If you've heard of ChatGPT, Gemini, or Llama, you know they're powered by Large Language Models (LLMs). But what makes these models so powerful? The secret lies in their core structure—the Transformer architecture.
In this post, we’ll break down the Transformer architecture in simple terms so you can understand how it works.
What is a Transformer?
Introduced in the 2017 paper "Attention Is All You Need" by Vaswani et al., the Transformer is a deep learning model designed to handle sequential data (like text) more efficiently than older models (RNNs, LSTMs).
Unlike older models that process words one by one, Transformers can look at all words in a sentence at once using a mechanism called self-attention. This makes them faster and better at understanding context.
Key Components of a Transformer
A Transformer has two main parts:
- Encoder (processes input text)
- Decoder (generates output text)
For LLMs like GPT, only the decoder is used (since they focus on generating text). Models like BERT use only the encoder (since they focus on understanding text).
Let’s break down the key components:
1. Tokenization & Embeddings
- Words are split into smaller pieces called tokens (e.g., "learning" → "learn" + "ing").
- Each token is converted into a vector (list of numbers) using word embeddings (e.g., Word2Vec, GloVe).
- Since word order matters, positional encodings are added to give the model information about word positions.
2. Self-Attention Mechanism
This is the most important part of a Transformer.
- Self-attention allows the model to weigh the importance of each word relative to others.
- Example:
- In the sentence "The cat sat on the mat because it was tired."
- The word "it" refers to "cat". Self-attention helps the model link these words.
- The model computes attention scores to decide how much focus each word should get.
3. Multi-Head Attention
Instead of one attention mechanism, Transformers use multiple attention heads in parallel.
- Each head looks at different relationships between words (e.g., one head may focus on subject-verb, another on pronouns).
- This makes the model more powerful in understanding context.
4. Feed-Forward Neural Networks (FFNN)
After attention, each token passes through a simple neural network to process the information further.
5. Layer Normalization & Residual Connections
- Residual connections help avoid the "vanishing gradient" problem (allowing deep networks to train better).
- Layer normalization stabilizes training by normalizing values between layers.
6. Decoder (for Text Generation)
- The decoder works similarly but has an extra masked self-attention layer to prevent it from "cheating" by looking at future words.
- It generates text one word at a time, using previous outputs as inputs (autoregressive generation).
Why Transformers Are Better Than RNNs/LSTMs?
| Feature | RNNs/LSTMs | Transformers |
|---|---|---|
| Parallel Processing | ❌ (Sequential) | ✅ (All words at once) |
| Long-range Dependencies | ❌ (Struggles with long sentences) | ✅ (Self-attention captures distant relationships) |
| Training Speed | ❌ (Slow due to sequential steps) | ✅ (Faster due to parallelization) |
How Transformers Power LLMs?
Models like GPT-4, Llama 2, Gemini are decoder-only Transformers trained on massive text data. They:
- Take a prompt (input text).
- Process it through multiple Transformer layers.
- Predict the next word repeatedly to generate responses.
Summary
- Transformers use self-attention to understand relationships between words.
- They process all words at once (unlike RNNs).
- Multi-head attention helps capture different word relationships.
- Positional embeddings help track word order.
- Decoder-only models (like GPT) generate text autoregressively.
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