of research in this area. Consequently, the research community would benefit from a short but thorough review of the recent changes in this area. This article thoroughly overviews LLMs, including their history, architectures, transformers, resources, training methods, applications, impacts, challenges, etc. This paper begins by discussing the fundamental concepts of LLMs with its traditional pipeline of the LLMs training phase. Then the paper provides an overview of the existing works, the history of LLMs, their evolution over time, the architecture of transformers in LLMs, the different resources of LLMs, and the different training methods that have been used to train them. The paper also demonstrates the datasets utilized in the studies. After that, the paper discusses the wide range of applications of LLMs, including biomedical and healthcare, education, social, business, and agriculture. The study also illustrates how LLMs create an impact on society and shape the future of AI and how they can be used to solve real-world problems. Finally, the paper also explores open issues and challenges to deploy LLMs in real-world scenario. Our review paper aims to help practitioners, researchers, and experts thoroughly understand the evolution of LLMs, pre-trained architectures, applications, challenges, and future goals. Large Language Models (LLMs) recently demonstrated extraordinary capability, including natural language processing (NLP), language translation, text generation, question answering, etc. Moreover, LLMs are a new and essential part of computerized language processing, having the ability to understand complex verbal patterns and generate coherent and appropriate replies for the situation. Though this success of LLMs has prompted a substantial increase in research contributions, rapid growth has made it difficult to understand the overall impact of these improvements. Since a lot of new research on LLMs is coming out quickly, it is getting tough to get an overview of all of them in a short note. Consequently, the research community would benefit from a short but thorough review of the recent changes in this area. This article thoroughly overviews LLMs, including their history, architectures, transformers, resources, training methods, applications, impacts, challenges, etc. This paper begins by discussing the fundamental concepts of LLMs with its traditional pipeline of the LLMs training phase. It then provides an overview of the existing works, the history of LLMs, their evolution over time, the architecture of transformers in LLMs, the different resources of LLMs, and the different training methods that have been used to train them. It also demonstrated the datasets utilized in the studies. After that, the paper discusses the wide range of applications of LLMs, including biomedical and healthcare, education, social, business, and agriculture. It also illustrates how LLMs create an impact on society and shape the future of AI and how they can be used to solve real-world problems. Then it also explores open issues and challenges to deploying LLMs in real-world scenario. Our review paper aims to help practitioners, researchers, and experts thoroughly understand the evolution of LLMs, pre-trained architectures, applications, challenges, and future goals.
Author	Fahad, Nur Mohammad Ali, Mohammed Eunus Sakib, Sadman Azam, Sami Fatema, Kaniz Mim, Most. Marufatul Jannat Raiaan, Mohaimenul Azam Khan Ahmad, Jubaer Mukta, Md. Saddam Hossain
Author_xml	– sequence: 1 givenname: Mohaimenul Azam Khan orcidid: 0009-0006-4793-5382 surname: Raiaan fullname: Raiaan, Mohaimenul Azam Khan organization: Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh – sequence: 2 givenname: Md. Saddam Hossain orcidid: 0000-0003-2675-5471 surname: Mukta fullname: Mukta, Md. Saddam Hossain organization: Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh – sequence: 3 givenname: Kaniz surname: Fatema fullname: Fatema, Kaniz organization: Faculty of Science and Technology, Charles Darwin University, Australia – sequence: 4 givenname: Nur Mohammad surname: Fahad fullname: Fahad, Nur Mohammad organization: Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh – sequence: 5 givenname: Sadman orcidid: 0009-0007-2007-5746 surname: Sakib fullname: Sakib, Sadman organization: Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh – sequence: 6 givenname: Most. Marufatul Jannat orcidid: 0009-0000-7138-2331 surname: Mim fullname: Mim, Most. Marufatul Jannat organization: Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh – sequence: 7 givenname: Jubaer surname: Ahmad fullname: Ahmad, Jubaer organization: Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh – sequence: 8 givenname: Mohammed Eunus orcidid: 0000-0002-0384-7616 surname: Ali fullname: Ali, Mohammed Eunus organization: Department of CSE, Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh – sequence: 9 givenname: Sami orcidid: 0000-0001-7572-9750 surname: Azam fullname: Azam, Sami organization: Faculty of Science and Technology, Charles Darwin University, Australia
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Snippet	Large Language Models (LLMs) recently demonstrated extraordinary capability, including natural language processing (NLP), language translation, text... Large Language Models (LLMs) recently demonstrated extraordinary capability in various natural language processing (NLP) tasks including language translation,...
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SubjectTerms	Agriculture Application Artificial intelligence Automatic text generation Biomedicine Challenges Cognition Community research Computerization Evolution Health care Health services Information analysis Language Language modeling Language translation Large Language Models Large language models (LLM) Linguistics Natural language generation Natural language processing natural language processing (NLP) pre-trained models Pretrained models Question answering (information retrieval) Surveys Task analysis Taxonomy Training Transformer Transformers Translation World problems
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Title	A Review on Large Language Models: Architectures, Applications, Taxonomies, Open Issues and Challenges
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