Analytical Study of Pre-trained Transformers in Natural Language Generation

The paper presents an overview of how conversational agents have evolved over time from rule-based to complex models of NLP to address issues in creating an agent that understands the user's intentions and responds in a natural way. How these different applications of machine learning, deep neural networks, and reinforcement learning helped to maintain the contexts, understand the languages better, generate responses with ethical considerations are discussed hereafter for improving the user experience. This paper contributes valuable insights into the field of AI chatbot development by fusing existing technologies with future opportunities and manages to come out with a pathway for innovations further down the track of conversational AI.