Paper Contents
Abstract
Transformer models have shifted the growth curve for the chatbot industry, ensuring accuracy and efficiency never known before within the systems used in conversational AI. Unlike earlier designs, based on the predefined script or sequential model like RNNs, Transformer-based models support the self-attention mechanism with the processing of language. All of these enable chatbots to engage with multi-turn dialogues, contextual situations, and coherent, human-like responses in many application domains. The strength of the chatbots in executing diversified tasks from customer support to educational tutoring has evolved due to the advancement of architectures such as GPT, an acronym for Generative Pre-trained Transformer, and BERT, an acronym for Bidirectional Encoder Representations from Transformers, which are fine-tuned for certain domain applications.Current trends are hybrid approaches through the use of Transformers combined with reinforcement learning techniques that are specifically designed to optimize the multi-modal systems, especially text, image, and voice data for better interaction. All these developments have challenges in them, including high computational demands in processing data, good quality datasets, and diversification in the responses generated. This paper reviews leading-edge approaches and their implications for the future of conversational AI. This paper discusses the developments and challenges, giving an all-around review of Transformer-based chatbot technologies and their outlooks for the future of conversational AI. Keywords: Transformer Models, Natural Language Processing, Chatbots, Self-Attention Mechanisms, GPT, BERT, Reinforcement Learning, Multi-modal Chatbots.
Copyright
Copyright © 2024 Divya Meena. This is an open access article distributed under the Creative Commons Attribution License.
