Understanding Self-Attention in Transformers: An Intuitive Crash Course!

Introduction

Self-attention is the heart of the Transformer model, powering state-of-the-art NLP applications like ChatGPT, BERT, and GPT models. But how does it work? This blog breaks down self-attention step by step, using a simple sentence: “The cat sat on the mat”, and explains Query (Q), Key (K), and Value (V) vectors in an intuitive way.

What is Self-Attention?

Self-attention allows a model to weigh the importance of different words relative to each other in a sentence. Each word plays multiple roles: it asks questions (queries), provides information about itself (keys), and contributes actual content (values).

At the core of this mechanism, we have three learned weight matrices:

Wq (Query Weight Matrix) — Determines what each word is trying to learn from others.
Wk (Key Weight Matrix) — Determines how each word presents itself when queried.
Wv (Value Weight Matrix) — Determines what content each word contributes to the final representation.

Each word is transformed usingthese matrices to obtain Q, K, and V vectors:

Q (Query Matrix) = Token Embeddings * Wq
K (Key Matrix) = Token Embeddings * Wk
V (Value Matrix) = Token Embeddings * Wv

Sentence Example: “The cat sat on the mat”

To understand Q, K, and V, let’s assign embedding vectors to words:

After multiplying with Wq, Wk, and Wv, each word gets transformed into Q, K, and V matrices.

Wq — Query Matrix (What the word is asking about). Each word now asks different questions about its context:

Example Interpretation:

The cat (a noun) asks, “Am I important in the context?”, scoring 0.7.
The verb “sat” asks, “Should I focus on objects?”, scoring 0.8.
The preposition “on” asks, “Should I look for nouns after me?”, scoring 0.9.

Wk — Key Matrix (How each word presents itself). Each word presents information about itself in response to queries.

Example Interpretation:

“Mat” responds strongly (0.9) to being attended to because it is a noun and the object of the sentence.
“Sat” responds strongly (0.9) to holding an action, as it is the main verb.
“On” responds very strongly (0.95) to linking things, as prepositions exist to link nouns and verbs.

Wv — Value Matrix (What information each word contributes). Each word contributes meaning to the final sentence representation.

Example Interpretation:

“Cat” contributes a strong subject meaning (0.85) because it’s the main noun.
“Sat” contributes a strong action meaning (0.95) because it’s the verb of the sentence.
“On” contributes a strong location meaning (0.9) because it tells us where “sat” happened.

Calculating Attention Weights

The attention mechanism works by computing similarity scores between Queries (Q) and Keys (K) using a dot product:

QK^T computes similarity between query and key.
SoftMax normalizes the scores.
Multiplication with V retrieves meaningful values.

Example: If “sat” queries “mat”, the dot product score might be 0.85, meaning “sat” considers “mat” highly relevant”.

Putting It All Together

Each word is transformed into Q, K, and V.
The attention scores between Q and K determine word importance.
Words contribute their values (V) based on attention scores.
The model learns contextualized representations!

This mechanism enables models like GPT to reason about relationships between words dynamically, handling context like pronoun resolution, subject-object relationships, and word dependencies effortlessly.

Conclusion

Understanding self-attention is crucial for grasping how Transformer models process language. By breaking down Q, K, and V with real-world word features and queries, we can see how words ask questions, present themselves, and contribute meaning dynamically.

Next time you use an AI-powered chatbot, remember: it’s not just memorizing words — it’s reasoning about relationships, just like we do!