Data
Synthetic data
What is synthetic data?
Synthetic data is training data generated by a model rather than collected from real activity. It is useful for covering cases that are rare in real data and risky as a wholesale substitute, because it carries the generating model’s biases and blind spots.
The strongest use is targeted: you have identified a category your model handles badly and you have too few real examples of it. Generating more of that specific case, then having a person check them, is efficient and low-risk.
The weakest use is generating an entire dataset from a larger model and training on it unfiltered. The result is a smaller model that reproduces the larger one’s mistakes with less capacity to recover from them.
Whatever the source, provenance should be recorded per record. A dataset where nobody can say which examples were generated is a dataset nobody can debug.
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