Are LLMs Any Good at Ranking People?

When the AI hype was reaching it’s peak I felt quite often challenged to justify my reasoning for thinking it was cool. Suddenly something I had been following for a few years and would annoy people about over drinks was now me buying into the hype. This was frustrating (yes, I liked LLMs before it was cool). However, needing to justify it so many times meant coming up with a succinct answer.

LLMs have some understanding (albeit flawed) of the fuzziest concepts we know. The human world. Something computers have been famously bad at until literally just the past few years.

Whenever people come up with a terrible app idea, a very common thing standing in the way was you’d need the computer to somehow make sense of a very fuzzy human concept, something people have a hard time recognizing1. There’s a lot to be said for a wide range of machine learning methods that have made these fuzzy concepts more tangible, but I do think LLMs take the cake. I don’t think it’s a coincidence that multi modal models (MMMs?) commonly use LLMs at their core and then attach vision or speech recognition or generation components.

It will take years to fully work out how useful this is, where LLMs will plateau in their reasoning and capability, how much we can optimize them and so on. Will they go the way of the plane, big, expensive, built by one of two companies and where you rent a seat and get where you want to go. Or will they be more like cars, small, personalizable, affordable, ubiquitous. Perhaps both, who knows. Anyways, if all those past app ideas were held back by an understanding of the fuzzy and abstract, I’d better back that idea up and build something I thought could only be done with the fuzzy and the abstract.

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