“filtering out all of the ambiguity. So sometimes the LLM…” — Practical AI at 51:48
“filtering out all of the ambiguity. So sometimes the LLM wouldn't align with the original chosen rejected pair. And based on that, we were actually able to scale down the data set at 50%, leading to less training time and also leading to a higher performing model. And that was one of the really famous examples that…”
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