4. Loss of Nuanced Knowledge
- Generic outputs: Research published in Nature shows that models trained on error-ridden synthetic data tend to lose their grasp of more esoteric or specialized knowledge over generations.
- Relevance degradation: These models may increasingly produce answers that are irrelevant to the questions they're asked.
- Broader impact: This phenomenon isn't limited to text-based models; image generators and other AI systems are also susceptible to this type of degradation.