For Heads-of · Practitioner
Model Collapse
A phenomenon where training language models on synthetic data generated by other language models leads to degradation of model quality and diversity over successive generations.
- model collapse
- training data
- synthetic data
- degradation
Mechanism
As AI-generated content dominates training data, models learn artifacts and patterns of previous models rather than patterns in real human data, causing quality decay.
Risk Amplification
Errors in early models get perpetuated and amplified in downstream models, potentially leading to systematic failures across an ecosystem of AI systems.
Long-term Governance Concern
Model collapse suggests that relying heavily on synthetic data for training future systems could degrade AI capability over time; strategies for preserving high-quality real data are essential.