BERT can process 512 tokens. LLAMA and FLAN-UL2 can process 2048 tokens. GPT-4 can process 32768 tokens, and is much better at ignoring irrelevant context.

These general models can be fine tuned with domain specific data with a very small number of samples, and have surprisingly good transfer performance (beating classical models). New research like LORA/PEFT are making things like continuous finetuning possible. Statistical models also do a much better job at translating sentences to formal structure than the old ways ever did – so I wouldn't necessarily view those fields are disconnected.

I agree with the general sentiment, there are still major issues with the newer generation of models and things aren't fully cracked yet. But the scaling laws are saying there's still a lot of upside, even without new paradigms or architectural improvements.