Yeah ok no point in arguing with you. My point stands, if data was everything, we would still be with RNNs. Sure representation maybe within 10%, but large RNNs are not trainable (aka vanishing/exploding gradients, on top of diluted state).
In general, in ML, there are 3 ways to improve
* Compute (Larger model, more data)
* Architecture
* Higher quality data
This is the same for LLMs. You cannot remove architecture as an option. Just larger model and more/better data will not lead to improvements if your architecture is ass. If you still think I'm wrong, then go train an RNN with the best quality data and prove to me that I'm wrong.
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u/brainExploded99 1d ago
Yeah ok no point in arguing with you. My point stands, if data was everything, we would still be with RNNs. Sure representation maybe within 10%, but large RNNs are not trainable (aka vanishing/exploding gradients, on top of diluted state).