A Survey on Large Language Models: Applications, Challenges, Limitations, and Practical Usage

Abstract

<p>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</p>

Digital Resurrection: Challenging the Boundary between Life and Death with Artificial Intelligence

“…They can interact through written or oral communication. With the integration of other generative AI techniques and the architecture of Pretrained Generative Transformers (GPTs) in Large Language Models (LLMs), not only is it possible to maintain smooth communication, but it is also possible to show empathy and improve coherence in the flow of contextually appropriate responses and questions, thanks to the understanding of linguistic patterns in the context of trust and usefulness [6]. This allows the system to train and refine itself to address user demands more accurately.…”

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

“…LLMs refer to a category of AI models developed specifically to comprehend and produce human language [19]. LLMs have significantly contributed to the field of AI and have been applied in diverse areas, including education, communication, content generation, article composition, healthcare, research, entertainment, and information dissemination, among others [19], [20]. The origins of LLMs can be attributed to the emergence and advancement of neural network-based methodologies in the field of NLP [20].…”

“…LLMs have significantly contributed to the field of AI and have been applied in diverse areas, including education, communication, content generation, article composition, healthcare, research, entertainment, and information dissemination, among others [19], [20]. The origins of LLMs can be attributed to the emergence and advancement of neural network-based methodologies in the field of NLP [20]. In order to process language, early NLP systems utilized rule-based techniques and statistical models.…”

“…Following the advent of transformers, subsequent years saw the development of scaling-up LLMs models through the expansion of training data and parameter counts [20]. OpenAI significantly contributed to the development of LLMs in 2018.…”

“…This proficiency is attributed to its extensive training on a vast number of parameters, specifically 117 million. The model underwent a two-step procedure consisting of unsupervised pre-training followed by supervised fine-tuning [20]. The initial iteration of GPT did not attain the same level of popularity as BERT due to several inherent limitations [40].…”