Examination Questions Generation System Based on Soft Knowledge Prompt and Large Language Model
Year: 2025
Type: conference-paper
Abstract: In the field of education, the traditional way of writing examination questions by hand has low efficiency and uneven quality. An examination question generation system based on soft knowledge prompt and large language model is proposed to address this difficulty. The large language model is used to enhance the examination question generation process of review materials. The key knowledge points are extracted and the soft knowledge prompt is generated through the feature representation and knowledge mining module. The soft knowledge prompt calculates the similarity score of external domain knowledge, selects the most relevant knowledge segment, and guides the large language model to generate examination questions through the adaptive fusion mechanism, so as to realize the automatic generation from review materials to examination questions, and improve the generation efficiency and quality of educational resources.
Authors: Zhan Tang, Xiaoyu Lu, Nian Yang, Xiaohong Xiang, Xiang Hou, Xiaoli Peng
Institution: Sichuan University of Arts and Science
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
OpenAlex ID: W4413985254