Alucinações em modelos de IA Generativa: limitações e desafios na compreensão da linguagem humana

Abstract

Chatbots are artificial intelligence systems that simulate human interactions and have been widely used in diverse areas, from customer service to content creation. However, despite advances in technology, chatbots still present limitations and challenges, such as the tendency to make mistakes and the difficulty in dealing with complex questions and nuances of human language. These errors are known as hallucinations and can be caused by several factors, such as a lack of common sense, biased data and limitations in the models' ability to deal with non-existent or unrepresented information in the data. Chatbot hallucinations can have significant impacts in several areas, such as education, customer service and content creation. For example, a chatbot that cannot understand the nuances of human language may provide inappropriate or incorrect responses, which can lead to a negative user experience. Additionally, chatbots can perpetuate social bias and discrimination if they are trained on biased data. To deal with these limitations in the educational context, it was proposed the development of a chatbot for teaching programming in Python that uses advanced natural language processing techniques to reduce hallucinations when learning the language.