A new guide by Eugene Yan examines synthetic data as a way to overcome the bottleneck of human annotations in finetuning. Instead of relying on hand-labeled examples, practitioners can generate training data with models, then use it for instruction-tuning, preference-tuning, and pretraining.
The article frames synthetic data not as a replacement for human input in all cases, but as a practical workaround when annotations are limited. It walks through how to generate synthetic data and how to integrate it into finetuning pipelines, with attention to using it effectively.
The guide's significance lies in its practical focus: it treats synthetic data as a tool for reducing dependence on human annotations, while acknowledging that generation alone is not enough—the data must be used well in finetuning.