Craftsman Leadership

Introduction

Transformers revolutionized AI when they were introduced in 2017. They’re the architecture behind ChatGPT, BERT, and most modern language models. In this guide, you’ll learn how transformers work, why they’re so powerful, and what makes them different from previous approaches.

Prerequisites

Understanding of basic neural networks and how they learn is recommended. Familiarity with the concept of sequential data (like sentences or time series) will be helpful.

The Problem Transformers Solved

Before transformers, processing sequential data (like sentences) was challenging:

Recurrent Neural Networks (RNNs) processed text word by word, like reading a book one word at a time. This was slow and they “forgot” information from earlier in long sequences.

The Limitation: Imagine trying to understand the end of a long article but forgetting what the beginning was about. That was the RNN problem.

Transformers solved this by processing all words simultaneously while still understanding the relationships between them.

The Key Innovation: Attention

The breakthrough was the attention mechanism—the transformer’s ability to focus on relevant parts of the input when processing each word.

An Analogy: Reading Comprehension

When you read the sentence “The animal didn’t cross the street because it was too tired,” your brain knows “it” refers to “animal,” not “street.” You pay attention to the context.

Transformers do the same thing mathematically—they learn which words to pay attention to when understanding each word.

How Attention Works

For each word, the transformer:

Looks at all other words in the sentence
Calculates how relevant each word is to understanding the current word
Creates a weighted combination where important words contribute more
Uses this context-aware representation for further processing

This happens simultaneously for all words, making it much faster than sequential processing.

The Transformer Architecture

The Two Main Components

Encoder: Reads and understands the input Decoder: Generates the output based on the encoder’s understanding

Some models use both (like translation models), while others use only encoders (BERT) or only decoders (GPT).

Multi-Head Attention

Instead of one attention mechanism, transformers use multiple “attention heads” running in parallel.

Think of it like reading a complex document from multiple perspectives simultaneously:

One head focuses on grammatical relationships
Another focuses on semantic meaning
Another tracks logical connections
And so on

This multi-perspective approach captures richer information about the text.

Positional Encoding

Since transformers process all words simultaneously, they need a way to know word order. “Dog bites man” means something different from “Man bites dog.”

Positional encoding adds information about each word’s position in the sequence. It’s like numbering sentences in an outline so you know the order even if pages get shuffled.

Feed-Forward Networks

After attention, each word’s representation goes through a simple neural network independently. This processes the context gathered by attention into a more refined representation.

Why Transformers Are So Powerful

1. Parallelization

Unlike RNNs that must process sequentially, transformers process all positions simultaneously. This makes training much faster on modern GPUs.

Analogy: It’s the difference between washing dishes one at a time versus having a dishwasher do them all at once.

2. Long-Range Dependencies

Attention can connect words far apart in a text. In “The cat, which was sitting on the mat, was fluffy,” transformers easily connect “cat” and “fluffy” despite the separation.

3. Flexible Context

The attention mechanism learns what context is important rather than being told. This makes transformers adaptable to many different tasks.

4. Scalability

Transformers get better with more data and more parameters. Bigger transformers (like GPT-4) can capture increasingly subtle patterns in language.

Training Transformers

Pre-training

Large transformers are typically pre-trained on massive text datasets with a simple task:

For GPT (decoder-only): Predict the next word For BERT (encoder-only): Fill in blanks and understand sentence relationships

This teaches the model general language understanding.

Fine-tuning

After pre-training, the model can be fine-tuned on specific tasks:

Question answering
Sentiment analysis
Translation
Summarization

The pre-trained knowledge transfers to these tasks, requiring much less data.

Transformer Variants

GPT (Generative Pre-trained Transformer)

Decoder-only architecture
Trained to predict next words
Good at text generation
Used by ChatGPT and similar models

BERT (Bidirectional Encoder Representations from Transformers)

Encoder-only architecture
Trained by filling in masked words
Excellent at understanding text
Used for search engines and classification

T5 (Text-to-Text Transfer Transformer)

Full encoder-decoder architecture
Treats every task as text-to-text transformation
Very versatile across different tasks

Real-World Applications

Language Translation: Transformers can translate between languages by encoding the source language’s meaning and decoding it into the target language, understanding context that earlier systems missed.

Code Generation: Models like GitHub Copilot use transformers trained on code to suggest completions and generate entire functions.

Protein Folding: AlphaFold uses transformer-like attention to predict how proteins fold, a breakthrough in biology.

Image Generation: DALL-E and similar models combine transformers with other techniques to generate images from text descriptions.

Limitations and Considerations

Computational Cost

Training large transformers requires enormous computing resources. GPT-3 cost millions of dollars to train and has a significant environmental footprint.

Context Length Limits

Attention becomes computationally expensive with long sequences. Most transformers have a maximum context length (e.g., 4,096 or 8,192 tokens for many models).

Data Requirements

Transformers need massive amounts of training data to reach their full potential. This can be a barrier for specialized domains with limited data.

Understanding vs. Memorization

While transformers are powerful, they can sometimes memorize training data rather than truly understanding patterns, leading to issues with generalization.

The Future of Transformers

Recent developments include:

Sparse attention: Making attention more efficient for longer sequences
Mixture of Experts: Activating only parts of the network for each input
Multi-modal transformers: Combining text, images, and other data types
Efficient architectures: Making transformers smaller and faster while maintaining performance

Try It Yourself

Visualize Attention: Search for “BERT attention visualizer” to see interactive demos showing which words the model pays attention to for each word.
Experiment with Prompting: When using ChatGPT, notice how it uses earlier parts of the conversation in later responses. That’s the transformer’s attention mechanism at work.
Compare Architectures: Try the same task with an older model (like a standard RNN) and a transformer-based model. Notice the difference in quality and speed.
Token Length Exercise: Try giving ChatGPT increasingly long prompts. Notice how it handles context from throughout your entire message in its response.

Key Takeaways

Transformers use attention mechanisms to process sequences in parallel rather than sequentially
Multi-head attention looks at the input from multiple perspectives simultaneously
Positional encoding maintains information about word order in the sequence
Transformers excel at capturing long-range dependencies and complex patterns
They can be pre-trained on large datasets and fine-tuned for specific tasks
Different variants (GPT, BERT, T5) are optimized for different types of tasks
Their main limitations are computational cost, context length, and data requirements
Transformers are the foundation of most state-of-the-art NLP models today