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When synthetic examples start to help
Question: What condition must synthetic data satisfy before it can substantially improve training, and what benefit does meeting that condition provide?
Sample answer: The synthetic data has to be realistic enough to resemble the true data distribution. If it is close enough, the main benefit is that the team can effectively train on a much larger dataset.
Key points:
- Synthetic data must be close to the real distribution.
- The payoff is access to a much larger training set.
Rubric: Full credit requires stating that the synthetic data should match or closely approximate the real distribution and that this makes a much larger training set available.
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When synthetic examples start to help