Yeah I'm pretty sure you could do this just with the classic word embeddings (king =queen + man - woman). Maybe it doesn't work as well as with a full LLM.
Associativity and commutivity in vector addition doesn't translate well to semantic meaning. Extrapolating your example, it'd also mean:
v_king - v_queen ~= v_father - v_mother
v_king - v_royalty ~= v_father - v_parent
v_king - v_father ~= v_royalty - v_parent ~= v_queen - v_mother
I don't see why those should all be true. Intuitively, trying to satisfy O(N^2) semantic pairings with vectors that are optimised for a very specific and different numerical operation (cosine similarity) feels like something that won't work. I'd imagine errors get amplified with 3+ operands.But I'd be surprised if either of those were the primary reason. The words "sea" and "ocean" are different vectors but they'll be very close to each other. salt + water = sea and salt + water = ocean both sound correct to me so the problem is more about whether the v_salt + v_water can even get to the vicinity of either v_sea or v_ocean.