
Indiscriminate use of model-generated content in training causes irreversible defects in resulting generative AI models, leading to the disappearance of tails of the original content distribution, a phenomenon referred to as 'model collapse'.


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&nbsp;Analysis shows that indiscriminately training generative artificial intelligence on real and generated content, usually done by scraping data from&nbsp;the Internet, can lead to a collapse in the ability of the models to generate diverse high-quality output.

AI models collapse when trained on recursively generated data - Nature

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Generative AI Models Exhibit Catastrophic Collapse When Trained on Recursively Generated Data
