I\u2019ve been thinking about the role of synthetic datasets in data projects, especially now that LLMs and generative models make data generation much easier.<\/p>\n
On one hand, synthetic data can help with privacy, class imbalance, rare cases, benchmarking, and testing pipelines when real data is limited or sensitive.<\/p>\n
On the other hand, I\u2019m not sure how people evaluate whether a synthetic dataset is actually useful rather than just plausible-looking. Distribution shift, hidden bias, leakage from source data, and weak evaluation seem like real risks.<\/p>\n
For people who have used synthetic datasets in practice: when did they work well, and when did they fail?<\/p>\n
Also, what checks or metrics do you use before trusting a synthetic dataset for training, evaluation, or analysis?<\/p>\n
Thanks in advance for any thoughts. This is especially important for me because one of the core directions I\u2019m working on in OpenDCAI\/DataFlow is large-scale synthetic data generation, and a recurring challenge is figuring out whether the synthetic data is actually useful.<\/p>\n<\/div>\n
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