So this is an interesting benchmark, because if the answer is actually in the top 3 google results, then my python script that runs a google search, scrapes the top n results and shoves them into a crappy LLM would pass your benchmark too!
Which also implies that (for most tasks), most of the weights in a LLM are unnecessary, since they are spent on memorizing the long tail of Common Crawl... but maybe memorizing infinite trivia is not a bug but actually required for the generalization to work? (Humans don't have far transfer though... do transformers have it?)
I've tried doing this query with search enabled in LLMs before, which is supposed to effectively do that, and even with that they didn't give very good answers. It's a very physical kind of thing, and its easy to conflate with other similar descriptions, so they would frequently just conflate various different things and give some horrible mash-up answer that wasn't about the specific thing I'd asked about.
So it's a difficult question for LLMs to answer even when given perfect context?
Kinda sounds like you're testing two things at the same time then, right? The knowledge of the thing (was it in the training data and was it memorized?) and the understanding of the thing (can they explain it properly even if you give them the answer in context).
Which also implies that (for most tasks), most of the weights in a LLM are unnecessary, since they are spent on memorizing the long tail of Common Crawl... but maybe memorizing infinite trivia is not a bug but actually required for the generalization to work? (Humans don't have far transfer though... do transformers have it?)