Definition
A type of neural network architecture that revolutionised natural language processing. It leverages the "attention mechanism" to weigh the importance of different parts of the input sequence when processing information. This allows Transformers to effectively capture complex relationships and dependencies within data, leading to significant advancements in tasks like machine translation, text summarisation, and question answering.
In modern artificial intelligence, few breakthroughs have had as much impact as the transformer. This type of neural network changed how machines understand language. It replaced older systems that read text step by step and helped power today’s large language models, chatbots, and generative AI tools.
The change is simple. Instead of reading words one at a time in order, a transformer looks at all the words at once and decides which ones matter most.
From tokens to meaning
AI systems do not see text the way humans do. Words are first broken into small pieces called tokens. These tokens are turned into lists of numbers using something called an embedding table. Each token becomes a set of numbers that represents parts of its meaning.
But words only make sense in context. The word sentence could mean a group of words in grammar or a punishment in court. The transformer’s job is to figure out which meaning is correct. It does this using attention.
Self-attention
The most important feature of transformers is the self-attention mechanism. Self-attention lets every token in a sentence look at every other token and decide how important each one is. Instead of moving word by word, the model studies relationships across the whole sentence at the same time.
Take this sentence:
On Friday, the judge issued a sentence.
To understand the word sentence, the model should focus a lot on judge and issued, a bit on the, and not much on the other words. Self-attention calculates these importance levels automatically.
This happens in four main steps:
1. Embedding. Tokens are turned into number vectors.
2. Similarity scoring. The model compares tokens to see how related they are.
3. Attention weights. The scores are adjusted so they add up to 1.
4. Context update. Each token is updated using information from the most relevant other tokens.
In short, tokens learn from one another.
Queries, keys and values
Inside the transformer, each token is turned into three different vectors:
- Query. What this token is looking for
- Key. What this token contains
- Value. The information it can share
The model compares queries with keys to decide how much attention to give each token. It then combines the value vectors based on those attention scores. This gives each token a new meaning that depends on the full sentence. This idea is similar to searching a database, where a query matches keys to retrieve values.
Multi-head attention: many views at once
Transformers do not calculate attention just once, but use multi-head attention. The token vectors are split into smaller parts. Each part goes through its own attention process. Each “head” can learn something different, such as grammar, meaning, or long-distance relationships between words. The results from all heads are then combined. This helps the model understand language from several angles at the same time.
No recurrence, full parallelism
Older language models used recurrent neural networks and LSTMs. These systems read one word at a time in order. That made them slow and not very good at handling long sentences.
Transformers removed this step-by-step process. All tokens are handled in parallel, which works very well with modern computer hardware like GPUs. This is a big reason transformers could grow to very large sizes and be trained on huge datasets.
The 2017 turning point
The modern transformer was introduced in a 2017 research paper called “Attention Is All You Need.” It described an encoder-decoder design built entirely from attention layers and simple neural networks. This model set new records in translation tasks and trained much faster than older systems. After that, attention-based models quickly became the standard in AI.
Encoder, decoder and variants
A typical transformer has two main parts:
- Encoder. Builds deep representations of the input text
- Decoder. Uses those representations to generate output text
Over time, three main types appeared:
- Encoder-only models, like BERT, which are strong at understanding text
- Decoder-only models, like GPT, which are strong at generating text
- Encoder-decoder models, like T5, which are good for tasks like translation
Training transformers
Transformers are usually trained in two stages.
First is pretraining. The model learns from massive amounts of text without human labels. It might predict missing words or guess the next word in a sentence.
Second is fine-tuning. The model is trained on smaller, more specific datasets for tasks like answering questions or classifying text.
Researchers also use special training tricks to keep learning stable, such as slowly increasing the learning rate at the start.
Beyond language
Transformers are not only for text. The same idea works for other types of data, since they excel at learning relationships inside any kind of sequence:
- Images can be split into patches and treated like tokens
- Audio can be processed as sequences of sound features
- Some systems combine text, images, and audio
- Transformers are even used in science and games
How the Transformer changed the world of AI
- Handle long-range relationships between words well
- Process data in parallel, which makes training faster
- Learn effectively from huge amounts of unlabelled data
- Scale up to very large models with broad abilities
- They made systems like GPT and BERT possible and helped start today’s generative AI boom.
More than just a new model, the transformer introduced a new approach. It taught machines to understand data by learning where to pay attention.
Key Takeaways
- Transformers are a 2017 neural network architecture that transformed natural language processing.
- Self-attention lets the model understand relationships between all parts of an input at once.
- They train faster than RNNs and LSTMs because they process data in parallel, not step by step.
- Multi-head attention and positional encoding help capture meaning and word order.
- Transformers power models like GPT and BERT and are now used in language, vision, speech, and science.
- Their ability to scale and handle long-range context drives today’s AI advances.