Explore Transformers and attention
Learn how attention lets token representations use information from surrounding context.
- Describe self-attention and explain why context changes a token representation.
A Transformer is a neural network architecture built around attention mechanisms. In self-attention, each position computes how much information to draw from other positions in the sequence. This helps a model represent relationships across context, while layers transform those representations. Positional information helps distinguish token order. Many modern LLMs use Transformer architectures, though model designs continue to evolve.
A small example
1sentence = ["Maya", "put", "the", "book", "on", "the", "table"]
2focus = "it"
3related_context = ["book", "table"]
4print(f"To interpret {focus}, consider {related_context}")To interpret it, consider ['book', 'table']
Attention scores are learned computations, not human-readable explanations by themselves. Attention can help combine contextual clues, but looking at attention weights alone does not prove why a model produced an answer. Transformer layers, embeddings, positional signals, and training all contribute to the final behavior.
Key takeaways
Describe self-attention and explain why context changes a token representation.
Measure behavior with realistic examples and inspect important failures.
Lesson quiz
5 questions · pass with 4 correct · up to 50 XP
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