3. First evidence that performance continues to improve at hundreds of billions of weights -- paving the way for trillions of weights, approaching orders of magnitude comparable to that of the human brain connectome.
4. First evidence (AFAIK) that larger NLP models do not need task-specific finetuning -- paving the way for general-purpose models that work well on any NLP task without additional training.
5. First evidence (AFAIK) that larger NLP models fool human beings without cherry-picking -- paving the way for models that can pass ever more challenging Turing tests.
GPT-1,2,3 et al. have all shown that larger is better. While in the short term this means people will simply throw larger and larger clusters at the problem, in the longer term there needs to be inovation in making it more efficient on the clusters we have (as even the cloud has limits).
I think sheer parameter count is an important part of the equation in general intelligence, so it's important that there are labs that work on scaling up promising leads to trillions of parameters on top of labs thinking of new promising directions.
This paper implements an architecture that will be out of reach for me for about 5 years. So, I ask myself "why will this paper matter in 5 years?"
There are two reasons I can imagine:
1. It shows that there is no phase change in the size-performance trend already documented over many orders of magnitude.
2. It was used as input data for pruning or distillation algorithms rooted in a better understanding of why language models work.
If the NLP community remains laser focused on hill-climbing compute-agnostic benchmarks, I don't think there will be enough people working on 2.
If 1 is the only reason it matters, I struggle to see how it is worth the cost in high-end research talent. It feels like standard low-risk corporate iteration.