Comparative Analysis of Large Language Models

Impressive performance has been achieved by large language models (LLMs) across multiple natural language processing tasks, such as text generation, machine translation, and question answering. The wide range of architectural designs and training datasets employed in large language models pose a challenge when it comes to evaluating their performance and exploring potential applications. The purpose of this paper is to present an all-inclusive examination of the latest developments in large language models, highlighting the comparison of various models in terms of parameters, accuracy metrics, and other significant attributes. In addition, this paper delves into the examination of the merits and drawbacks of various models, while marking out potential avenues for future exploration. According to the survey findings, it has been discovered that there is not a particular large language model program that can be considered as the ultimate choice for all tasks. Certain models like PaLM are exceptionally skilled at handling language tasks in a general sense. On the other hand, there exist models like GPT-3 that are specifically designed for tasks like text generation and machine translation, and they excel in those domains.