Transformative Trends in Generative AI: Harnessing Large Language Models for Natural Language Understanding and Generation - Patil, Dinesh D.; Dhotre, Dhanraj R.; Gawande, Gopal S.; Mate, Dipali S.; Shelke, Mayura V.; Bhoye, Tejaswini S. (International Journal of Intelligent Systems and Applications in Engineering, 2023)
The advent of Large Language Models (LLMs) has ushered in transformative trends in the field of Generative Artificial Intelligence (AI). These models, with billions of parameters, have demonstrated unparalleled capabilities in Natural Language Understanding (NLU) and Generation (NLG) tasks. This paper delves into the evolution of generative AI, emphasizing the pivotal role played by LLMs. We explore the mechanisms by which these models have revolutionized NLU and NLG through their capacity to process vast amounts of textual data and generate coherent and contextually relevant text. Additionally, we investigate the techniques and methodologies employed in harnessing the power of LLMs for various applications, ranging from chatbots and content generation to machine translation and sentiment analysis. Furthermore, we examine the challenges associated with LLM-based generative AI, such as ethical concerns, model bias, and the computational resources required for training and fine-tuning. Finally, we offer insights into the future directions of research in this domain, with a focus on optimizing LLMs for broader applications, mitigating their limitations, and ensuring their responsible deployment in real-world scenarios. This paper serves as a comprehensive overview of the current state of generative AI, shedding light on its potential to reshape the way we interact with and generate natural language content.