Question-answering enhancement method for large educational models based on re-ranking and post-retrieval reflection
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 l...
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China InfoCom Media Group
2025-01-01
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| Series: | 大数据 |
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| Online Access: | http://www.j-bigdataresearch.com.cn/zh/article/109538360/ |
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| author | SUN Haoran WANG Zhihao WU Yifan GAO Xiaoying XIANG Yang |
| author_facet | SUN Haoran WANG Zhihao WU Yifan GAO Xiaoying XIANG Yang |
| author_sort | SUN Haoran |
| collection | DOAJ |
| description | 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 their 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. |
| format | Article |
| id | doaj-art-00d6a6aea0d54f85b60d22e04ad4edb5 |
| institution | Kabale University |
| issn | 2096-0271 |
| language | zho |
| publishDate | 2025-01-01 |
| publisher | China InfoCom Media Group |
| record_format | Article |
| series | 大数据 |
| spelling | doaj-art-00d6a6aea0d54f85b60d22e04ad4edb52025-08-20T03:32:11ZzhoChina InfoCom Media Group大数据2096-02712025-01-01114109538360Question-answering enhancement method for large educational models based on re-ranking and post-retrieval reflectionSUN HaoranWANG ZhihaoWU YifanGAO XiaoyingXIANG YangComputer 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 their 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.http://www.j-bigdataresearch.com.cn/zh/article/109538360/large language modelretrieval-augmented generationcomputer education |
| spellingShingle | SUN Haoran WANG Zhihao WU Yifan GAO Xiaoying XIANG Yang Question-answering enhancement method for large educational models based on re-ranking and post-retrieval reflection 大数据 large language model retrieval-augmented generation computer education |
| title | Question-answering enhancement method for large educational models based on re-ranking and post-retrieval reflection |
| title_full | Question-answering enhancement method for large educational models based on re-ranking and post-retrieval reflection |
| title_fullStr | Question-answering enhancement method for large educational models based on re-ranking and post-retrieval reflection |
| title_full_unstemmed | Question-answering enhancement method for large educational models based on re-ranking and post-retrieval reflection |
| title_short | Question-answering enhancement method for large educational models based on re-ranking and post-retrieval reflection |
| title_sort | question answering enhancement method for large educational models based on re ranking and post retrieval reflection |
| topic | large language model retrieval-augmented generation computer education |
| url | http://www.j-bigdataresearch.com.cn/zh/article/109538360/ |
| work_keys_str_mv | AT sunhaoran questionansweringenhancementmethodforlargeeducationalmodelsbasedonrerankingandpostretrievalreflection AT wangzhihao questionansweringenhancementmethodforlargeeducationalmodelsbasedonrerankingandpostretrievalreflection AT wuyifan questionansweringenhancementmethodforlargeeducationalmodelsbasedonrerankingandpostretrievalreflection AT gaoxiaoying questionansweringenhancementmethodforlargeeducationalmodelsbasedonrerankingandpostretrievalreflection AT xiangyang questionansweringenhancementmethodforlargeeducationalmodelsbasedonrerankingandpostretrievalreflection |