- A transformer model in plain words
- The paper that introduced the transformer model
- How attention works inside a transformer model
- Why transformer models replaced older approaches
- Kinds of transformer model you will hear about
- A transformer model at work on a customer email
- What a transformer model can't do well
- What the transformer model means for your business
- Frequently asked questions
A transformer model is a type of neural network that reads a whole passage at once and uses attention to weigh how each part relates to the others. It was introduced in a 2017 research paper and now powers most chat assistants. The part people trip over: it predicts likely text, so it can sound sure and still be wrong.
The T in GPT stands for transformer, and so does the idea behind most of the chat assistants, translators and image tools in the news. I think it's worth ten minutes to understand it, because it explains why today's AI reads whole paragraphs well and why it has the limits it does.
A transformer model in plain words
Wikipedia describes the transformer as a family of neural network architectures based on the multi-head attention mechanism. Strip away the jargon and it's a way of building an AI so that, when it reads text, it can weigh how every part of a passage relates to every other part.
Take the sentence "The shop sent the parcel to the customer because she had ordered it." To understand who "she" is and what "it" refers to, you have to connect words that sit far apart. A transformer is built to make exactly those connections.
The paper that introduced the transformer model
The idea comes from a 2017 research paper called "Attention Is All You Need". Its abstract says the Transformer is based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Earlier top systems were built on complicated recurrent or convolutional networks.
The authors reported results on translation. They scored 28.4 BLEU on the WMT 2014 English-to-German task and 41.8 BLEU on English-to-French, and the French model trained in 3.5 days on eight GPUs. BLEU is a standard measure of translation quality, so higher is better. You don't need to remember the numbers. The point is that the new design beat the older ones on quality while training more quickly.
How attention works inside a transformer model
AWS describes self-attention as letting the model look at different parts of the sequence all at once and weigh which parts matter most. Here's the flow in everyday terms:
- The text is cut into small pieces called tokens, and each token is turned into a list of numbers (an embedding).
- A position signal is added so the model knows the order, since attention on its own doesn't.
- For every token, the model scores how relevant every other token is, then blends in the most useful ones.
- That process repeats through many stacked layers, building up a richer understanding each time.
- At the end, the model predicts what should come next, or produces the translation or answer.
Wikipedia explains the effect: key tokens get amplified and less important ones are diminished. It's a bit like highlighting the relevant sentences in a long email before you reply.
Why transformer models replaced older approaches
Older recurrent networks read one token at a time, in order, which is slow to train and tends to lose track of earlier words. Wikipedia points out that transformers don't process tokens one at a time, so training can be parallelised across positions. That made it practical to train on far more text, which is a big reason for today's large language models.
AWS adds that pretrained transformers can be customised through transfer learning and retrieval augmented generation instead of training from scratch, which is how many business tools are built.
Kinds of transformer model you will hear about
AWS names a few well-known types:
- BERT. Predicts masked words using context from both directions, handy for search and classification.
- GPT. Predicts the next token from everything before it, which suits writing and chat.
- BART. Combines a BERT-style reader with a GPT-style writer.
- ViT. Treats an image as a sequence of fixed-size patches, bringing the idea to pictures.
Wikipedia notes transformers are now used for text, computer vision, audio, robotics and image or video generators. If you want to see how chat assistants built on them draw on information, my piece on where ChatGPT gets its information is a useful companion.
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A transformer model at work on a customer email
Say a customer writes three rambling paragraphs about a late delivery, a wrong colour and a question about returns. A transformer-based tool reads the whole message together, so it can link "the blue one" in the second paragraph to the order mentioned in the first. It can then summarise the three issues, suggest a reply or sort the email to the right person. An older, one-word-at-a-time system tended to lose that thread.
What a transformer model can't do well
Wikipedia flags one built-in limit: the computing and memory demands of standard self-attention grow quadratically with sequence length. In plain terms, doubling the length of the text a model considers makes the work grow much faster than double. That's why there are limits on how much text a model can read in one go, and why research keeps looking for lighter variants.
Other cautions are practical rather than technical:
- A transformer predicts likely text. It can sound confident and still be wrong, so check facts that matter.
- It reflects the data it was trained on, including gaps and bias.
- Anything you paste into a tool may be handled according to that vendor's data policy, so read it first.
What the transformer model means for your business
You'll almost never build a transformer yourself. What matters is knowing that the tools you use rest on this design: it's why they cope with long, messy emails, why they can translate and summarise, and why they sometimes lose the thread in very long documents. For a shortlist of tools built on it, see my roundup of the best AI tools for small business owners, and if you want help turning any of it into a working process, talk to an AI consultant.
Frequently asked questions
What is a transformer model in simple terms?
It's a type of AI design that reads a whole passage at once and works out which parts relate to each other, using a mechanism called attention.
Who invented the transformer model?
It was introduced in the 2017 research paper "Attention Is All You Need", which has eight listed authors, led by Ashish Vaswani.
Is ChatGPT a transformer model?
Yes. The T in GPT stands for transformer. GPT-style models predict the next token from all the tokens before it.
What is the difference between a transformer and a neural network?
A transformer is one kind of neural network. Other kinds include convolutional networks for images and recurrent networks for ordered data.
What are the limits of transformer models?
Wikipedia notes that standard self-attention gets much more demanding as the text gets longer. They can also state wrong answers confidently, so facts need checking.
