Mechanistic View of Transformers: Patterns, Messages, Residual Stream and LSTMs

A recent development in transformer models proposes shifting from traditional concatenation-based attention mechanisms to a decomposition-based approach, offering a novel perspective on how attention operates within neural networks. This method emphasizes breaking down the attention process into more interpretable components, potentially enhancing the understanding of message passing and residual streams in models like Transformers and LSTMs. By decomposing attention, researchers aim to improve model interpretability and efficiency, paving the way for more transparent and potentially more effective deep learning architectures.