Integration of generative LLMs into the new generation of chatbots to enhance human-computer interaction

Abstract

Most conventional chatbots rely on strategies that extract information from databases and use predefined templates to generate responses, which poses a significant limitation in maintaining natural, rich, and contextually adapted dialogues. This study examines the enhancement of chatbots through the integration of application programming interfaces (APIs) from large pretrained language models (LLMs), focusing particularly on the GPT architecture. First, the conventional architectural paradigm of chatbots is described, followed by a description of the integration of GPT-based components. As a proof of concept, this enhanced architecture is implemented in a controlled environment, evaluating coherence, contextual relevance, and adaptability. Results, based on user opinions, indicate a significant improvement in the quality of interactions with the enhanced chatbot compared to its conventional counterpart. In conclusion, the integration of LLM APIs, in this case GPT, represents a notable advancement in dialogue systems, offering more contextual and adaptive responses. This study anticipates a relevant leap in chatbot technology, suggesting a paradigm shift towards more humanized and effective human-computer interactions in the coming years.