Nouman Ashraf discusses the architecture of transformers in large language models, emphasizing two key components: attention layers and feedforward layers. Attention layers map relationships between tokens (like words), while feedforward layers store knowledge that the model has learned through training. The encoder block processes input information layer by layer, highlighting relevant details and suppressing irrelevant ones, ultimately reaching a "concept space" where both user intent and contextual information are identified. This architecture allows transformers to efficiently process and generate responses without the computational bottlenecks seen in recurrent neural networks (RNNs).
Read the full article at Microsoft Research
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