Follow Up Question

Research on follow-up questions focuses on improving human-computer interaction and information retrieval by leveraging the power of iterative questioning. Current efforts concentrate on developing models, often based on large language models and transformer architectures, that generate relevant and insightful follow-up questions in various contexts, from medical diagnosis to conversational surveys and voice assistants. This work aims to enhance the accuracy and efficiency of information seeking, leading to improved user experiences and more effective knowledge acquisition in diverse applications. The ultimate goal is to create more natural and engaging interactions between humans and AI systems.