KBLaM采用适配器（Adapter）设计模式，仅在基础模型（如Llama-3、Phi-3）的注意力层添加轻量级参数，不改变原有语言理解与生成能力。技术文档显示，其默认配置下的适配器参数量不足基础模型的0.3%，却能带来38%的知识问答准确率提升。这种低耦合架构既保留了预训练模型的核心能力，又通过知识投影矩阵实现知识检索功能。微软提供的基准测试表明，增强后的模型在科学常识问答数据集（如ScienceQA）上F1值达到0.81，较原模型提高27个百分点。
This answer comes from the articleKBLaM: An Open Source Enhanced Tool for Embedding External Knowledge in Large ModelsThe
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