I was thinking about this after reading the P(Dumb) post.

All normal ML applications have a notion of evalutaion, e.g. the 2x2 table of {false,true}x{positive,negative}, or for clustering algorithms some metric of “goodness of fit”. If you have that you can make an experiment that has quantifiable results, and then you can do actual science.

I don’t even know what the equivalent for LLMs is. I don’t really have time to spare to dig through the papers, but like, how do they do this? What’s their experimental evaluation? I don’t seen an easy way to classify LLM outputs into anything really.

The only way to do science is hypothesis->experiment->analysis. So how the fuck do the LLM people do this?