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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:
by thomashop 2y ago
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 https://machinelearning.apple.com/research/gsm-symbolic
- EraYaN 2y agoIt 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.
- thomashop 2y agoYou 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.
- digging 2y ago> 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.