KBLaM（Knowledge Base augmented Language Model）由微软研发，其核心创新在于将外部知识库转化为键值向量对，通过矩形注意力机制直接嵌入大语言模型的注意力层。这种架构设计使得模型无需依赖传统检索模块就能实时访问结构化知识，相比检索增强生成（RAG）技术减少了系统复杂度。该项目已在GitHub完整开源，包含代码库、实验脚本及数据集，主要面向需要探索知识增强技术的研究人员。开源特性使其成为研究大模型知识融合机制的基准平台，特别适合需要验证专业领域知识嵌入效果的科研场景。
This answer comes from the articleKBLaM: An Open Source Enhanced Tool for Embedding External Knowledge in Large ModelsThe
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