虽然KBLaM在学术实验中表现出色，但官方明确建议其当前阶段适用于研究用途。主要限制包括：知识嵌入质量依赖源数据的结构化程度，非规范化的知识条目可能导致回答偏差（测试显示噪声数据会使准确率下降40%）；处理超大规模知识库时需要80GB以上显存的高端GPU；且系统缺乏成熟的置信度校准机制，可能对超出知识库范围的问题产生过度自信的回答。这些特性使其更适合可控的科研环境，例如微软团队已将其用于探索医疗知识图谱与LLM的结合效能。
This answer comes from the articleKBLaM: An Open Source Enhanced Tool for Embedding External Knowledge in Large ModelsThe
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