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this post was submitted on 23 Sep 2024
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also, what you described has already been studied. Training an llm its own output completely destroys it, not makes it better.
This is incorrect or perhaps updated. Generating new data, using a different AI method to tag that data, and then training on that data is definitely a thing.
yes it is, and it doesn't work.
edit: too expand, if you're generating data it's an estimation. The network will learn the same biases and make the same mistakes and assumtlptions you did when enerating the data. Also, outliers won't be in the set (because you didn't know about them, so the network never sees any)
Microsoft's Dolphin and phi models have used this successfully, and there's some evidence that all newer models use big LLM's to produce synthetic data (Like when asked, answering it's ChatGPT or Claude, hinting that at least some of the dataset comes from those models).