Is synthetic data useful for further training of AI models and knowledge context? Or does it degrade with each generation?
Synthetic data can indeed be useful for further training AI models and expanding knowledge contexts, but its effectiveness and potential for degradation depend on several factors. Let's break this down:
Benefits of synthetic data:
- Data augmentation: Increases dataset size and diversity.
- Privacy preservation: Allows training on sensitive data without exposing real information.
- Addressing data scarcity: Helpful for rare events or underrepresented classes.
- Controlled experiments: Enables testing specific scenarios or edge cases.