KBLaM: No more RAG?! · Data Alchemy

KBLaM: No more RAG?!

Large language models (LLMs) excel in reasoning, language comprehension, and creative tasks, but integrating external knowledge efficiently remains a challenge. Traditional methods like fine-tuning and Retrieval-Augmented Generation (RAG) are either costly or complex, while in-context learning struggles with scalability. To overcome these limitations, Microsoft introduces the Knowledge Base-Augmented Language Model (KBLaM), which embeds structured knowledge directly into LLMs using key-value vector pairs and a specialized rectangular attention mechanism. This approach enables implicit retrieval within the model, allowing for linear scalability and dynamic updates without retraining, offering a more efficient alternative to existing techniques.

https://www.microsoft.com/en-us/research/blog/introducing-kblam-bringing-plug-and-play-external-knowledge-to-llms/

Implementation: https://github.com/microsoft/KBLaM

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