Synthetic Data Generation: When and How to Bootstrap Training Sets
Synthetic data can accelerate development and fill gaps in real data, but quality is critical. Learn practical approaches for generation, filtering, and validation.
Generate with constraints
Unconstrained generation produces generic examples. The strongest synthetic data comes from: task-specific templates, constrained sampling, and validation against real distributions.
Always mix synthetic and real data, and measure whether synthetic data actually improves performance on held-out real examples.
Template-based generation
Start with templates that capture task structure: 'Given [context], what is [question]?' Then use a model to fill in diverse, realistic values.
Templates ensure structural correctness while generation provides variety. This balance is hard to achieve with pure generation or pure templates alone.
Constrained sampling for diversity
Use high temperature (1.0-1.5) for diversity, but add constraints: required keywords, length ranges, format validators. This prevents nonsensical outputs while maintaining variation.
Generate multiple candidates per template, then filter for quality. Not every generated example should make it to the final dataset.
Quality filtering pipeline
Apply automatic filters: remove duplicates, check for required fields, validate format, and detect toxic or biased content.
Use a classifier or embedding similarity to detect near-duplicates. Redundant examples waste training capacity without adding information.
Validation against real distributions
Compare synthetic data distributions to real data: token length, vocabulary diversity, entity types, sentiment distribution. Large divergences indicate generation problems.
Sample 100 synthetic examples and have domain experts review them. If experts can easily identify them as synthetic, they may not generalize well.
Mixing synthetic and real data
Start with real data as the foundation. Add synthetic data to fill gaps: rare cases, edge conditions, or to balance class distributions.
Typical mixing ratios: 70% real, 30% synthetic. But test this on your domain—some tasks benefit from higher synthetic ratios, others don't.
Measuring impact on downstream performance
The only metric that matters: does adding synthetic data improve performance on held-out real test data?
Run ablations: train with real only, real + synthetic, synthetic only. Compare performance. If synthetic data doesn't help, don't use it.