Last updated: July 31, 2025
The Transformer architecture includes several parts that work together to enable effective sequence modeling. Key mechanisms in a transformer are multi-head attention, layer normalization, residual connections, and feed-forward networks. In this blog, I focus on the main component of the Transformer architecture: multi-head attention.
Let’s unpack the term “multi-head attention” in a simple, non-technical way to understand what it means. Imagine the words themselves and what they suggest: “multi,” “head,” and “attention.” By piecing them together, we can build a picture of this powerful concept.
<aside> 💡
So, multi-head attention is like a superhero with many heads, each focusing on different aspects of something simultaneously.
</aside>
But what does this really mean? Let’s use an example to make it clear.
Imagine you’re looking at a photo and trying to figure out if it’s raining. With just one head (like us regular humans), you might focus on one clue, like someone holding an umbrella. Based on that, you might guess it’s raining, but you’re not totally sure because you only looked at one detail. Now, picture a superhero with multiple heads. Each head can focus on a different clue at the same time: one sees an umbrella, another notices raindrops on the ground, a third spots people wearing raincoats, and a fourth sees dark clouds in the sky. Because the superhero can pay attention to all these clues at once, their guess about whether it’s raining is much more accurate. That’s the power of multi-head attention!
Multi-attention is like a Superhero with many heads
Each attention head picks up different clues about rain: umbrellas, raindrops, and raincoats.
Now, think of a computer model doing the same. Let's look at another example, this time with text. Imagine a model is given the sentence: "The sky is dark, and people are holding umbrellas." The model's job is to answer the question, "Is it raining?" Just like our superhero, the model uses its "multi-head attention" to focus on different parts of the sentence simultaneously. One head might focus on "dark sky," another on "umbrellas," and a third on the connection between these elements. By analyzing all these important pieces together, the model makes a more intelligent assessment about whether it's raining!
<aside> 💡
Multi-head attention is like giving a model with multiple views, allowing it to focus on the most important parts of data - such as a picture or a sentence - from different angles simultaneously, while also enabling parallel processing for efficiency.
</aside>
This makes the computer’s predictions or answers much better than if it only looked at one thing at a time. (Later, we’ll dive into the technical details of how these “heads” are built and how they work in a model!)
To understand multi-head attention, let’s first focus on self-attention, the core of the transformer architecture. Self-attention is like one superhero’s head in our analogy, focusing on a key part of the input, like a word in a sentence. Multi-head attention simply combines multiple self-attention heads.
Multi-head attentions is simply just a bunch of attentions!
Self-attention receives a sequence of vectors (often representing words or subwords) as the input, then processes it, and returns a new sequence of vectors with a new representation.
Example of 3 vectors passed into Self-Attention layer (left). We get 3 resulting vectors with updated representation (right).