What mechanism in transformers allows for handling long-range dependencies in data?

The attention mechanism is a crucial component in transformers that enables the model to focus on specific parts of the input data when processing information. This is particularly important for handling long-range dependencies, which are common in sequences, such as natural language. Traditional sequence processing models, like RNNs (Recurrent Neural Networks), struggled with these dependencies due to their sequential nature and limited memory.

With the attention mechanism, each element in the input sequence can weigh the relevance of all other elements, allowing the model to refer back to previous parts of the input effectively, regardless of their distance in the sequence. This capability allows transformers to capture complex relationships in the data and improve the understanding of context, which is essential for tasks like translation, text summarization, and more.

In contrast, reinforcement training, data normalization, and feature extraction serve different purposes. Reinforcement training focuses on optimizing decision-making processes, data normalization helps maintain consistency in the scale of input features, and feature extraction involves reducing the dimensionality of data for better processing. None of these mechanisms address the specific challenge of managing long-range dependencies as effectively as the attention mechanism does.