What mechanism allows transformers to weigh the importance of different inputs?

The attention mechanism is a core component of transformer models that enables them to effectively weigh the importance of different inputs relative to each other. It allows the model to focus on relevant parts of the input sequence when generating an output, making it particularly powerful for tasks like natural language processing.

In the attention mechanism, each input receives a score or weight that reflects its relevance to the current processing context. This function allows the transformer to dynamically adjust its focus across different tokens in a sequence, providing easier access to pertinent information. By computing these attention scores, the model can prioritize certain words or segments over others based on their contextual relevance, leading to an enhanced understanding of the overall input.

While normalization techniques, such as layer normalization, serve important roles in stabilizing the training of neural networks, they do not specifically address the task of determining the relevance of different inputs in the manner the attention mechanism does. Feedback loops are generally more associated with recurrent neural networks rather than transformers, where information from previous processing steps is fed back into the model.