Imagine you’re reading a long story. Halfway through, you come across the word “she.” To know who “she” is, your brain quickly looks back at earlier sentences: “Oh yes, the girl with the red umbrella!” That ability to look around and find the right connection is exactly what makes transformers so powerful.
Before transformers, language models employed older methods, such as RNNs and LSTMs. These models read sentences one word at a time, like scanning a line of text with a narrow flashlight. They remembered recent words fairly well, but details from far back in the sentence often got lost in the shadows. That’s why they struggled with long or complicated texts.
The transformer changed everything with one key idea: attention. Instead of moving word by word, it allows the model to examine all the words at once and determine which ones are most important to focus on. It’s like replacing that narrow flashlight with stadium lights—suddenly, the model can see the whole field and notice connections between distant words.
Take this sentence:
“The cat that sat by the window looked at the bird.”
When the model processes the word “bird,” it doesn’t just look at the word right before it. With attention, it can connect “bird” to “cat,” to “window,” and even to “sat.” It asks itself: “Which of these words matter most to understand what’s happening?” That’s how it learns context.
And here’s the fun part: transformers don’t just use one “attention lens.” They use many lenses at once (called multi-head attention). Imagine a group of readers, each highlighting different parts of the same sentence. One reader marks grammar connections, another marks important nouns, and another marks descriptive words. When you combine all their notes, you get a richer understanding of the text.
This idea of attention makes transformers very good at handling long pieces of text, translating languages, summarizing, writing stories, and even generating code. It also makes training much faster, because the model can process words in parallel rather than one at a time. That’s why transformers scaled so quickly and became the backbone of today’s large language models like GPT, BERT, and many more.
So why was this such a revolution? Because attention gave machines a way to focus on what matters in language—just like humans do when we read, listen, or write. Without it, LLMs would still be stumbling in the dark, forgetting details from a few sentences ago. With it, they became the powerful, almost human-like tools we see today.
So the transformer is the reason language models stopped reading like forgetful students and started reading like attentive experts.