How Transformers Work: From Text to ChatGPT

Have you ever wondered how ChatGPT understands your questions and generates human-like responses? It all starts with text, but the model processes it through several mathematical steps.

Let's understand the process briefly.

1. Tokenization

The first step is breaking text into smaller units called tokens. A token can be a word, part of a word, or a punctuation mark.

For example:

"I love JavaScript"

The tokenizer converts this text into tokens, which are then mapped to numerical token IDs.

2. Embeddings

Computers work with numbers, not words. Each token ID is mapped to a vector of numbers called an embedding.

These vectors help represent information about tokens in a mathematical space, allowing the model to learn relationships between words and concepts.

3. Positional Information

Transformers need information about the order of tokens. Positional information helps the model distinguish between sequences such as "Dog bites man" and "Man bites dog."

4. Self-Attention

This is one of the key mechanisms in a Transformer.

Self-attention helps the model determine which other tokens are relevant when processing a particular token.

For example, in the sentence "The animal didn't cross the road because it was tired," attention helps the model relate "it" to relevant context.

5. Feed-Forward Neural Networks

After attention, neural network layers further transform the token representations, helping the model learn complex patterns in language.

Transformers repeat attention and neural network operations across multiple layers.

6. Predicting the Next Token

After processing the context, the model produces scores for possible next tokens. These scores are converted into probabilities, and a token is selected according to the model's decoding strategy.

For example:

"The sun rises in the..."

The model might assign a high probability to "east".

The selected token is added to the context, and the process repeats to generate the response.

7. Training and Fine-Tuning

During training, the model learns patterns from large amounts of data by adjusting its parameters to improve next-token predictions.

Additional training and alignment techniques can help make a conversational model more useful, instruction-following, and safe.

The Complete Pipeline

Text → Tokenization → Token IDs → Embeddings + Positional Information → Transformer Layers (Attention + Feed-Forward Networks) → Next-Token Probabilities → Generated Response

That's the basic idea behind how Transformer-based language models generate text. ChatGPT uses this general foundation, combined with additional training and systems that help it respond to users.