Transformer Models

The Transformer's Encoder-Decoder architecture operates through a sophisticated interplay of stacked layers, each contributing to the model's ability to process and generate complex sequences. The Encoder is typically composed of a stack of identical layers, each containing two sub-layers: a multi-head self-attention mechanism and a position-wise fully connected feed-forward network. The self-attention layer allows the encoder to weigh the importance of different words in the input sequence relative to each other, creating a rich contextual representation for each word. This is crucial for understanding nuances and ambiguities in language. The output of each sub-layer is then passed through a residual connection and layer normalization. The final output of the encoder stack is a set of context-rich representations, often referred to as 'keys' and 'values,' which encapsulate the entire input sequence's meaning. The Decoder, also a stack of identical layers, introduces a third sub-layer: a multi-head cross-attention mechanism. This cross-attention layer allows the decoder to attend to the output of the encoder stack, effectively 'looking at' the encoded input representation while generating its own output. This is where the encoder and decoder truly interact. The decoder also includes a masked multi-head self-attention layer to prevent it from attending to subsequent positions in its own output sequence during training, ensuring it only uses previously generated tokens. The final layer of the decoder is a linear layer followed by a softmax function, which outputs probabilities for the next token in the sequence. This intricate flow ensures that the decoder generates an output that is not only grammatically correct but also semantically aligned with the input provided by the encoder. For those looking to delve deeper into the core attention mechanism, our guide on Understanding the Self-Attention Mechanism in Transformers provides a comprehensive breakdown.

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