What mechanism within the transformer architecture allows the model to focus on different parts of the input sequence when generating each word?

The attention mechanism within the Transformer architecture allows the model to focus on different parts of the input sequence when generating each word. Attention enables the model to weigh the importance of different words in the input sequence relative to the word being generated. It does this by calculating attention weights, which represent the strength of the relationship between each input word and the current output word. These weights are then used to create a weighted sum of the input word embeddings, effectively allowing the model to focus on the most relevant parts of the input sequence. For example, when generating the word 'he' in the sentence 'The cat chased the mouse because he was hungry,' the attention mechanism would likely assign higher weights to 'cat' and 'mouse' than to 'chased' or 'because,' indicating that 'he' refers to the cat or the mouse. This dynamic weighting allows the model to capture long-range dependencies and understand the context of each word in the sequence, leading to more accurate and coherent text generation. Without attention, the model would treat all words equally, making it difficult to understand relationships and generate meaningful output.