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You have a subtle slight of hand.

You use the word “plausible” instead of “correct.”

That’s deliberate. “Correct” implies anchoring to a truth function the model doesn’t have. “Plausible” is what it’s actually optimising for, and the disconnect between the two is where most of the surprises (and pitfalls) show up.

As someone else put it well: what an LLM does is confabulate stories. Some of them just happen to be true.

It absolutely has a correctness function.

That’s like saying linear regression produces plausible results. Which is true but derogatory.

Do you have a better word that describes "things that look correct without definitely being so"? I think "plausible" is the perfect word for that. It's not a sleight of hand to use a word that is exactly defined as the intention.

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