Transformers & Attention

In 2017, Google researchers published a paper titled "Attention Is All You Need". It introduced the Transformer architecture, which killed RNNs and gave birth to modern LLMs (like GPT).

1. The Problem with RNNs 🐢

• Sequential: Must process word 1, then word 2, then word 3. Slow! Cannot be parallelized.
• Long-term Memory: Still struggled with very long contexts.

2. The Solution: Self-Attention 💡

Transformers process the entire sentence at once (parallelization). The core mechanism is Self-Attention. It allows every word to look at every other word to figure out context.

• Sentence: "The animal didn't cross the street because it was too tired."
• Attention: When the model processes "it", it pays high attention to "animal" and low attention to "street".

3. Architecture: Encoder-Decoder 🏗️

The original Transformer had two parts:

1. Encoder: Reads the input and understands it (e.g., for translation).
2. Decoder: Generates the output.

• BERT (Encoder-only): Good for understanding (Classification, Search).
• GPT (Decoder-only): Good for generation (Chatbots, Writing).

4. Positional Encoding 📍

Since Transformers process all words at once, they don't know the order of words. We add Positional Encodings (vectors representing position) to the word embeddings so the model knows that "Man bites Dog" is different from "Dog bites Man".

Interactive Challenge: Attention Matrix

Imagine we have 3 words. The Attention Matrix shows how much each word focuses on others.

Quiz

Key Takeaways

✅ Transformers replaced RNNs because they are parallelizable.
✅ Self-Attention allows words to understand their context.
✅ Positional Encodings tell the model the order of words.

What's Next?

We have the building blocks. Now let's scale it up. Way up. Next Chapter: Large Language Models (LLMs) — Deep Dive.