What is self-attention?

Self-attention refers to a mechanism used in natural language processing and other domains where a model must evaluate the importance of different parts of the input data relative to one another. Option B accurately describes self-attention as attention that considers the entire context in parallel. This means that each element in the input sequence can potentially attend to every other element, allowing the model to capture relationships and dependencies across the entire input.

In self-attention, each token in the input can weigh the significance of every other token simultaneously, rather than sequentially or in isolation. This parallel consideration of context enables models to better understand nuances and relationships within the data, leading to more nuanced representations in tasks like language understanding or generation.

The other options, while reflecting aspects of attention mechanisms, do not capture the full essence of what self-attention does. For example, calculating attention across a dataset implies a broader comparison, which isn’t specific to self-attention. Similarly, limiting attention to the most significant features or previous outputs only constrains the ability of the model to utilize all available information from the entire input context.