5. Model Collapse

• Definition: A state where a model becomes less "creative" and more biased in its outputs, potentially compromising its functionality.
• Causes: Overreliance on synthetic data without proper curation and mixing with fresh, real-world data.
• Consequences: Models may become increasingly homogeneous and less capable of handling diverse or novel tasks.