Question Generation

Automatic question generation (QG) focuses on using computational methods to create questions from given text, aiming to improve various applications like education, question-answering systems, and fact-checking. Current research emphasizes improving question quality (e.g., clarity, relevance, diversity), exploring cross-lingual transfer to address data scarcity in many languages, and leveraging large language models (LLMs) like GPT-3.5 and Llama 2, often incorporating techniques like contrastive learning and reinforcement learning to enhance performance. The advancements in QG have significant implications for creating more effective educational materials, building more robust conversational AI, and automating various knowledge-based tasks.