大数据2025，Vol.11Issue(5)：4-17,14.DOI:10.11959/j.issn.2096-0271.2025062
基于重排序和后检索反思的教育大模型问答增强方法
Question-answering enhancement method for large educational models based on re-ranking and post-retrieval reflection
摘要
Abstract
Computer education is one of the requirements of modern information society education.With the development of large language models,there has been increasing attention on applying of large language models to the computer education process.However,the hallucination problem associated with large language models poses significant challenges to its application.To solve the challenges,RAG techniques by incorporating external knowledge bases can effectively enhance the quality of responses generated by large language models.However,the traditional RAG techniques lack a fine screening mechanism for the retrieved information,which leads to the retention of a large amount of low-correlation knowledge,and the interference of irrelevant information makes the model hallucination problem not effectively solved.We collected computer-related textbooks and knowledge documents,dividing them into knowledge document blocks according to the content structure to construct an external knowledge database.On this base,we introduced the large educational models question-answering enhancement method based on re-ranking and post-retrieval reflection,which utilized a high-performance multilingual re-ranking model based on a cross-encoder to capture deep semantic information,filter the retrieval information,filter out irrelevant information to improve the retrieval quality.The proposed method applied RAG techniques for model reflection so that the model can further enhance the quality of the model's answers through self-examination,and effectively improve the accuracy of the large language model in computer question-answering.This approach significantly improves the accuracy of large language models in computer question-answering tasks.The proposed method has been tested on several popular current generative models,achieving promising results on CS-Bench,with an approximate 5%increase in accuracy for computer question-answering tasks.关键词
大语言模型/检索增强生成/计算机教育Key words
large language model/retrieval-augmented generation/computer education分类
信息技术与安全科学引用本文复制引用
孙浩然,王志豪,吴一帆,高晓影,向阳..基于重排序和后检索反思的教育大模型问答增强方法[J].大数据,2025,11(5):4-17,14.基金项目
国家自然科学基金项目(No.72071145) The National Natural Science Foundation of China(No.72071145) （No.72071145）
