Attention in transformers, visually explained | Chapter 6, Deep Learning

Tutorial: Understanding Attention Mechanism in Transformers

Step 1: Introduction to Transformers

• Transformers are a key technology in large language models and modern AI tools.
• Introduced in a 2017 paper called "Attention is All You Need."
• Transformers aim to predict the next word in a piece of text by breaking it into tokens and associating each token with a high-dimensional vector known as embedding.

Step 2: Understanding Token Embeddings

• Token embeddings represent semantic meaning in a high-dimensional space.
• Directions in the embedding space correspond to different meanings.
• Transformers adjust embeddings based on context to enrich their meaning.

Step 3: Role of Attention Mechanism

• The attention mechanism helps transformers update embeddings based on context.
• Attention allows the model to move information between embeddings, refining the meaning of words.
• Attention blocks consist of query, key, and value matrices that interact to update embeddings.

Step 4: Self-Attention Process

• Query and key matrices identify relevant tokens in the context.
• Dot products between keys and queries determine relevance.
• Softmax is applied to normalize relevance scores.
• Value matrix updates embeddings based on relevance, refining their meanings.

Step 5: Multi-Headed Attention

• Multiple attention heads run in parallel, each with distinct key, query, and value matrices.
• Each head produces proposed changes to embeddings based on context.
• Proposed changes from all heads are aggregated to update embeddings in a more refined manner.

Step 6: Parameter Count and Efficiency

• Each attention head involves key, query, and value matrices, contributing to the total parameter count.
• Multi-headed attention with multiple heads increases the number of parameters significantly.
• Efficient design ensures an equal distribution of parameters among key, query, and value matrices for each head.

Step 7: Further Concepts and Resources

• Transformers consist of multiple layers and operations like multi-layer perceptrons.
• Deep learning models like GPT-3 incorporate attention mechanisms in multiple layers.
• Explore additional resources and videos to deepen your understanding of attention mechanisms and transformers.

By following these steps, you can gain a comprehensive understanding of the attention mechanism in transformers as explained in the video "Attention in transformers, visually explained | Chapter 6, Deep Learning" by 3Blue1Brown.