Transformer

A transformer is a neural network architecture designed for processing sequences, especially language, by using attention to let each token incorporate information from other tokens in the context.

1. What it is

• Earlier sequence models often processed text one step at a time.
• A transformer instead represents the input as a set of vectors and lets those vectors interact through attention.
• This allows the model to decide which parts of the context matter most for understanding each token.
• In practice, transformers are especially powerful because they can process many parts of the sequence in parallel.

In simple terms:

• a transformer is the main engine behind most modern language models

2. What problem transformer solves

Older sequence models often processed text one step at a time, which made long-range context handling and large-scale parallel training harder.

Transformers solve this by combining:

• token embeddings
• attention
• feed-forward layers
• parallel processing across the sequence

This helps with:

• scaling to large datasets and models
• using context more effectively
• capturing relationships across long passages

In practice, this means transformers make it easier to train powerful models for language and other sequence tasks.

3. Where you see it

Transformers are used in:

• large language models such as GPT
• machine translation
• summarization
• code generation
• image and multimodal models

How they show up in LLM behavior:

• understanding long prompts
• using earlier context when generating later tokens
• scaling to very large parameter counts
• supporting chat, search, coding, and reasoning-style interfaces

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4. How it works internally

Intuition version

• Turn tokens into embedding vectors.
• Let those vectors interact through attention.
• Pass the results through feed-forward layers.
• Repeat this across many stacked layers.
• Use the final representation to predict the next token.

So a transformer is basically:

embeddings → attention → feed-forward → repeat → next-token prediction

Block version

A transformer layer typically includes:

• self-attention
• feed-forward neural network
• residual connections
• normalization steps

Each layer refines the token representations based on both:

• surrounding context
• learned parameters

What happens next

In an autoregressive language model:

1. input text is tokenized
2. tokens become embeddings
3. transformer layers repeatedly refine those embeddings
4. the final position is mapped to probabilities over the next token
5. one token is chosen and appended

Concrete example

Suppose the input sentence is:

“The bank by the river was flooded.”

A transformer does not treat the word bank in isolation.

Using attention inside its layers, it can relate bank to nearby words like:

• river
• flooded

So the internal representation of bank is adjusted toward the meaning of river bank, not financial bank.

In simple terms, a transformer helps each word understand its meaning from the surrounding context.

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5. Background

The transformer architecture was introduced in the 2017 paper “Attention Is All You Need” by researchers at Google.

Its key breakthrough was showing that sequence modeling could rely primarily on attention, instead of depending on recurrence as in older RNN-based models.

This made it easier to train large models efficiently on modern hardware such as GPUs.