I hope this dataset is used to probe the wrong answers much more than try to get all the answers correct. I don't need LLMs to know everything, but I do need them to know what they don't know. That's not a capability that comes naturally to a probabilistic sampling of tokens.

The ideal 'progress' on this benchmark is for a model to remove incorrect answers and replace them with I don't know answers. Even if it hurts the correct answer count a bit I'd gladly make that tradeoff for a model that hallucinated far less often.