Why a curated 1000 beats a raw 100000
Collecting vast datasets is easier than ever, but quality often deteriorates with scale. Raw data from the web contains duplicates, mislabeled samples, noise, and distribution shift (data from different sources behaving differently). A model trained on noisy data learns to fit the noise, not the signal, and generalizes poorly to clean test data.
A smaller, carefully curated dataset with consistent labeling, deduplication, and domain expertise often trains stronger models than a raw dump 100 times larger. The reason: models learn patterns, and noisy patterns are weak patterns. Cleaning, deduping, and mixing multiple domains strategically amplifies signal-to-noise ratio.
Practical data curation strategies
Start with the smallest dataset that works and gradually add diverse data sources. For each new source, test quality before merging (check label distribution, sample examples, look for obvious errors). Remove near-duplicates (same text with minor variations). Sample stratified by category to avoid one domain drowning out others.
For critical applications, human review of borderline examples is worth the cost. Annotators can flag ambiguous cases and clarify labeling guidelines. Active learning can focus annotation effort on informative examples (uncertain model predictions, low-confidence examples). A thousand confidently-labeled examples beats ten thousand uncertain ones; the constraint is not usually data scarcity but data quality and diversity.