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Updated: Sep 13, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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LS-PRISM:一种通过低级近似和散散的层选择性修剪方法,用于高效的大型语言模型压缩

Renshuai Tao1, Hairong Chen1, Yuzhe Guo1

  • 1Beijing Jiaotong University, Beijing, 100044, China.

Neural networks : the official journal of the International Neural Network Society
|August 1, 2025
PubMed
概括

我们开发了LS-PRISM,这是一种通过选择性修剪层来压缩大型语言模型 (LLM) 的新方法. 这种技术可以显著减少模型大小,同时在NLP任务上保持高性能.

关键词:
大型语言模型 (LLM)较低级别的近似估计.模型的压缩压缩.化 (Sparsification) 是一种化的过程.没有结构的修剪.

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