関連する実験動画

Updated: Sep 9, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

681

冗長なLLMセマンティクスから内在的な学習によって,見えない領域に向けて一般化されたゼロショット学習を可能にします

Jiaqi Yue1, Chunhui Zhao1, Jiancheng Zhao1

  • 1State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027, China.

Neural networks : the official journal of the International Neural Network Society
|August 28, 2025
PubMed
まとめ

この研究は,新しい領域のクラスを認識するために,クロスドメイン汎用ゼロショット学習 (CDGZSL) を導入します. 提案されたメタドメインアライメント・セマンティック・リファインメント (MDASR) 方法は,ドメインシフトとセマンティック情報不対称性を効果的に処理します.

キーワード:
一般的なゼロショット学習情報の非対称性大型言語モデルセマンティック・リファインメント見えない領域

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