3 ms·
getting tricks embedded into the weights is expensive, it doesn't happen in a single pass they's why we teach them new tricks on the fly (in-context learning)
by verdverm 10mo ago
getting tricks embedded into the weights is expensive, it doesn't happen in a single pass
they's why we teach them new tricks on the fly (in-context learning) with instruction files
- catlifeonmars 10mo agoRight, it sounds like an artificial limitation.
- verdverm 10mo agoit's more a mathematical / algorithmic limitation
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- catlifeonmars 10mo agoI’ll counter it’s an architectural issue
- verdverm 10mo agoI would put that under the umbrella of algo/math, i.e. the structure of the LLM is part of the algo, which is itself governed by math For example, DeepSeek has done some interesting things with attention, via changes to the structures / algos, but all this is still optimized by gradient descent, which is why models do not learn facts and such from a single pass. It takes many to refine the weights that go into the math formulas
- catlifeonmars 10mo ago> I would put that under the umbrella of algo/math, i.e. the structure of the LLM is part of the algo, which is itself governed by math Yes you’re right. I misspoke. I’m curious if there are ways to get around the monolithic nature of today’s models. There have to be architectures where a generalized model can coordinate specialized models which are cheaper to train, for example. E.g calling into a tool which is actually another model. Pre-LLM this was called boosting or “ensemble of experts” (I’m sure I’m butchering some nuance there).
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