Building an LLM from Scratch: Unraveling the Mystery of Attention

This post delves into the inner workings of the self-attention mechanism in large language models. The author analyzes multi-head attention and layered mechanisms, explaining how seemingly simple matrix multiplications achieve complex functionality. The core idea is that individual attention heads are simple, but through multi-head attention and layering, complex and rich representations are built. This is analogous to how convolutional neural networks extract features layer by layer, ultimately achieving a deep understanding of the input sequence. Furthermore, the post explains how attention mechanisms solve the inherent fixed-length bottleneck problem of RNN models and uses examples to illustrate the roles of query, key, and value spaces in the attention mechanism.