What is Synthetic Data?
Also called model-generated data.
Synthetic data is training data generated by a model or a program rather than collected from human-produced sources. It is used to cover cases real data lacks, to scale instruction and preference datasets, and to avoid privacy or licensing constraints on real records. Its value depends heavily on generation quality and on filtering the results.
Common uses include generating instruction and response pairs to teach task formats, producing variations of scarce examples to balance a dataset, creating adversarial or edge cases that rarely occur naturally, and replacing records containing personal information with statistically similar substitutes. Each use targets a gap that collecting more real data would address slowly or not at all.
Quality control is the deciding factor. Generated data inherits the generating model's errors and stylistic tics, and unfiltered generation tends to be narrower and more repetitive than it appears. Effective pipelines pair generation with verification, using executable checks, rule-based validation, model-based filtering, or human spot-checks to discard weak samples before training.
Verifiable domains benefit most. Mathematics, code, and structured transformations allow a generated example to be checked automatically, so a generator can produce far more candidates than survive filtering and the surviving set is reliably correct. Open-ended domains such as opinion, style, or nuanced judgment lack that check and are correspondingly riskier.
Concern about training on generated content is well founded but often overstated. Studies of repeatedly training models on their own unfiltered output show progressive degradation, sometimes called model collapse. Practical pipelines avoid this by mixing synthetic with real data and by filtering aggressively, and current evidence suggests curated synthetic data is beneficial rather than harmful under those conditions.
Key points
- Data generated by models or programs, not collected
- Covers rare cases, scales instruction sets, sidesteps privacy limits
- Only as good as the verification and filtering applied
- Works best where correctness can be checked automatically
- Unfiltered self-training degrades quality over generations
In practice
A team needs a model that handles unusual date formats in shipping records, but their real corpus contains only three such cases. They programmatically generate forty thousand records spanning every format variant they can enumerate, validate each against a parser so no malformed example enters the set, and mix them into real data at a modest proportion. Accuracy on the rare formats rises without measurable regression on the common ones.