Unanswerable Question

Research on unanswerable questions focuses on improving the reliability and robustness of AI models, particularly large language models (LLMs), by enabling them to accurately identify and handle questions that cannot be answered based on available information. Current efforts concentrate on developing benchmarks to evaluate model performance on unanswerable questions across various domains (e.g., visual question answering, knowledge base question answering, text-to-SQL) and creating methods for automatically generating high-quality unanswerable question datasets for training and evaluation. This work is crucial for building more trustworthy and reliable AI systems, mitigating the risk of misinformation and improving user experience in applications ranging from healthcare to information retrieval.