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: | TANG Bowen, MA Mingxuan, ZHANG Yining, LI Hourun, WEN Feifan, WANG Dabin, YANG Jia, MA Hao |
---|---|
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!
|
Similar Items
-
Enhancing Environmental Control in Broiler Production: Retrieval-Augmented Generation for Improved Decision-Making with Large Language Models
by: Marcus Vinicius Leite, et al.
Published: (2025-01-01) -
Research on campus question answering system supported by retrieval-augmented generation technology
by: JIA Chunyan, et al.
Published: (2024-11-01) -
CFP-AL: Combining Model Features and Prediction for Active Learning in Sentence Classification
by: Keuntae Kim, et al.
Published: (2025-01-01) -
An Innovative Solution to Design Problems: Applying the Chain-of-Thought Technique to Integrate LLM-Based Agents With Concept Generation Methods
by: Shijun Ge, et al.
Published: (2025-01-01) -
Evaluating Large Language Models for Optimized Intent Translation and Contradiction Detection Using KNN in IBN
by: Muhammad Asif, et al.
Published: (2025-01-01)