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🎯 Multi-Head Attention

Understanding the core mechanism powering modern transformers

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Positional Encoding Deep Dive

Introduction to Attention

🎯 What is Attention?

Attention is a mechanism that allows models to focus on relevant parts of the input when processing each element. Instead of treating all inputs equally, attention weighs their importance dynamically.

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Key Insight

When reading "The cat sat on the mat," we naturally pay more attention to "cat" and "mat" when understanding "sat." Attention mechanisms replicate this in neural networks.

🔄 Why Multi-Head?

Single attention can only learn one type of relationship. Multi-head attention runs multiple attention operations in parallel, each learning different patterns:

Head 1: Syntax

Learns grammatical relationships like subject-verb agreement

Head 2: Semantics

Captures meaning and context between related concepts

Head 3: Long-range

Connects distant words that reference each other

Head 4: Local

Focuses on adjacent words and immediate context

📊 Historical Context

2014: Neural Machine Translation

Bahdanau et al. introduce attention for seq2seq models

2017: Attention Is All You Need

Vaswani et al. introduce multi-head self-attention in transformers

2018-Present: Transformer Era

BERT, GPT, T5, and modern LLMs all use multi-head attention

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Parallel Processing

All positions processed simultaneously, enabling efficient training

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Long-range Dependencies

Direct connections between any positions, regardless of distance

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Interpretability

Attention weights reveal what the model focuses on