| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2025-09-30 |
| タイトル |
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タイトル |
GenKP: generative knowledge prompts for enhancing large language models |
| 言語 |
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言語 |
eng |
| キーワード |
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主題Scheme |
Other |
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主題 |
Large language models |
| キーワード |
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主題Scheme |
Other |
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主題 |
Knowledge graph |
| キーワード |
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主題Scheme |
Other |
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主題 |
Knowledge prompts |
| キーワード |
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主題Scheme |
Other |
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主題 |
In-context learning |
| 資源タイプ |
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資源タイプ |
journal article |
| アクセス権 |
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アクセス権 |
open access |
| 著者 |
Li, Xinbai
Peng, Shaowen
矢田, 竣太郎
若宮, 翔子
荒牧, 英治
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| 抄録 |
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内容記述タイプ |
Abstract |
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内容記述 |
Large language models (LLMs) have demonstrated extensive capabilities across various natural language processing (NLP) tasks. Knowledge graphs (KGs) harbor vast amounts of facts, furnishing external knowledge for language models. The structured knowledge extracted from KGs must undergo conversion into sentences to align with the input format required by LLMs. Previous research has commonly utilized methods such as triple conversion and template-based conversion. However, sentences converted using existing methods frequently encounter issues such as semantic incoherence, ambiguity, and unnaturalness, which distort the original intent, and deviate the sentences from the facts. Meanwhile, despite the improvement that knowledge-enhanced pre-training and prompt-tuning methods have achieved in small-scale models, they are difficult to implement for LLMs in the absence of computational resources. The advanced comprehension of LLMs facilitates in-context learning (ICL), thereby enhancing their performance without the need for additional training. In this paper, we propose a knowledge prompts generation method, GenKP, which injects knowledge into LLMs by ICL. Compared to inserting triple-conversion or templated-conversion knowledge without selection, GenKP entails generating knowledge samples using LLMs in conjunction with KGs and makes a trade-off of knowledge samples through weighted verification and BM25 ranking, reducing knowledge noise. Experimental results illustrate that incorporating knowledge prompts enhances the performance of LLMs. Furthermore, LLMs augmented with GenKP exhibit superior improvements compared to the methods utilizing triple and template-based knowledge injection. |
| 書誌情報 |
en : Applied Intelligence
巻 55,
号 7,
ページ数 15,
発行日 2025-02-19
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| 出版者 |
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出版者 |
Springer |
| ISSN |
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収録物識別子タイプ |
EISSN |
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収録物識別子 |
1573-7497 |
| 出版者版DOI |
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関連タイプ |
isReplacedBy |
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識別子タイプ |
DOI |
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関連識別子 |
https://doi.org/10.1007/s10489-025-06318-3 |
| 出版者版URI |
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関連タイプ |
isReplacedBy |
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識別子タイプ |
URI |
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関連識別子 |
https://link.springer.com/article/10.1007/s10489-025-06318-3 |
| 権利 |
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権利情報Resource |
https://creativecommons.org/licenses/by-nc-nd/4.0/ |
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権利情報 |
© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. |
| 著者版フラグ |
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出版タイプ |
NA |
| 助成情報 |
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助成機関名 |
National Center for Global Health and Medicine (NCGM) |
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研究課題番号 |
JPJ012425 |
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研究課題名 |
Cross-ministerial Strategic Innovation Promotion Program (SIP) on “Integrated Health Care System” |
