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GPT basically showed that scalable brute-force trumps clever theoretical models which makes many academics salty.

That's something that Microsoft research wrote two decades ago. And those results were well known in the NLP community.

Example: https://www.microsoft.com/en-us/research/wp-content/uploads/... (Michele Banko published a few similar papers on that topic)

There was no hugely scalable approach before transformers, RNNs, the previous SOTA, were notoriously bad at scaling.

Yes, we needed clever ideas from scientists to make them scale. In fact, we still need clever ideas to make them scale because the current architectures still have all sorts of problems with length and efficiency.

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