Attention Mechanism

The Attention Mechanism is a neural network component that allows models to dynamically focus on the most relevant parts of an input sequence when processing information. It is the core innovation that powers Transformer architectures and modern language models.

In traditional neural networks, all input elements are treated equally. Attention mechanisms solve this limitation by computing "attention scores" that determine how much weight to assign to each element when producing an output. This enables the model to:

• Capture Context: Understand that "bank" in "river bank" has a different meaning than "bank" in "savings bank" by looking at surrounding words.
• Handle Long Dependencies: Connect concepts that are far apart in text, such as pronouns to their antecedents across multiple sentences.
• Scale Efficiently: Process variable-length inputs without architectural changes, making it ideal for diverse text lengths.

There are several types of attention: self-attention (where a sequence attends to itself), cross-attention (where one sequence attends to another), and multi-head attention (using multiple parallel attention mechanisms to capture different relationships). The attention mechanism is what gives LLMs their remarkable ability to understand nuance, context, and complex relationships in language.