Explanation of Self-Attention For Dummies

What is Attention?

First, let’s understand the concept of "attention" in machine learning, particularly in natural language processing (NLP):

Imagine you are reading a book. While reading, you focus on some words more than others to understand the meaning of a sentence. This is similar to how the attention mechanism works. It allows the model to focus on different parts of the input sequence when making predictions.

What is Multi-Head Attention?

Now, let’s break down the multi-head attention module:

Multiple Perspectives:

• Single Attention Head: Think of a single attention head as one person reading a book and focusing on different words to understand a sentence.
• Multiple Attention Heads: Now, imagine you have multiple people (heads) reading the same sentence, but each person has a slightly different perspective or focus. Each person might pick up on different important words or parts of the sentence.

Why Multiple Heads?

Having multiple attention heads allows the model to understand different aspects of the data simultaneously. Each head can focus on different parts of the input and capture various relationships within the data.

How Does Multi-Head Attention Work?

Let’s walk through the process step-by-step:

1. Input Representation: The input (e.g., a sentence) is represented as a series of vectors, one for each word.
2. Linear Transformations: For each head, the model creates three different versions of these vectors:
   • Query (Q): What we are looking for in the input.
   • Key (K): What the input contains.
   • Value (V): The actual content of the input.
3. Calculate Attention Scores: Each head calculates attention scores by comparing the query with all keys. This tells us how much focus to give to each part of the input.
4. Weighted Sum: Using these scores, each head calculates a weighted sum of the values. This results in a new representation of the input where the focus has been adjusted according to the attention scores.
5. Concatenation: All the heads’ outputs are concatenated (joined together) to form a single vector.
6. Final Transformation: This combined vector is then transformed one more time to get the final output.

Putting It All Together

So, in simple terms, multi-head attention is like having multiple people (heads) read the same text (input), each paying attention to different parts in their own way. They then combine their insights to get a better understanding of the text.

Why is Multi-Head Attention Useful?

• Enhanced Understanding: By focusing on different parts of the input simultaneously, the model can capture complex relationships and patterns.
• Rich Representation: It helps in creating a richer representation of the data, which can improve the performance of tasks like translation, summarization, and more.

Visual Representation:

If it helps, here’s a very simplified visual analogy:

1. Input Sentence: "The quick brown fox jumps over the lazy dog."
2. Single Head: Focuses on "quick" and "fox."
3. Another Head: Focuses on "jumps" and "lazy dog."
4. Multi-Head: Combines insights from both heads for a comprehensive understanding.

In essence, multi-head attention allows the model to attend to different parts of the input in parallel, making it a powerful tool for understanding and generating complex sequences.