[Transfinity]

The types of error made by the model in the article are exactly the sort of error that I (senior engineer / decade of experience) would make if asked to describe how something works and I didn't want to spend too much time on the details. The difference is, I can usually give a decent estimate of how sure I am of something, and know how to zero in on the truth when it's important.

Maybe a confidence level for a given explanation, along with some sort of "here's where you can go to learn more" would be useful? No idea if language models would be good at that kind of meta reasoning.

[ShamelessC]

There’s already a per-token confidence score given by GPT-3 actually. As for citing actual sources, retrieval architectures like RETRO are becoming popular for this as they can tell you which sources they used for the current prediction and are actually faster thanks to including a fast KNN lookup over the dataset.

[meken]

Dunning-Kreuger notwithstanding? ;)