Missing the Survey paper: Here’s the results of 5-10 other papers
Depending on the application case and benchmark, being 0.1 to 0.3 % better than other SOTA approaches can still be statistically highly significant. Even though such a number does notmlook like much, it can mean a large leap forward in practise.
Anyway, I wouod add a machine learning paper type that was written by an LLM and nobody cared to call that out in the peer review.
“Our model has no sense of permanence or real understanding of what words even mean and we re-interpreted this as the ability to lie.”
To be fair an argument can be made for the Lego block one, using a novel combination of existing technologies to get better results is how nearly all innovation happens in machine learning.
Proving a thing that’s only known empirically is extremely valuable, too. We’ve an enormous amount of evidence that the Riemann hypothesis is correct - we can produce an infinite amount of points on the line, in fact - but proving it is a different matter.
And for the kid challenging the 0.1% result, that’s about as close to pure scientific method as you can get.
Especially in ML too. It’s currently easier to integrate multiple small specialised models than to train a big model for every use case. If I understand correctly, that was one of the main motivations for Anthropic developing the Model Context Protocol, including interacting with LLMs from front-end clients.
I fucking loathe the term “compute”. Every time one of these mealy-mouthed motherfuckers lets it slide through sphincter-like lips I want to kick some teeth in.
Your rage makes me feel seen. I share your feelings.
“We repeat the experiment with a newer dataset and act like we are the first doing this kind of experiment”
“We talk about possible applications in the future writing like your run-of-the-mill generalist newspaper”
“Another article resuming other articles”
