期刊論文
| 學年 | 112 |
|---|---|
| 學期 | 2 |
| 出版（發表）日期 | 2024-06-18 |
| 作品名稱 | Empowering Large Language Models to Leverage Domain-Specific Knowledge in E-Learning |
| 作品名稱（其他語言） | |
| 著者 | Ruei-Shan Lu; Ching-Chang Lin; Hsiu-Yuan Tsao. |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | Applied Sciences 14(12), 5264 |
| 摘要 | Large language models (LLMs) have demonstrated remarkable capabilities in various natural language processing tasks. However, their performance in domain-specific contexts, such as E-learning, is hindered by the lack of specific domain knowledge. This paper adopts a novel approach of retrieval augment generation to empower LLMs with domain-specific knowledge in the field of E-learning. The approach leverages external knowledge sources, such as E-learning lectures or research papers, to enhance the LLM’s understanding and generation capabilities. Experimental evaluations demonstrate the effectiveness and superiority of our approach compared to existing methods in capturing and generating E-learning-specific information. |
| 關鍵字 | LLM; domain-specific knowledge; E-learning |
| 語言 | en |
| ISSN | 2076-3417 |
| 期刊性質 | 國外 |
| 收錄於 | SCI |
| 產學合作 | |
| 通訊作者 | Ching-Chang Lin |
| 審稿制度 | 否 |
| 國別 | CHE |
| 公開徵稿 | |
| 出版型式 | ,電子版 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/128363 ) |