A Systematic Analysis of Diverse Large Language Models and Their Operational Paradigm

LLMs (Large Language Models) generated texts (e.g. Texts generated by Chat GPT) and its use has been growing rapidly where any language-related problems can be solved or any queries based on language translation can be answered easily. Some of the most well-known LLMs include OpenAI’s GPT models (GPT1, 2, 3.5, 4), Google’s BARD, BERT, Facebook’s RoBERTa and so on. Since natural language text is generated by such LLMs, it has the several possible issues associated with it. For example, our creativity will be faded away as all the ideas, codes and solutions are generated by these models. Therefore, accurate and efficient classifier tool is necessary to be formulated and implemented. Before developing a classifier tool, review of various LLMs will be done so that actual working of the large language models can be identified and used for further analysis of classifier model. LLM research has recently made tremendous strides in both academia and business industry, with ChatGPT’s introduction—a potent AI chatbot built on LLMs being a noteworthy milestone received a great deal of public interest. LLMs’ technical development has had a significant impact on AI community, which have fundamentally altered how we create and employ AI systems. Given this rapid advancement in technology, we evaluate current developments in LLMs in this survey by explaining the backdrop, major findings, and mainstream techniques. Pre-training, adaptation adjustment, use, and capacity evaluation—the four core LLM components—are the ones we focus on. We also discuss the difficulties that still need to be overcome in order to advance future advances, as well as the resources that are available for developing LLMs. For both academics and engineers, this study provides a current review of the LLM literature.