Analysing LSTM and Transformer Models for Chatbot Development: A Comparative Study

In today’s digital landscape, the development of chatbots plays a pivotal role in enhancing user engagement across various applications. This paper conducts a comparative study between Long Short-Term Memory (LSTM) and Transformer models for chatbot development, focusing on their efficacy in generating contextually coherent responses. LSTM, known for its sequential data processing capabilities, is compared with Transformers, which excel in capturing long-range dependencies and context understanding. Leveraging the Hugging Face Transformers library and TensorFlow/Keras for implementation, we evaluate metrics such as accuracy, perplexity, and computational efficiency on structured conversational datasets. Through empirical experiments and evaluations, this study provides insights into optimal model selection and training strategies to improve chatbot performance in real-world applications. The findings contribute to understanding the strengths and limitations of each architecture, informing best practices for developing robust conversational AI systems.