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To be fair, the claim wasn't that it always produced the wrong answer, just that there exists circumstances where it does. A pair of examples where it was correct hardly justifies a "demonstrably false" response.

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Conversely, a pair of examples where it was incorrect hardly justifies the opposite response.

If you want a more scientific answer there is this recent paper: https://machinelearning.apple.com/research/gsm-symbolic

It kind of does though, because it means you can never trust the output to be correct. The error is a much bigger deal than it being correct in a specific case.

You can never trust the outputs of humans to be correct but we find ways of verifying and correcting mistakes. The same extra layer is needed for LLMs.

> It kind of does though, because it means you can never trust the output to be correct.

Maybe some HN commenters will finally learn the value of uncertainty then.

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