AI based multilingual chatbot: A review on multilingual AI chatbot using transformer

The rapid advancement of Artificial Intelligence (AI) and Natural Language Processing (NLP) has significantly influenced the development of conversational agents. Multilingual chatbots, enabled by transformer-based architectures, address the critical challenge of cross-linguistic communication in domains such as education, healthcare, and customer service. Unlike traditional rule-based or LSTM-based models, transformer models leverage self-attention mechanisms to provide contextual understanding, scalability, and superior performance in multilingual settings. This paper presents a consolidated review of existing research on multilingual AI chatbots, focusing on their architectures, applications, and challenges. Prior studies have shown effective use of machine translation systems, integration with large language models, and reinforcement learning strategies to enhance dialogue quality. However, persistent gaps remain in cultural adaptability, low-resource language support, and bias mitigation. The paper highlights the need for advanced research to develop robust, culturally aware, and resource-efficient multilingual chatbots. The insights presented serve as a roadmap for future research, demonstrating the transformative role of transformer-driven chatbots in bridging global communication barriers.