Innovative Attention Architecture Explained
KBLaM's innovative Rectangular Attention (RA) mechanism stores knowledge vectors in separate weight matrices by decoupling the key-value dimensions. Unlike traditional self-attention mechanisms, the design allows the knowledge key (K) dimension to be much larger than the value (V) dimension (a ratio of 2048:256 was used for the experiments), creating a 'narrow and deep' knowledge storage structure. The technical whitepaper shows that this architecture allows the model to support fast retrieval of more than 1 million knowledge records with query response latency controlled to less than 50ms (A100 environment) while maintaining a 768-dimensional hidden state. The mechanism has been shown to improve attention accuracy by 191 TP3T over the standard Transformer in tasks such as chemical molecule property prediction that require precise retrieval of expertise.
This answer comes from the articleKBLaM: An Open Source Enhanced Tool for Embedding External Knowledge in Large ModelsThe
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