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 these models to the computer education process. However, the hallucination problem associated with large language mode...
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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/thesisDetails#10.11959/j.issn.2096-0271.BDR25001 |
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| author | SUN Haoran WANG Zhihao WU Yifan XIANG Yang |
| author_facet | SUN Haoran WANG Zhihao WU Yifan 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 these models to the computer education process. However, the hallucination problem associated with large language models poses significant challenges to their application. To mitigate this issue, retrieval-augmented generation (RAG) techniques, by incorporating external knowledge bases, can effectively enhance the quality of responses generated by large language models. However, traditional RAG methods often struggle with filtering irrelevant external knowledge, leading to interference from unrelated information that fails to adequately address the hallucination problem. In this paper, we collect computer-related textbooks and knowledge documents, dividing them into knowledge document blocks to construct an external knowledge database. We introduce an large educational models question-answering enhancement method based on re-ranking and post-retrieval-augmented reflection. We utilize a high-performance multilingual re-ranking model based on a cross-encoder to capture deep semantic information for filtering retrieved information, thereby alleviating the shortcomings of traditional retrieval generation methods. Additionally, we apply retrieval-augmented generation for model reflection to further enhance response quality. This approach significantly improves the accuracy of large language models in computer question-answering tasks. Our 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-2ad37a179ca84cddbc358b1de68d575c |
| 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-2ad37a179ca84cddbc358b1de68d575c2025-08-20T03:25:07ZzhoChina 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 YifanXIANG 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 these models to the computer education process. However, the hallucination problem associated with large language models poses significant challenges to their application. To mitigate this issue, retrieval-augmented generation (RAG) techniques, by incorporating external knowledge bases, can effectively enhance the quality of responses generated by large language models. However, traditional RAG methods often struggle with filtering irrelevant external knowledge, leading to interference from unrelated information that fails to adequately address the hallucination problem. In this paper, we collect computer-related textbooks and knowledge documents, dividing them into knowledge document blocks to construct an external knowledge database. We introduce an large educational models question-answering enhancement method based on re-ranking and post-retrieval-augmented reflection. We utilize a high-performance multilingual re-ranking model based on a cross-encoder to capture deep semantic information for filtering retrieved information, thereby alleviating the shortcomings of traditional retrieval generation methods. Additionally, we apply retrieval-augmented generation for model reflection to further enhance response quality. This approach significantly improves the accuracy of large language models in computer question-answering tasks. Our 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/thesisDetails#10.11959/j.issn.2096-0271.BDR25001retrieval-augmented generationcomputer education |
| spellingShingle | SUN Haoran WANG Zhihao WU Yifan XIANG Yang Question Answering Enhancement Method for Large Educational Models Based on Re-ranking and Post-retrieval Reflection 大数据 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 | retrieval-augmented generation computer education |
| url | http://www.j-bigdataresearch.com.cn/thesisDetails#10.11959/j.issn.2096-0271.BDR25001 |
| work_keys_str_mv | AT sunhaoran questionansweringenhancementmethodforlargeeducationalmodelsbasedonrerankingandpostretrievalreflection AT wangzhihao questionansweringenhancementmethodforlargeeducationalmodelsbasedonrerankingandpostretrievalreflection AT wuyifan questionansweringenhancementmethodforlargeeducationalmodelsbasedonrerankingandpostretrievalreflection AT xiangyang questionansweringenhancementmethodforlargeeducationalmodelsbasedonrerankingandpostretrievalreflection |