>It’s not just something done by nefarious Chinese copycats
replyAnd even that would be rich as a accusation from SOTAs that depend on explicitly disregarding millions of training data intellectual property..
> nefarious Chinese copycats
replyLLMs are themselves copy cats.
I say thanks for open sourcing and thereby promoting affordable innovation, instead of "nefarious". :)
But how? The training data is the unadulterated content those models are based on? I genuinely don’t understand, no snark.
replyRaw training data is raw. A really big model trained on it has already done a first-pass of finding patterns and squeezing out redundancy. Re-ingesting the full training set to train a smaller model is probably more expensive, for marginal quality improvement over distilling from the large model.
replyDistilling from a larger model is not only probably cheaper than from data, it's also likely higher quality. There's pretty strong support for the proposition that NNs learn a smoothed and regularized version of the data. The NNs are likely higher quality than most of the data they are training from.
replyI think you replied to the wrong parent.
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