Multiple Choice Question Generation

Multiple-choice question (MCQ) generation aims to automate the creation of high-quality MCQs, a crucial task in education and assessment. Current research focuses on leveraging large language models (LLMs), particularly transformer-based architectures, often employing techniques like fine-tuning, retrieval-augmented generation, and multi-stage prompting to improve the quality and diversity of generated questions and distractors. A key challenge lies in ensuring the accuracy and educational value of generated MCQs, leading to the development of novel evaluation metrics that go beyond simple textual similarity. This automated MCQ generation holds significant potential for improving the efficiency and scalability of educational assessment and other applications requiring automated question generation.