Attention Mechanism
A neural network component that dynamically weighs the importance of different input elements when producing an output, enabling models to focus on relevant context.
In Depth
The attention mechanism is a fundamental component of modern neural networks that enables models to dynamically focus on the most relevant parts of their input when producing each element of their output. Rather than compressing an entire input sequence into a fixed-size representation, attention allows the model to selectively access different portions of the input with varying degrees of focus, dramatically improving performance on tasks involving long sequences and complex dependencies.
In the self-attention variant used by transformers, each position in a sequence computes three vectors: a query (what information it is looking for), a key (what information it contains), and a value (the actual content to retrieve). Attention scores are computed as the scaled dot product of queries and keys, then normalized via softmax to produce weights that are applied to the values. This mechanism enables each token to gather relevant information from all other tokens in the sequence.
Multi-head attention extends this by running several attention operations in parallel, each with different learned projections. This allows the model to attend to different types of relationships simultaneously, such as syntactic structure in one head and semantic meaning in another. The outputs from all heads are concatenated and projected to produce the final representation.
Advanced attention variants have been developed to address computational and memory challenges at scale. FlashAttention reformulates the attention computation to be IO-aware, reducing memory usage from quadratic to linear while maintaining exact results. Grouped query attention shares key-value heads across multiple query heads, reducing the memory footprint during inference. Sliding window attention limits each token to attending only to a local window, enabling efficient processing of very long sequences. Multi-query attention and cross-attention (used in encoder-decoder models) are additional variants that optimize for specific use cases and deployment constraints.
Related Terms
Transformer
A neural network architecture based on self-attention mechanisms that processes input sequences in parallel, forming the foundation of modern large language models.
Neural Network
A computing system inspired by biological neural networks, consisting of interconnected layers of nodes that learn patterns from data through training.
Deep Learning
A subset of machine learning using neural networks with many layers to automatically learn hierarchical representations from large amounts of data.
Large Language Model (LLM)
A neural network with billions of parameters trained on massive text corpora that can understand, generate, and reason about natural language.
Tokens
The fundamental units of text that language models process, representing words, subwords, or characters depending on the tokenization method.
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