Question Answering System Based on Graph Neural Networks and Contrastive Learning Combined with Large Language Models
學年 113
學期 2
發表日期 2025-07-16
作品名稱 Question Answering System Based on Graph Neural Networks and Contrastive Learning Combined with Large Language Models
作品名稱（其他語言）
著者 Yu-Ting Yang; Syu-Jhih Jhang; Ai-Ling Liou; Chih-Yung Chang
作品所屬單位
出版者
會議名稱 IEEE ICCE-TW 2025
會議地點 Kaohsiung, Taiwan
摘要 In the era of information explosion, question-answering (QA) systems are crucial for efficient information retrieval. However, existing QA models face challenges in knowledge updating, semantic understanding, and computational efficiency. This study proposes a QA system integrating Graph Neural Networks (GNNs), Contrastive Learning, and Large Language Models (LLMs) within a Retrieval-Augmented Generation (RAG) framework. Our method enhances vector representation learning through GNNs and contrastive loss while leveraging RAG for efficient knowledge retrieval. Experimental results demonstrate significant improvements in accuracy and computational efficiency compared to baseline models.
關鍵字
語言 en
收錄於
會議性質 國際
校內研討會地點 無
研討會時間 20250716~20250718
通訊作者
國別 USA
公開徵稿
出版型式
出處
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/128902 )

SDGS 尊嚴就業與經濟發展,產業創新與基礎設施