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147 points fzliu | 1 comments | | HN request time: 0.202s | source
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Straw ◴[] No.45071026[source]
In the theoretical section, they extrapolate assuming a polynomial from 40 to thousands of dimensions. Why do they trust a polynomial fit to extrapolate two orders of magnitude? Why do we even think it's polynomial instead of exponential in the first place? Most things like this increase exponentially with dimension.

In fact, I think we can do it in d=2k dimensions, if we're willing to have arbitrarily precise query vectors.

Embed our points as (sin(theta), cos(theta), sin(2 x theta), cos(2 x theta)..., sin(k x theta), cos(k x theta)), with theta uniformly spaced around the circle, and we should be able to select any k of them.

Using a few more dimensions we can then ease the precision requirements on the query.

replies(2): >>45071266 #>>45072420 #
1. namibj ◴[] No.45071266[source]
In practice you're actually hitting further problems because you don't have those synthetic top-k tasks but rather open-domain documents and queries to support. And if you hope to get better than "just" having the top-k correct and instead get some sort of inclusion/exclusion boundary between what should be matched and what should not be matched, you'll hit the same bounds as apply to context length limitations for kq dimenionality in a transformer's attention layers, as I mentioned about 6 weeks ago: https://news.ycombinator.com/item?id=44570650