Research on Educational Knowledge Base Question Answering System Based on Large Language Model

In recent years, methods based on pre-trained large models have received extensive attention in the field of natural language processing. The large language model has the potential to reduce the development cost of question answering systems in the education field and improve the accuracy of question answering system. In this paper, based on the curriculum standards of compulsory education, an educational knowledge base is constructed by using the method of label alignment optimization, and a professional large language model question answering system is constructed based on the large language model to meet the requirements of the new curriculum standards, so as to generate a high-availability teaching scheme. Secondly, a prompt word method based on educational keyword template is proposed, aiming at improving the quality of question answering generation of large language model in educational question answering system. The experimental results show that this system is superior to the universal large language model in both objective and subjective evaluation, and has higher usability in the field of education and more accuracy in generating answers.