Book2QA: A Framework for Integrating LLMs to Generate High-quality QA Data from Textbooks

The scarcity of high-quality question answering (QA) data remains a significant bottleneck in the development of intelligent educational systems, as traditional datasets lack the necessary scale and diversity to support personalized model training. To address this challenge, we propose the Book2QA framework, which integrates multiple language models to provide an effective and flexible approach for generating QA datasets derived from textbook content. To further enhance the quality of the generated data, we implement a hierarchical prompting strategy grounded in Bloom's taxonomy, substantially increasing both the depth and breadth of the QA datasets. In addition, we fine-tune our model using these data, with evaluations conducted by both human reviewers and GPT-4 confirming its strong performance in real-world questioning scenarios. Experimental results demonstrate that our framework excels not only in specific textbook domains but also shows promise for broader applications across diverse fields. We open source our data and code at https://github.com/Curtain2020/Book2QA.