Transformer
The neural network architecture behind modern LLMs. Uses attention mechanisms to process sequences in parallel, enabling training on massive datasets.
In-Depth Explanation
The transformer architecture, introduced in the 2017 paper "Attention Is All You Need", revolutionised AI by enabling efficient processing of sequences without the limitations of recurrent networks.
Key innovations of transformers:
- Attention mechanism: Allows direct connections between any positions in a sequence
- Parallel processing: Unlike RNNs, processes all positions simultaneously
- Positional encoding: Injects position information into the parallel architecture
- Layer normalisation: Stabilises training of deep networks
Transformer components:
- Self-attention layers: Each position attends to all others
- Feed-forward layers: Process each position independently
- Residual connections: Help train very deep networks
- Multi-head attention: Multiple parallel attention operations
Why transformers won:
- Scale better with compute and data
- Train faster (parallelisable)
- Handle long-range dependencies
- Emerge with new capabilities at scale
Transformer variants:
- Encoder-only (BERT): Understanding tasks
- Decoder-only (GPT): Generation tasks
- Encoder-decoder (T5): Sequence-to-sequence tasks
Business Context
Transformers revolutionised AI in 2017. Understanding this architecture helps you appreciate LLM capabilities and limitations.
How Clever Ops Uses This
The transformer architecture underlies all the models we deploy for Australian businesses. Understanding its strengths and limitations helps us design effective AI solutions.
Example Use Case
"GPT, Claude, Llama, and virtually all modern language models use transformer architecture, demonstrating its dominance in the field."