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Large Language Models: A Comprehensive Survey of its Applications, Challenges, Limitations, and Future Prospects
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  • Muhammad Usman Hadi ,
  • qasem al tashi ,
  • Rizwan Qureshi ,
  • Abbas Shah ,
  • amgad muneer ,
  • Muhammad Irfan ,
  • Anas Zafar ,
  • Muhammad Bilal Shaikh ,
  • Naveed Akhtar ,
  • Jia Wu ,
  • Seyedali Mirjalili ,
  • Mubarak Shah
Muhammad Usman Hadi
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qasem al tashi
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Rizwan Qureshi
MD Anderson Cancer Center, MD Anderson Cancer Center, MD Anderson Cancer Center, MD Anderson Cancer Center

Corresponding Author:[email protected]

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Abbas Shah
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amgad muneer
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Muhammad Irfan
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Anas Zafar
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Muhammad Bilal Shaikh
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Naveed Akhtar
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Seyedali Mirjalili
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Mubarak Shah
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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