A Transformer is a neural network architecture. It's important not because of the name, but because it does a good job of "parallel processing" and "contextual modeling". Most of the large language models you see today are inseparable from it or its variants.
Before Transformer, many models relied more on loop structures, reading text step by step, slow, and easy to drop chains over long distances. Transformer uses self-attention to calculate the relationship between all positions in the sentence at the same time, so it is faster and easier to grasp the distant association.
Why is it popular?
| Contrast points | Legacy sequence model | Transformer |
|---|---|---|
| Treatment | Read slowly in order | You can see the big picture in parallel |
| Long-distance relationships | It's easy to forget the previous article | Easier to make remote connections |
| Training efficiency | Usually slower | More suitable for large-scale training |
| Scalability | More restricted | It is easier to build large models |
This is why many people see the Transformer as the base of the era of large models. It is not equal to a large language model, but without it, it would be difficult for today's large model ecology to grow into what it is now. Many of the chat assistants, code models, and graphic models you use today are just extensions of Transformer for different tasks. As long as the model needs to process sequence information, the idea of transformers will continue to exist.
Don't think of it as "universal intelligence"
Transformer is strong, but it's just architecture, not knowledge itself. Whether a model is good or not also depends on the training data, alignment, parameter amount, context design, and inference strategy. In other words, Transformer offers "how to learn, how to calculate", not "what to learn".
If you only remember one sentence, it can be remembered: Transformer allows models to understand context more efficiently and in parallel, which directly promotes the explosion of modern large models.
