This chapter introduces the concept of attention by showing how words can have different meanings based on context.
Example: the word “crane” could mean a bird, a machine, or a neck movement.
Humans use the surrounding words to figure out the intended meaning.
Attention mechanisms in language models do something similar.
They focus on different parts of the sentence depending on what the model is trying to understand or generate.
This is the core idea behind transformers, which power modern LLMs.
Instead of processing words in sequence, attention lets the model consider all parts of the input in parallel and weigh them differently.
The takeaway is that attention is what gives language models their ability to handle complex and ambiguous language effectively.