Synthetic Question

Synthetic question generation is a rapidly developing area focusing on creating artificial question-answer pairs to augment or evaluate question-answering (QA) systems. Current research emphasizes using synthetic data to improve model performance in various domains, including conversational QA, mathematical reasoning, and medical record analysis, often leveraging large language models and techniques like consistency training or iterative question composing to generate high-quality synthetic data. This approach addresses the limitations of relying solely on manually annotated datasets, offering a cost-effective and scalable method for improving QA model accuracy and robustness, particularly for low-resource or specialized domains.