Model Collapse: Comprehensive Review of AI Degradation from Synthetic Data Training

Model collapse is a phenomenon where an AI model's performance degrades when trained on data generated by previous models, leading to loss of diversity and error accumulation.

Companies using synthetic data to expand datasets may face declining model accuracy over time, impacting critical applications like financial analytics and healthcare.

Solutions include maintaining a proportion of real data, using controlled mixing techniques, continuous output quality monitoring, and developing validation standards for synthetic data.