How do transformers work, and why did they beat older networks?

What a transformer actually is

A transformer is a neural network architecture built for sequences, and it is the design sitting underneath almost every modern language model. It arrived in a single 2017 paper from Google researchers with a deliberately blunt title, "Attention Is All You Need." The authors described it in one sentence, proposing "a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely." Before it, the best translation systems read a sentence one word at a time. The transformer threw that out and looked at the whole sequence at once.

IBM's plain-language definition holds up: a transformer is "a type of neural network architecture that excels at processing sequential data, most prominently associated with large language models." The thing to hold onto is that a transformer does not read. It converts text into numbers, compares those numbers against each other, and produces new numbers. Everything that feels like comprehension is downstream of that arithmetic.

Self-attention, in plain language

The mechanism that does the work is called self-attention. When the model processes a word, it does not consider that word alone. As Google's own write-up of the paper put it, "the Transformer compares it to every other word in the sentence. The result of these comparisons is an attention score for every other word."

Take the sentence "the animal didn't cross the street because it was too tired." To work out what "it" refers to, the model scores "it" against every other word and finds that "animal" matters far more than "street." Hugging Face's course describes the effect: an attention layer tells the model "to pay specific attention to certain words in the sentence you passed it (and more or less ignore the others)." That is all attention is, a learned, weighted comparison that lets each word pull context from the words that matter to it. There is no lookup of meaning, only math over positions.

Why it beat the recurrent network

The models the transformer displaced were recurrent neural networks, or RNNs, which "process language sequentially in a left-to-right or right-to-left fashion," in Google's words. Sequential is the problem. To understand word 50, an RNN had to pass through the 49 before it, one step at a time, and information from early words tended to fade by the end of a long sentence.

Attention looks at all positions at once, so nothing has to survive a long relay, and, just as important, the work can be split across many processors simultaneously. Google noted that "the sequential nature of RNNs also makes it more difficult to fully take advantage of modern fast computing devices such as TPUs and GPUs, which excel at parallel and not sequential processing." That single shift, from sequential to parallel, is most of why the architecture won. The 2017 results made the case: 28.4 BLEU on English-to-German translation and a then-record 41.8 BLEU on English-to-French, after just 3.5 days on eight GPUs, at "a small fraction of the training costs of the best models from the literature."

Tokens, embeddings, attention, output

The full path from your text to the model's answer has four stages. First the text is broken into tokens, the sub-word chunks the model actually handles. Each token becomes an embedding, a long list of numbers that places it in a space where related meanings sit near each other. Because attention sees all positions at once and has no built-in sense of order, the model also adds a positional signal so it knows which token came first.

Then come the attention layers, stacked many deep, each one letting every token gather context from the others and refine its own representation. At the top, the model produces a probability for every possible next token and picks one. That is the whole trick behind a chatbot, a transformer predicting one token at a time, with each prediction folded back into the input for the next. How much text it can attend to at once is set by its context window.

Why it scales

The reason transformers, and not RNNs, became the base of GPT, Claude, and the rest is that they keep improving as you make them bigger. Because attention is parallel, you can pour in more data, more parameters, and more chips, and the training still finishes in a reasonable time. That property is what made it economical to train models with hundreds of billions of parameters. The catch is baked into the same mechanism: comparing every token to every other token means cost grows with the square of the sequence length, which is why very long inputs get expensive and why a great deal of research now goes into making attention cheaper.

Our take

The transformer is one of the rare cases where the hype and the substance line up, as long as you are precise about what it does. It did not teach machines to understand language. It found a way to compute the relationships between all parts of a sequence at once, cheaply enough to scale, and that turned out to be enough to power a decade of progress. The word "attention" invites the wrong intuition, as if the model were consciously focusing. It is weighted arithmetic over token positions, learned from data. Keep that in view and the transformer stops being magic and becomes what it is, a very good, very parallel way of mixing context, which is most of what modern AI is built on.