[matsemann]

The "whole point" of embeddings is that words have a vector that represents how well that word fits into a certain categories, so words belonging together is close in that vector space. So in that sense it almost feels like this should be solvable using something simpler than a full LLM. To "just" get the embeddings of the words, and then find the groups of 4 that minimizes the total distances within the groups.

[basil-rash]

The problem is Connections is designed to use a tons of alternate definitions and other vaguities that aren’t well modeled in typical embeddings. Today’s for instance (spoilers!!) has Coat, Green, Pod, and Soup as being linked for them matching “Pea ___”. No embedding would relate them at all, unless that suffix is known a priori.