LLMs are like super-smart word detectives who guess what comes next in a story, and they do it really well.
Imagine you're reading a book, and you get to the end of a sentence: “The cat jumped over the ___.” You probably know the next word is fence, or maybe dog, if it's a funny book. That’s what LLMs do, but with millions of words instead of just one.
The Detective Team: Transformers
LLMs use something called the Transformer Architecture, which is like a team of detectives working together.
Each detective looks at the words before the blank and tries to guess the next word. But they don’t work alone, they talk to each other, sharing clues so they can make better guesses. This is called attention.
Think of it like this: You’re telling a story to your friends. Each friend listens carefully, pays attention to what you said before, and then suggests the next word. The more friends you have, the smarter your guess, and that’s how LLMs get so good at predicting the next word.
Examples
- A simple game where you guess what comes next in a sentence based on previous words.
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See also
- How do large language models like GPT-4o actually generate text?
- How do large language models like ChatGPT actually learn?
- What are transformers?
- How do large language models learn to talk like humans?
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