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\begin{document}

\title{Large Language Models: A Comprehensive Survey of its Applications,
Challenges, Limitations, and Future Prospects}



\author[1]{Muhammad Usman Hadi }%
\author[1]{qasem al tashi }%
\author[2,2,2,2]{Rizwan Qureshi }%
\author[1]{Abbas Shah }%
\author[1]{amgad muneer }%
\author[1]{Muhammad Irfan }%
\author[1]{Anas Zafar }%
\author[1]{Muhammad Bilal Shaikh }%
\author[1]{Naveed Akhtar }%
\author[1]{Jia Wu }%
\author[1]{Seyedali Mirjalili }%
\author[1]{Mubarak Shah }%
\affil[1]{Affiliation not available}%
\affil[2]{MD Anderson Cancer Center}%


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  \date{June 08, 2024}


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\begin{abstract}
Within the vast expanse of computerized language processing, a
revolutionary entity known as Large Language Models (LLMs) has emerged,
wielding immense power in its capacity to comprehend intricate
linguistic patterns and conjure coherent and contextually fitting
responses. Large language models (LLMs) are a type of artificial
intelligence (AI) that have emerged as powerful tools for a wide range
of tasks, including natural language processing (NLP), machine
translation, and question-answering. This survey paper provides a
comprehensive overview of LLMs, including their history, architecture,
training methods, applications, and challenges. The paper begins by
discussing the fundamental concepts of generative AI and the
architecture of generative pre- trained transformers (GPT). It then
provides an overview of the history of LLMs, their evolution over time,
and the different training methods that have been used to train them.
The paper then discusses the wide range of applications of LLMs,
including medical, education, finance, and engineering. It also
discusses how LLMs are shaping the future of AI and how they can be used
to solve real-world problems. The paper then discusses the challenges
associated with deploying LLMs in real-world scenarios, including
ethical considerations, model biases, interpretability, and
computational resource requirements. It also highlights techniques for
enhancing the robustness and controllability of LLMs, and addressing
bias, fairness, and generation quality issues. Finally, the paper
concludes by highlighting the future of LLM research and the challenges
that need to be addressed in order to make LLMs more reliable and
useful. This survey paper is intended to provide researchers,
practitioners, and enthusiasts with a comprehensive understanding of
LLMs, their evolution, applications, and challenges. By consolidating
the state-of-the-art knowledge in the field, this survey serves as a
valuable resource for further advancements in the development and
utilization of LLMs for a wide range of real-world applications. The
GitHub repo for this project is available at
https://github.com/anas-zafar/LLM-Survey%
\end{abstract}%



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