Understanding Attention Mechanisms in Transformers

Understanding Attention Mechanisms in Transformers

• Goal: Enable a transformer to accurately predict the next word by leveraging contextual information.
• Tokenization: Input text is divided into tokens (words or parts of words).
• Embeddings: Each token has a high-dimensional vector embedding representing its meaning.
• Contextual Meaning: Embeddings are adjusted to represent richer contextual meaning beyond individual words.
• Attention Benefits: Attention captures semantic connections between words, updating embeddings accordingly.
• Example: "Mole": Different meanings in context, decoded by attention.
• Example: "Tower": Embedding refined by preceding words ("Eiffel," "miniature") for specific meaning.
• Attention Block: Crucial component processing embeddings based on contextual relationships.
• Final Vector: Predicts the next word, derived from the processed embeddings in the sequence.
• Single Head of Attention: Simplified example—adjectives modifying nouns.
• Query Vector: Represents a question about word relationships (e.g., noun's query checks for preceding adjectives).
• Key, Query, and Value Matrices: Tunable parameters, calculate the attention pattern.
• Key Matrix: Maps embeddings to a smaller space, providing "answers" to queries.
• Dot Product: Measures alignment between keys and queries.
• Attention Pattern: Grid of values showing each word's relevance to others in the context.
• Softmax Normalization: Normalizes the attention pattern to probabilities.
• Masking: Prevents later words from influencing earlier ones during training (unidirectional flow).
• Value Matrix: Provides information to update embeddings based on attention scores.
• Value Vectors: Represent the change applied to an embedding based on relevance to other words.
• Weighted Sum: Attention pattern weights combine value vectors from various words, updating embeddings.
• Multi-headed Attention: Multiple heads operate in parallel, enhancing performance.
• Parameter Efficiency: Parameter sharing (key, query, value) improves efficiency, especially with multiple heads.
• Value Down and Value Up Matrices: Reduce value matrix parameters while mapping embeddings correctly.
• Low-Rank Transformation: Value matrix constrained to low rank, enhancing efficiency.
• Self-Attention: Attention mechanism focusing on relationships within a single sequence.
• Cross-Attention: Attention considering relationships between two different sequences (e.g., translation).

Cross-Attention

• Cross-attention processes two different data types (e.g., language translation).
• Keys and queries come from different datasets.

Self-Attention

• Self-attention understands relationships between words within a sentence.
• Keys and queries come from the same dataset.
• Key, query, and value matrices are learned during training.

Multi-Headed Attention

• Multi-headed attention learns multiple word relationships.
• Uses multiple attention heads with their own key, query, and value matrices.
• GPT-3 has 96 attention heads per block.

Value Maps

• Value maps project value vectors into a smaller space.
• Composed of value down and value up matrices.
• Value up matrices frequently combined into a single output matrix for the multi-headed attention block.

Transformer Architecture

• Transformers use multiple layers of multi-headed attention blocks and multi-layer perceptrons (MLPs).
• Each layer refines understanding of word relationships.
• Deeper networks capture complex relationships.

Training Transformers

• Attention mechanisms are parallelizable, ideal for training on large datasets.
• Large-scale training significantly impacts model performance (accuracy and generalization).

This quiz explores the intricacies of attention mechanisms in transformers, focusing on how they enable accurate word prediction in text sequences. Learn about tokenization, embeddings, and the contextual meanings captured through attention. Gain insights into the mechanisms that allow transformers to decode semantic connections in language.