One of the weird things you do in transformers is add a position vector which captures the distance between the token being attended to the some other token.
This is obviously not powerful enough to express non-linear relationships - like graph relationships.
This person seems to be experimenting with doing pre-processing of the input token set, to linearly reorder it by some other heuristic that might map more closely to the actual underlying relationship between each token.
they replace dot-product attention with topology-based scalar distances derived from a laplacian embedding - that effectively reduces attention scoring to a 1D energy comparison which can save memory and compute
that said, i’d treat the results with a grain of salt give there is no peer review, and benchmarks are only on 30M parameter model so far
This may work well for their use case but fail horribly in others without further peer review and testing.
Experiments I want to build on top of it:
1. Adding lsp context to the embeddings - that way the model could _see_ the syntax better, closer to how we use IDEs and would not need to read/grep 25k of lines just to find where something is used. 2. Experiments with different "compression" ratios. Each embedding could encode a different amount of bytes and we would not rely on a huge static token dictionary.
I'm aware that papers exist that explore these ideas, but so far no popular/good open source models employ this. Unless someone can prove me wrong.
> Standard AI models (like GPT-4) treat data using Global Geometry. They imagine every word as a point floating in a massive, flat, high-dimensional room. To see how two words relate, they draw a straight line between them.
> Local Topology changes the "room" into a landscape (a manifold). Instead of a flat void, the data exists on a curved surface that has hills, valleys, and paths.
The presented counterpoint to this metaphor has the "room" change into a "landscape". The room is a "flat void" compared to a landscape with "hills, valleys, and paths". None of these landscape features evoke higher dimensionality in my imagination. Certainly not in the way, say, the metaphor of the "coastline" of Great Britain does when discussing the unusual properties of a fractal.
These moves don't shift my railroad mind from one track onto another. So I wonder, if a metaphoric usage is not in some way universal, how can it be instructive?
lostmsu•1h ago
Lerc•1h ago
Provided the flops are not prohibitive. Output quality per model bytes might be better. In general people run the largest model they can.
I certainly think trading speed for quality at the same size is worth looking at. Especially if it uses methods that can benefit from the efforts of others to improve speed in general.
That said performance difference at 30M may not be representative of performance difference at 30B
There are probably a lot of really good ideas out there waiting for someone to drop a few million in training to reveal how good they are on large sizes.
lostmsu•1h ago