What makes a good instruction-tuning dataset?

A strong instruction-tuning dataset prioritizes three core axes. First, it must demonstrate diversity in task types to ensure the model generalizes well. Second, it requires correctness of responses relative to the model’s existing knowledge base, rather than just superficial quality. Third, it should include calibration-explicit hedges, where the model is trained to express uncertainty on low-confidence claims instead of fabricating information. Weak approaches often prioritize response length or subjective notions of quality, which do not correlate with robust model performance.