In our last lesson, we studied tokens and embeddings — the small pieces of language that LLMs use to think. This lesson, we’re going to uncover the architecture that makes LLMs truly powerful: transformers. You’ll learn how self-attention works, why multiple attention heads matter, and how positional encodings help models understand the order of words.
Transformer Basics: A transformer is a neural network architecture introduced in 2017. It's especially good for language because it can look at entire sentences at once.
Self-Attention: The key idea is self-attention. Each word (token) in the input text can "pay attention" to other words to gather context.
Why it helps: This means the transformer can learn relationships between any two words in a sentence, even if they are far apart. Unlike older models, it doesn't have to read words strictly left-to-right.
How Attention Works: When processing text, the model computes weights for each pair of tokens. A higher weight means "these words matter together," while a lower weight means "this word is less relevant."
Focus on Context: Using these weights, the model focuses on relevant words and ignores less important ones. For example, in "The tree bark was rough," the model learns whether "bark" means the sound or the tree part based on context.
Flexible understanding: Self-attention lets the LLM understand each word in the context of the whole sentence, not just its immediate neighbors. This gives it a very flexible understanding of language.
Multi-Head Attention: Transformers often use several attention "heads" simultaneously. Each head might learn a different type of relationship (e.g., one might focus on subject-verb relationships, another on synonyms).
Why multiple heads: This allows the model to capture different patterns in parallel, making it more powerful without changing the basic structure.
Positions Matter: Since self-attention alone doesn't know word order, transformers add positional encodings. This is like giving each word a tag that says "I'm the first word," "I'm the second word," etc.
Role of Position: With positional information, the model can tell the difference between "the cat sat" and "sat the cat," understanding sentence structure.
Example: After adding positions, the model combines position + token information so it respects the order of words.
Parallel Processing: Unlike older sequential models, transformers process all tokens at once. This parallelism speeds up training and lets the model learn patterns across long texts more easily.
Analogy: Think of it as reading a whole page of text at once instead of word by word; the transformer can spot patterns across the entire page simultaneously.
Efficiency: Because of this, transformers can handle very large datasets and complex tasks, which was a key breakthrough enabling modern LLMs.
What Feature Of Transformer Models Helps Them Understand Context Across An Entire Sentence?
Transformers can process tokens ___ at once, unlike older models that read one word at a time.
Imagine you read a long sentence with many clauses. How does knowing the context of all the words help you understand it?
For example, how does the meaning of "bank" change if the earlier words were "river" versus "money"? Consider how a model with self-attention might figure this out by weighting different words.
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