An attention layer in large language models (LLMs) is a neural network component that enables the model to focus on the most relevant parts of the input when generating or interpreting output.
Key Features:
Context-aware weighting: Assigns different levels of importance (“attention scores”) to different input tokens based on their relevance to the current task.
Scales with input length: Allows the model to consider the entire input sequence, not just nearby words.
Foundation of transformers: Core to architectures like GPT and BERT.
How it works:
For each token, the attention layer computes a weighted combination of all tokens in the sequence using:
Queries, Keys, and Values—mathematical representations that determine how much attention each token gives or receives.
Self-attention: A mechanism where each token attends to all others in the same input.
The attention layer enables LLMs to understand context, handle long-range dependencies, and generate coherent, contextually relevant responses.
