hackpert

The practical motivation for MoEs is very clear but I do worry about loss of compositional abilities (that I think just emerge from superposed representations?) that some tasks may require, especially with the many experts phenomenon we're seeing. This is an observation from smaller MoE models (with like top-k gating etc.) that may or may not scale, that denser models trained to the same loss tend to perform complex tasks "better".

Intuitively, do you think MoEs are just another stopgap trick we're using while we figure out more compute, better optimizers or could there be enough theoretical motivation to justify their continued use? If there isn't, perhaps we need to at least figure out "expert scaling laws" :)

Comment · 2 years ago

I'm not sure how to quantify how quickly or well humans learn in-context (if you know of any work on this I'd love to read it!)

In general, there is too much fluff and confusion floating around about what these models are and are not capable of (regardless of the training mechanism.) I think more people need to read Song Mei's lovely slides[1] and related work by others. These slides are the best exposition I've found of neat ideas around ICL that researchers have been aware of for a while.

[1] https://www.stat.berkeley.edu/~songmei/Presentation/Algorith...

Comment · 2 years ago

There has been some interesting work when it comes to distributed training. For example DiLoCo (https://arxiv.org/abs/2311.08105). I also know that Bittensor and nousresearch collaborated on some kind of competitive distributed model frankensteining-training thingy that seems to be going well. https://bittensor.org/bittensor-and-nous-research/

Of course it gets harder as models get larger but distributed training doesn't seem totally infeasible. For example if we were to talk about MoE transformer models, perhaps separate slices of the model can be trained in an asynchronous manner and then combined with some retraining. You can have minimal regular communication about say, mean and variance for each layer and a new loss term dependent on these statistics to keep the "expertise" for each contributor distinct.

Comment · 2 years ago

I tried to do this ages ago in 2018 by adapting OpenAI's flow architecture and it sort of seemed to work (was at least promising). With today's models with a significantly more disentangled latent space it should be much easier to do! I saw a transformer trained on the UK Biobank recently, excited for this space!

Comment · 3 years ago

Wow I had hoped for a more productive discussion than these 1-1 comparisons of Bard vs ChatGPT that I'm seeing everywhere. The model deployed with this version of Bard is clearly a smaller model than the biggest LaMDA/PaLM models Google has been working on for ages. Which, according to their publications, show unprecedented results on _proof writing_ of all things (see Minerva). While their strategic decisions may be questionable (or they're just trying to quantize the model for mass deployment without burning billions per month in compute costs), its almost silly to question Google's ability to build useful LLMs.