Question Answer Generation

Question Answer Generation (QAG) focuses on automatically creating question-answer pairs from given text, aiming to improve question answering systems and augment datasets, particularly in low-resource domains. Current research emphasizes improving the diversity and quality of generated pairs, exploring techniques like explicit diversity conditions and few-shot prompting with large language models (LLMs) to control question attributes. This work is significant for expanding access to high-quality training data for question answering, leading to more accurate and robust systems across various applications, including education and information retrieval.