Self-Attention: The Superpower Behind Transformers (Part 1)

Have you ever been at a party, trying to follow multiple conversations at once? You naturally tune in to the person whose story catches your attention, while still keeping an ear open for someone mentioning your name across the room. This ability to selectively focus is exactly what makes self-attention in transformers so powerful. It helps AI figure out which words to "listen" to in a sentence and how they all connect.

In this article, we’ll break down self-attention step-by-step, using a simple sentence and visuals to make it crystal clear.

Step 1: Breaking Words into Three Roles

In self-attention, every word in a sentence takes on three roles:

1.??? Query (Q): Represents what this word is looking for.

2.??? Key (K): Represents what this word can offer to others.

3.??? Value (V): Represents the actual meaning or contribution of the word.

Example Sentence:

Let’s take the sentence: "How are you?"

Each word—"How", "are", and "you"—is first encoded into its own representation (denoted as eHow, eAre, and eYou). From this encoding, three unique vectors are created for every word:

• QHow, kHow, and vHow for "How".
• QAre, kAre, and vAre for "Are".
• QYou, kYou, and vYou for "You".

The following image represents this process visually:

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Step 2: Comparing Queries and Keys

Once every word has its Query, Key, and Value vectors, the algorithm starts comparing them. Each Query (Q) is compared to the Keys (K) of all words in the sentence to decide how much attention to pay to each word.

Analogy:

Think of this like a team brainstorming session:

• Each team member (word) asks: "Who has the most relevant ideas for me?" (Query).
• Each team member shares what they know (Key).
• Based on the comparison, they decide how much to focus on each person.

For example:

• QHow checks the Keys kHow, kAre, and kYou to see which words are most relevant to "How".
• Similarly, QAre compares with all Keys, and so on.

Step 3: Calculating Attention Scores

The results of these comparisons are attention scores, which indicate how much focus one word should give to another. These scores are normalized using a softmax function so that all attention values add up to 1, making it easier for the algorithm to weigh them.

In the second visual below, this process is depicted clearly:

1.??? Queries are compared with Keys to generate a matrix of raw attention scores.

2.??? The scores are normalized using softmax to distribute the attention.

3.??? The softmax-weighted scores are finally multiplied with Values (V) to combine information.

Step 4: Combining Information with Values

After calculating attention scores, the algorithm uses them to gather information from the Values (V) of the relevant words. The Values represent the actual content each word contributes to the sentence. The output is a new representation of each word that takes into account its context.

Example:

For the word "How":

• It combines information from the Values of "How," "are," and "you" based on their attention scores.
• This gives a context-rich representation of "How," embedding its relationship with the other words in the sentence.

This process ensures that every word in the sentence is understood in the context of the others, allowing the model to grasp nuanced meanings.

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Stay tuned for Part 2, where we dive deeper into the inner workings of this remarkable mechanism!

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