Clarification Question

Clarification questions in human-computer interaction aim to improve the accuracy and efficiency of information retrieval and task completion by addressing ambiguities in user requests. Current research focuses on developing methods for large language models (LLMs) to strategically generate and utilize clarification questions, often employing reinforcement learning and fine-tuning techniques to optimize question selection and improve downstream task performance, such as code generation or open-domain question answering. These advancements are significant because they enable more natural and effective interactions between users and AI systems, leading to improved user experience and more accurate results across various applications.