Lecture Notes: Understanding Transformers and Attention Mechanisms

Introduction

• Transformers are key technology in AI, especially for large language models.
• Originated from the 2017 paper "Attention is All You Need."
• Focus: Understand the attention mechanism and its role in data processing.

Goal of Transformers

• Predict the next word in a sequence of text.
• Text is broken into tokens, often words.
• Tokens are associated with high-dimensional vectors, called embeddings.

Embeddings and Semantic Meaning

• Directions in high-dimensional embedding space can reflect semantic meanings (e.g., gender).
• Transformers adjust embeddings for richer contextual meaning.

Attention Mechanism

• Allows context to refine the meaning of words (e.g., "mole" in different contexts).
• Uses embeddings to shift meanings based on context.
• Attention block updates embeddings to include contextual information.

Examples

• Example: "Eiffel tower" vs. "miniature tower" changing the vector direction.
• Attention allows rich information transfer beyond single words.

Computational Details

• Initial embeddings are vectors encoding word meaning and position.
• Process involves matrix-vector products with tunable weights.
• Queries and keys are derived from embeddings to form an attention pattern.

Attention Pattern

• Queries seek context (e.g., adjectives before nouns).
• Keys potentially answer queries (alignment checked via dot products).
• Dot products visualize relevance between words, creating an attention pattern.

Softmax and Masking

• Softmax converts values to probabilities for normalization.
• Masking prevents future tokens from influencing past tokens during training.

Multi-headed Attention

• Multiple heads run in parallel, each with distinct key, query, and value matrices.
• GPT-3 uses 96 attention heads per block.
• Each head contributes to refining embeddings by different contextual updates.

Parameter Count

• Key, query, and value matrices contribute to model parameters.
• GPT-3 has approximately 58 billion parameters for attention heads.

Practical Considerations

• Attention mechanism's parallelizability is a major advantage.
• Large-scale computations possible with GPUs.

Conclusion

• Attention mechanisms enable large models to perform efficiently.
• Further reading and resources are available for deeper understanding.

Resources

• Recommended: Andrej Karpathy, Chris Ola, Vivek's videos, and Britt Cruz's "The Art of the Problem."

Note:

Focus on understanding how transformers adjust word vectors through attention to process language with contextual nuances, leveraging parallel computing for efficiency.