My intuition is two things:
1. Carefully curated pre-training data & finding the right data mix 2. Domain/ task specific evals for downstream use-cases In the end training data is still king! – finding which combination to go for is where the real moneys at. In addition- Post
Training Data Mix and Domain-Specific Evals Drive AI Model Performance
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