Transformer Explained Simply

Core Idea: Words Talking to Words

Imagine reading this sentence: "The cat sat on the mat, and it felt cozy."

When you understand that "it" refers to "the cat," your brain is doing one thing: connecting the current word to every other word in the sentence and deciding which ones are most relevant. This is the core mechanism of the Transformer—Attention.

Three Core Components

1. Self-Attention Mechanism

Each word generates three vectors:

• Q (Query): What am I looking for?
• K (Key): What information can I provide?
• V (Value): What content do I actually carry?

Each word's Q is multiplied (dot product) with every word's K to compute relevance scores. After Softmax normalization, these scores are used to take a weighted sum of all V vectors, producing the word's updated representation.

2. Multi-Head Attention

The model doesn't run attention just once—it runs 8, 16, or even more "heads" simultaneously. Each head captures different linguistic relationships: syntactic structure, coreference, semantic similarity, and so on. The results from all heads are concatenated and fused into a richer representation.

3. Feed-Forward Network (FFN)

After attention completes the "inter-word communication," each word's vector independently passes through a two-layer neural network for non-linear transformation, further enhancing the model's expressive power. This step is applied word-by-word, with no further interaction between words.

These three steps form one complete Transformer layer: Self-Attention → Residual Connection → Layer Norm → FFN → Residual Connection → Layer Norm. Real models stack 12, 32, or even 96 of these layers—the deeper the layers, the more abstract the captured semantics.

Try It Yourself: How Attention Is Distributed

Below, using "The cat sat on the mat, and it felt cozy" as an example, click any word to see which words it "attends to" at different layers—thicker, darker lines mean stronger connections.

Full Pipeline: From Input to Output

① Word Embedding + Positional Encoding

Each word is first converted into a vector (e.g., 512 dimensions), then augmented with positional information (which position in the sentence). Since the Transformer has no inherent sense of order, positional information must be explicitly encoded.

② Stacked Transformer Layers

The input vectors pass through multiple Transformer layers sequentially. At each layer, words thoroughly "communicate" with each other, continuously refining higher-level semantic features.

③ Output

The final layer's output vectors are passed through a linear layer followed by Softmax, producing a probability distribution over the next word—thus completing the prediction.

The Math Behind Attention

The entire mechanism boils down to a single formula:

The division by $\sqrt{d_k}$ prevents dot-product values from growing too large, which would cause vanishing gradients after Softmax and destabilize training. This deceptively simple formula powers the entire core computation of GPT, BERT, Claude, and more.

Why Is the Transformer So Powerful?

It is this "all-words-parallel + direct-connection-at-any-distance" design that makes the Transformer superior to earlier recurrent architectures in long-text understanding, training efficiency, and scalability—and it has become the foundation of virtually all modern large language models.