MLG 033 Transformers

Machine Learning Guide

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Background & Motivation

RNN Limitations: Sequential processing prevents full parallelization—even with attention tweaks—making them inefficient on modern hardware.
Breakthrough: “Attention Is All You Need” replaced recurrence with self-attention, unlocking massive parallelism and scalability.

Core Architecture

Layer Stack: Consists of alternating self-attention and feed-forward (MLP) layers, each wrapped in residual connections and layer normalization.
Positional Encodings: Since self-attention is permutation invariant, add sinusoidal or learned positional embeddings to inject sequence order.

Self-Attention Mechanism

Q, K, V Explained:
- Query (Q): The representation of the token seeking contextual info.
- Key (K): The representation of tokens being compared against.
- Value (V): The information to be aggregated based on the attention scores.
Multi-Head Attention: Splits Q, K, V into multiple “heads” to capture diverse relationships and nuances across different subspaces.
Dot-Product & Scaling: Computes similarity between Q and K (scaled to avoid large gradients), then applies softmax to weigh V accordingly.

Masking

Causal Masking: In autoregressive models, prevents a token from “seeing” future tokens, ensuring proper generation.
Padding Masks: Ignore padded (non-informative) parts of sequences to maintain meaningful attention distributions.

Feed-Forward Networks (MLPs)

Transformation & Storage: Post-attention MLPs apply non-linear transformations; many argue they’re where the “facts” or learned knowledge really get stored.
Depth & Expressivity: Their layered nature deepens the model’s capacity to represent complex patterns.

Residual Connections & Normalization

Residual Links: Crucial for gradient flow in deep architectures, preventing vanishing/exploding gradients.
Layer Normalization: Stabilizes training by normalizing across features, enhancing convergence.

Scalability & Efficiency Considerations

Parallelization Advantage: Entire architecture is designed to exploit modern parallel hardware, a huge win over RNNs.
Complexity Trade-offs: Self-attention’s quadratic complexity with sequence length remains a challenge; spurred innovations like sparse or linearized attention.

Training Paradigms & Emergent Properties

Pretraining & Fine-Tuning: Massive self-supervised pretraining on diverse data, followed by task-specific fine-tuning, is the norm.
Emergent Behavior: With scale comes abilities like in-context learning and few-shot adaptation, aspects that are still being unpacked.

Interpretability & Knowledge Distribution

Distributed Representation: “Facts” aren’t stored in a single layer but are embedded throughout both attention heads and MLP layers.
Debate on Attention: While some see attention weights as interpretable, a growing view is that real “knowledge” is diffused across the network’s parameters.

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