What should synthesized training data achieve when it is built to mirror a dev set?
Question: If you generate artificial training examples to bring the training distribution closer to the development set, what two properties should the resulting dataset have?
Sample answer: It should contain many examples, and those examples should resemble the development data closely enough to be useful for evaluation and tuning.
Key points:
- It should be large in size
- It should approximate the development set well
Rubric: The answer is correct if it states that the synthesized dataset should be large and that it should closely resemble or match the development set.
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What should synthesized training data achieve when it is built to mirror a dev set?