Large language models can sometimes say things that aren’t quite right because they’re learning from lots of examples, not always knowing what’s true.
Think of a large language model like a kid who reads a lot of books but doesn’t always check if everything in the book is correct. This kid might remember that pigs can fly from one story and believe it even when it's not true in real life.
Like Learning by Heart
When a language model learns, it’s like memorizing many sentences from different stories or articles. It doesn't always understand why those things are written that way, just that they happened to be there. So sometimes, it picks one part of a story and uses it in a new place, even if it doesn’t quite fit.
Sometimes the Best Guess Isn’t Right
A language model is like a puzzle solver who tries to complete a picture with pieces from different puzzles. It might put together parts that look similar but don't always match perfectly, just like how you might guess someone’s favorite color is blue because they wore blue yesterday, even if their favorite is actually green.
It's not about being wrong on purpose, it's more like making the best guess based on what it knows.
Examples
- A child might say 'the sky is green' because they saw a green balloon in the sky.
- The AI could say 2 + 2 = 5 if it was trained on many wrong examples.
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See also
- How do large language models develop human-like conversational abilities?
- How are large language models trained to mimic human conversation?
- How do large language models learn to talk like humans?
- How do large language models like ChatGPT learn to respond?
- How do large language models like ChatGPT actually learn?