I’m not sure when you developed this work, but the LLM.int8 paper identifies outliers as an essential factor in achieving performance for models larger than 2.7B parameters (see Fig. 1 and Fig. 3 especially). There’s also some follow-up work here and here. Very curiously, the GLM-130B paper reports that they don’t see outlier features at all, or the negative effects of their lack of impact.

I’ve spoken with Tim (LLM.int8 lead author) about this a bit and some people in EleutherAI, and I’m wondering if there’s some kind of explicit or implicit regularizing effect in the GLM model that prevents it from learning outlier features. If this is the case, one might expect to find different patterns in outliers in models with sufficiently different architecture, perhaps GPT-2 vs Pythia vs GLM vs T5

The distinction between "large scale era" and the rest of DL looks rather suspicious to me. You don't give a meaningful defense of which points you label "large scale era" in your plot and largely it looks like you took a handful of the most expensive models each year to give a different label to.

On what basis can you conclude that Turing NLG, GPT-J, GShard, and Switch Transformers aren't part of the "large scale era"? The fact that they weren't literally the largest models trained that year?

There's also a lot of research that didn't make your analysis, including work explicitly geared towards smaller models. What exclusion criteria did you use? I feel like if I was to perform the same analysis with a slightly different sample of papers I could come to wildly divergent conclusions.

If superintelligence is approximately multimodal GPT-17 plus reinforcement learning, then understanding how GPT-3-scale algorithms function is exceptionally important to understanding super-intelligence.

Also, if superintelligence doesn’t happen then prosaic alignment is the only kind of alignment.

Due to the redundancy, changing any single weight—that is associated with one of those two pieces of logic—does not change the output.

You seem to be under the impression that the goal is to make the NN robust to single-weight perturbation. But gradient descent doesn’t modify a neural network one weight at a time, and so being robust to single-weight modification doesn’t come with any real guarantees. The backward pass could result in weights of both forks being updated.

I don’t understand what the purported ontological crisis is. If ghosts exist, then I want them to be happy. That doesn’t require a dogmatic belief that there are ghosts at all. In fact, it can even be true when I believe ghosts don’t exist!