Self-Attention Mechanism

Self-attention is a fundamental component of the Transformer architecture. It empowers a model to understand the intricate relationships between words within a sentence by allowing each word to attend to (or consider) all other words. This process helps the model discern which words are most important when encoding a specific word's meaning.

Real-Life Example of Self-Attention

Consider the sentence: "A dog ate the food because it was hungry."

As humans, we intuitively understand that the pronoun "it" refers to the "dog" due to the contextual cues. The self-attention mechanism aims to replicate this contextual understanding within a machine learning model.

The mechanism works by building a representation for each word by relating it to every other word in the sentence. For instance, when processing the word "it," the model evaluates its connection with all other words: "A," "dog," "ate," "food," and "because." In this scenario, "dog" would likely receive a significantly higher attention score, indicating that "it" most probably refers to the "dog" rather than the "food."

How the Self-Attention Mechanism Works

Let's break down the process with a simpler sentence: "I am good."

Step 1: Word Embeddings

Each word in the sentence is initially transformed into a vector representation through embeddings.

• Let $E_I$ be the embedding vector for "I."
• Let $E_{am}$ be the embedding vector for "am."
• Let $E_{good}$ be the embedding vector for "good."

These individual word embeddings are then stacked together to form an input embedding matrix. If our sentence has 3 words and the embedding dimension is 512, the shape of this input embedding matrix would be [3 x 512].

Step 2: Creating Query, Key, and Value Matrices

From the input embedding matrix, the model generates three new matrices:

• Query (Q)
• Key (K)
• Value (V)

These matrices are derived by multiplying the input embedding matrix with three distinct weight matrices:

• $W_Q$ (Weight matrix for Query)
• $W_K$ (Weight matrix for Key)
• $W_V$ (Weight matrix for Value)

Crucially, these weight matrices ($W_Q$, $W_K$, $W_V$) are trainable parameters. During the model's training process, their values are iteratively updated to optimize the generation of more effective query, key, and value representations.

Step 3: Dimensions of Query, Key, and Value Matrices

For each word in the sentence, the generated vectors serve distinct purposes:

• Query Vector: Represents what the current word is "looking for" or trying to attend to in other words.
• Key Vector: Represents the content or identity of other words that might be relevant to the query.
• Value Vector: Holds the actual information or representation of the word that will be used to compute the attention output.

If the dimensions for the query, key, and value vectors are set to 64, and the sentence contains 3 words, then each of the $Q$, $K$, and $V$ matrices will have dimensions of [3 x 64].

• The Query matrix will have rows representing the $Q$ vectors for "I," "am," and "good."
• The Key matrix will have rows representing the $K$ vectors for "I," "am," and "good."
• The Value matrix will have rows representing the $V$ vectors for "I," "am," and "good."

Why Do We Need Query, Key, and Value?

These three vectors are fundamental for calculating attention scores. The attention score quantifies how much focus a particular word (represented by its query) should place on other words (represented by their keys).

The subsequent step in the self-attention mechanism involves using these attention scores to compute weighted sums of the Value vectors. This process generates more contextually rich word representations.

This mechanism allows the Transformer to effectively model long-range dependencies and complex contextual relationships, surpassing the capabilities of traditional sequential models like Recurrent Neural Networks (RNNs).

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Interview Questions:

• What is the primary purpose of self-attention in the Transformer architecture?
• How does the self-attention mechanism help resolve ambiguity in sentences?
• Please explain the distinct roles of Query, Key, and Value vectors within the self-attention mechanism.
• How are the $Q$, $K$, and $V$ matrices derived from the initial word embeddings?
• Why are trainable weight matrices ($W_Q$, $W_K$, $W_V$) crucial for the functioning of self-attention?
• What does an "attention score" signify in the context of the self-attention mechanism?
• Describe the process by which self-attention models relationships between words in a sentence.
• Given a sentence and a specific vector size, what are the typical dimensions of the $Q$, $K$, and $V$ matrices?
• How does self-attention contribute to the creation of context-aware word representations?
• Compare and contrast how self-attention in Transformers processes context versus how RNNs typically do.