Walk me through the trade-offs between FLAN-style aggre...

FLAN-style aggregated data is generally cheap to produce and provides broad coverage, but it often suffers from being unnatural and producing output styles that are overly benchmark-centric. In contrast, model-generated data is typically more naturally phrased and easier to scale. However, a significant risk with model-generated data is hallucination transfer, where the student model inherits the errors or fabrications of a much stronger teacher model. Both approaches generally underperform human-written data in terms of naturalness, though they are superior in terms of volume and scalability.