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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

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アグリゲートベースの光インクリメンタルシャードリングは,推奨システムのための効率的な埋め込みテーブル管理を目的としています

Chao Kong1,2, Jiahui Chen3,4, Dan Meng5

  • 1School of Computer and Information, Anhui Polytechnic University, Wuhu, 241000, China. kongchao@ahpu.edu.cn.

Scientific reports
|August 28, 2025
PubMed
まとめ

この研究は,推奨システムに効率的なダイナミックエンベディングテーブルシャードリングのための新しい方法である,集積ベースのライトインクリメンタルシャードリング (ALIS) を導入します. ALISは,ネットワークのオーバーヘッドを大幅に削減し,リアルタイムアプリケーションの推論効率を高めます.

キーワード:
ディープエンベディングモデル分布した推論モデル並列化推奨者システム

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