KBLaM's core mechanism and open source value
KBLaM (Knowledge Base augmented Language Model) is developed by Microsoft, and its core innovation lies in transforming the external knowledge base into key-value vector pairs, which are directly embedded into the attention layer of the big language model through the rectangular attention mechanism. This architectural design allows the model to access structured knowledge in real-time without relying on traditional retrieval modules, reducing system complexity compared to retrieval augmented generation (RAG) techniques. The project is fully open-sourced on GitHub and contains code libraries, experimental scripts, and datasets for researchers who need to explore knowledge enhancement techniques. The open source feature makes it a benchmark platform for studying the knowledge fusion mechanism of large models, which is especially suitable for scientific research scenarios that need to verify the effect of knowledge embedding in specialized domains.
This answer comes from the articleKBLaM: An Open Source Enhanced Tool for Embedding External Knowledge in Large ModelsThe
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