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Good question - one followup question there is value for who? If it is to train the LLM that is labeling, then I agree. If it is to train a smaller downstream model (e.g. finetune a pretrained BERT model) then the value is as good as coming from any human annotator and only a function of label quality

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Why retrain that smaller model from scratch tho? Just do a little transfer learning, or get creative and see if you can prune down to a smaller model algorithmically instead of doing the whole label and train rigamarole from scratch on what is effectively regurgitation.

I’m not sold this has directional value.

Hmm, I'm not suggesting training a smaller model from scratch - in most cases you'd want to finetune a pretrained model (aka, transfer learning) for your specific usecase/problem domain.

The need for labeled data for any kind of training is a constant though :)

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