Architecture Evolution: From Bengio to Modern Transformers

Bengio's Neural Language Model

The Scaling Crisis

The Problem: Fixed Concatenation

All positions treated equally: No way to focus on relevant context
Parameter explosion: Input size grows with context length
Example calculation: 1024 context × 1024 dimensions = ~1M input size
Hidden layer: 1M × 1M = 1 trillion parameters

                            The Crisis: Completely impractical for real applications. Longer contexts meant exponentially more parameters.
                        

This approach worked for small experiments but couldn't scale to the long contexts needed for real language understanding.

Single-Layer Transformer

The Breakthrough Solution

The Solution: Dynamic Computation

Fixed parameters: Model size doesn't grow with context length
Dynamic attention: Computes relevance between positions on-the-fly
Knowledge storage: Feed-forward networks store learned patterns
Example result: ~60M parameters for any sequence length

                            The Breakthrough: Same 1024 context length processed with a tiny fraction of the parameters!
                        

This architecture separates parameter count from context length, enabling both efficiency and dynamic focus.

Multi-Layer Transformer

Scaling to Sophistication

The Power: Stacking for Sophistication

Linear scaling: Each layer adds similar parameter count
Refinement process: Each layer builds on the previous one's output
Example: 3 layers ≈ 180M parameters
Real models: Modern LLMs use 12-96+ layers with same principle

                            The Result: Complex reasoning and sophisticated language understanding emerge from stacking these simple, efficient building blocks.
                        

This is how we get from simple text prediction to systems that can reason, converse, and create.

The Revolutionary Impact

Bengio's Approach

~1T

Parameters needed for 1024 context length

Impractical for real applications

Transformer Approach

~60M

Parameters for ANY context length

Efficient and scalable

The Key Insight

Instead of storing all possible context combinations in parameters (Bengio's approach), transformers compute context relationships dynamically. This breakthrough solved the scaling crisis while enabling the dynamic focus that makes sophisticated language generation possible.