Campus question-answering system based on intent recognition and retrieval-augmented generation
To address the issues of poor information integration and generalization in traditional campus question-answering systems, a campus question-answering system based on a large language model was designed. The fine-tuned model identified user intents and provided targeted solutions for various types o...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2024-11-01
|
| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024245/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | To address the issues of poor information integration and generalization in traditional campus question-answering systems, a campus question-answering system based on a large language model was designed. The fine-tuned model identified user intents and provided targeted solutions for various types of questions, enhancing the user experience. To mitigate the hallucination problem during language model generation, a knowledge base using diverse campus data was constructed and a retrieval-augmented generation method was employed to ensure factual accuracy. Experimental results indicate that the open-source large language model, after instruction tuning, achieves intent recognition accuracy that is comparable to or even surpasses that of closed-source models. |
|---|---|
| ISSN: | 1000-436X |