Question Generation Method

Question generation (QG) methods aim to automatically create questions from various inputs like text, knowledge bases, or summaries, serving diverse applications such as fact-checking, education, and information retrieval. Current research focuses on improving QG quality and relevance through techniques like contrastive learning, data augmentation, and the use of both large and smaller, fine-tuned language models, often incorporating strategies to generate diverse question types and handle long-form contexts. These advancements enhance the effectiveness of question answering systems and related tasks, impacting fields ranging from education and fact-checking to biomedical information retrieval.