The attention mechanism is inspired by how humans read: we don’t give equal focus to every word-we concentrate on those most relevant to understanding the context. Neural networks apply the same principle, dynamically weighting parts of the input sequence based on relevance.
Originally introduced to overcome the limitations of models like Recurrent Neural Networkss and LSTMs, attention mechanisms significantly improve the handling of long-range dependencies in sequence tasks. They are now central to many modern NLP applications, including machine translation, text generation, and language understanding.
Why Attention Matters
In traditional sequence models, all information must be compressed into a single fixed-size vector, which leads to loss of context-especially for long inputs. Attention allows the model to:
- Focus selectively on relevant input tokens
- Dynamically adjust what it “attends” to at each prediction step
- Better capture dependencies across distant positions in a sequence
How Attention Works (Simplified)
- Score Calculation: Compute how relevant each token is to a given query token (e.g., using dot product).
- Weighting: Apply softmax to get attention weights (a probability distribution).
- Context Vector: Take the weighted sum of value vectors to produce a context-specific representation.
This mechanism enables the model to emphasize important tokens and de-emphasize irrelevant ones during prediction.