What is a Transformer Model?
Definition
A Transformer model is a neural network architecture introduced in the 2017 paper "Attention Is All You Need" that uses self-attention mechanisms to process sequential data in parallel rather than sequentially. This revolutionary architecture is the foundation of modern large language models like GPT, BERT, and Gemini.
Key Components
- Self-Attention: Allows model to weigh importance of different parts of input
- Positional Encoding: Adds sequence order information to input
- Feed-Forward Layers: Process attention outputs
- Layer Normalization: Stabilizes training
Transformer Timeline
- 2017: "Attention Is All You Need" - original Transformer paper
- 2018: BERT (Google) - bidirectional understanding
- 2018: GPT (OpenAI) - generative pretrained transformer
- 2020: GPT-3 - 175B parameters, few-shot learning
- 2023: GPT-4, Claude 2, Gemini - multi-modal, longer context
- 2024: GPT-4o, Claude 3.5, Gemini 1.5 - real-time reasoning