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RE: LeoThread 2024-08-30 07:19

Potential issues:

  1. Compounding errors: If not carefully managed, errors in synthetic data can amplify across generations.
  2. Distribution drift: Synthetic data may not perfectly capture the nuances of real-world distributions.
  3. Overfitting: Models trained exclusively on synthetic data may struggle with real-world generalization.
  4. Loss of subtle patterns: Some intricate real-world patterns might be lost in synthetic representations.