Attention Mechanism
The attention mechanism lets a model focus on different parts of its input when producing each part of the output. It is the core of transformer architectures and enables handling long sequences and rich context.
In Simple Terms
Think of it as a spotlight: for each word it generates, the model shines a light on the most relevant parts of the input.
Detailed Explanation
Instead of compressing everything into a fixed vector, attention computes weights over the input (e.g., which words matter for the current word). That allows the model to reference relevant context anywhere in the sequence. Variants include self-attention (within one sequence) and cross-attention (between two sequences). Attention is why modern LLMs can use long contexts and why they can appear to “focus” on specific passages. It is a key concept for understanding how transformers work.
Related Terms
Transformer
A transformer is a neural network architecture that uses attention to process sequences (e.g., text or tokens) in parallel rather than step-by-step. It underlies most large language models and many vision and multimodal systems.
Read moreFine-Tuning
Fine-tuning is training a pre-trained model on your own data so it gets better at specific tasks or styles while keeping its general abilities.
Read moreRetrieval-Augmented Generation (RAG)
RAG is a pattern where an AI model gets up-to-date or private information from a search step before answering, so answers are grounded in your data.
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