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Updated: Feb 17, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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ニューロモルフィック・スパイクベースの大型言語モデル

Han Xu1,2,3, Xuerui Qiu1,4,5, Yunhui Xu6

  • 1Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.

National science review
|February 16, 2026
PubMed
まとめ

この研究は,統合された神経形状のスパイクベースの大型言語モデル (NSLLM) フレームワークを導入し,エネルギー消費を大幅に削減し,解釈性を向上させます. NSLLMフレームワークは,大型言語モデル (LLM) を効率的で解釈可能なニューラルダイナミクスに変換し,よりグリーンなAIへの道を開きます.

キーワード:
インターディシピナリの神経科学です.ニューロモルフィックコンピューティングスパイクベースのLLMです.線形的な注意をピークする.

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