Synthetic data can fill gaps where real data is scarce, private or expensive — rare medical cases, driving scenarios or math problems with verified solutions. Many modern language models are trained partly on text generated by other models.
Risks include copying the generator’s errors and reduced diversity if models are trained repeatedly on their own outputs.