One surprising discovery about AI: There's nothing inherently wrong with cherrypicking. I used to think it was this dirty thing. Oh, you're only showing your best results?
But the best results are what matters. If you can create an amazing song from scratch by telling a computer what to do, you only need to do it once. The song is still good.
And it's easy to automate. If you have a way of detecting a good song, or at least filtering out rubbish, then you can generate thousands of attempts to get the good result.
I like to say "It's hard to pick cherries from a rotten tree."
The flipside of this post, though, really hits home. I recently was super excited about a paper called FNet, which showed that "fourier transforms can be competitive with transformers", i.e. just replace multihead attention with a fourier transform. 7x faster on a GPU! Woo!
Buried in section 3.1, they casually note that BERT with half the parameter count completely dominates them in terms of accuracy: https://twitter.com/theshawwn/status/1393315603973386240
In other words, the paper was ... advertised in a misleading way, to put it kindly. Most of the transformers they compared with aren't the traditional multihead GPT style transformer that people think of when they hear "transformer". And the one that was (BERT) totally annihilated them.
But it's also easy to jump to conclusions too quickly. I've been a hothead in the past, and called out a paper when it turned out that I just didn't understand enough. It's tricky to know for sure.