KBLaM创新的矩形注意力（Rectangular Attention）机制通过解耦键值维度，将知识向量存储在独立的权重矩阵中。与传统自注意力机制不同，该设计允许知识键（K）维度远大于值（V）维度（实验采用2048:256的比例），形成’窄而深’的知识存储结构。技术白皮书显示，这种架构使模型在保持768维隐藏状态的同时，可支持超过100万条知识记录的快速检索，查询响应延迟控制在50ms以内（A100环境）。该机制已被证明在化学分子属性预测等需要精确检索专业知识的任务中，比标准Transformer注意力准确率提高19%。
This answer comes from the articleKBLaM: An Open Source Enhanced Tool for Embedding External Knowledge in Large ModelsThe
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