Synthetic Data Generation
Synthetic data generation creates examples using a model or a programmed process rather than collecting each example directly from the target setting. In language-model applications, it can produce demonstrations, test cases or training records, but usefulness depends on validation and coverage rather than the volume generated.
What it is
A generator receives a specification, seed examples, source material or a sampling procedure and produces candidate records. Those records may be labeled by construction, reviewed by people, checked against executable rules, or evaluated with another model. Synthetic data is a property of how data was created, not a training method in itself. The same generated records can support supervised training, preference comparisons or evaluation. Grounding generation in real documents can preserve domain facts, while deliberately varying situations can explore rare cases that ordinary sampling would miss.
What the work involves
Work begins with a target distribution and a reason that existing data is insufficient. The practitioner designs generation instructions, records provenance, filters duplicates and invalid labels, and audits a sample before using the result. Training and evaluation records need separate construction paths to avoid circular success. Useful deliverables include a generation recipe, acceptance rules, a coverage table and a comparison against a model trained without the synthetic addition. Sensitive seed material also needs review before it is propagated into generated examples.
Illustrative example
A team building a document classifier has many ordinary purchase orders but few examples of canceled orders. It generates fictional orders with explicit cancellation language and varied layouts, checks that required fields and labels agree, and asks reviewers to inspect unusual cases. The resulting examples supplement the real training set. A held-out collection of genuine documents then tests whether the classifier learned cancellation evidence or merely the generator's preferred wording and formatting.
Limits and common mistakes
Generated data can amplify the generator's mistakes, hide distribution gaps and create misleading diversity through superficial rewording. A second model agreeing with a label is not independent ground truth. Repeated training on model-produced material can also change the distribution in undesirable ways. Synthetic records should be labeled as such, evaluated on realistic external data and checked for memorized source content. More examples are useful only when they add reliable information relevant to the intended task.
Prerequisites
- mediumLLM Fine-Tuning
Synthetic data is typically generated to be used FOR fine-tuning — understanding the downstream use improves generation quality
Related skills
- → is part of: LLM Fine-Tuning
- → is part of: Model Fine-Tuning
Sources and further reading
- Self-Instruct: Aligning Language Models with Self-Generated Instructions
Describes generating and filtering instruction data for language-model adaptation.
- The Curse of Recursion: Training on Generated Data Makes Models Forget
Examines distributional risks from recursively using model-generated training data.
Last updated: 2026-10-10