Data Quality is All You Need: Why Synthetic Data Is Not A Replacement For High-Quality Data | HackerNoon

Synthetic data cannot replace high-quality original data, as it poses risks like model collapse, especially when models rely heavily on recursively generated synthetic data.

The Nature article emphasizes that models trained on recursively generated data can become biased towards that data, degrading performance when faced with actual real-world input.

Understanding whether the transformer architecture's reliance on self-attention increases its susceptibility to model collapse is crucial for effective machine learning application development.

High-quality data quality, lineage, observability, and monitoring practices are essential in mitigating risks associated with synthetic data and model collapse in machine learning.

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