When is synthetic data useful for matching a development set?
Question: Explain when artificial data generation is a practical strategy for making training data more similar to a development set. In your answer, describe the conditions that make it worthwhile and what the generated examples need to resemble.
Sample answer: Synthetic data is useful when it lets a team produce a large amount of training data that is close enough to the development set to be helpful. It is especially valuable when collecting enough real examples is difficult, but the team can still design or simulate examples that capture the main characteristics of the development data.
Key points:
- Appropriate when there is a training/dev distribution gap that synthetic examples can help reduce
- Useful when it enables a very large training set
- The generated data should resemble the development set closely enough to support the target task
Rubric: The answer must state that synthetic data is helpful when it allows creation of a large dataset, and that the synthesized examples should reasonably match the development set.
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