What is a Transformer?
A transformer is a specific type of neural network architecture introduced in a 2017 research paper. It was designed to handle sequential data (like sentences) and revolutionized the field of artificial intelligence by allowing models to track relationships between words over long distances.
How does it work?
Older language models processed text sequentially, reading one word after another. The transformer introduced the "self-attention mechanism." This allows the model to process all the words in a sequence simultaneously and mathematical weigh how important every word is to every other word, regardless of how far apart they are in the sentence.
What is it commonly confused with?
Transformers are often confused with Large Language Models. The transformer is the architecture (the engine design). An LLM is the resulting product (the car built using that engine). Furthermore, while almost all modern LLMs use transformers, not every AI model is a transformer (image diffusion models use different architectures).
Why does it matter?
Because transformers process data simultaneously rather than sequentially, they are incredibly efficient to train on modern parallel hardware (GPUs). This efficiency allowed researchers to scale up training datasets from gigabytes to petabytes, directly leading to the creation of the massive foundation models we use today.