Degradation of AI models trained on synthetic data
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Model collapse, also known as AI collapse, refers to the gradual degradation in the output of a generative artificial intelligence model trained on synthetic data, meaning the outputs of another model (including prior versions of itself).[1][2][3]
Repeating this process for generation after generation of models forms a so-called "autophagous" (self-consuming) loop. [4]
Theoretical and empirical analysis has demonstrated that, without enough fresh real data in each generation of an autophagous loop, future generative models are doomed to have their quality (precision) or diversity (recall) progressively decrease.[citation needed]
Model collapse in generative models can be avoided by accumulating data
Recent[when?] work challenged the claim that frontier models are headed for model collapse, showing that model collapse does not occur under the realistic assumption that data accumulates over time.[5]
^Mok, Aaron. "A disturbing AI phenomenon could completely upend the internet as we know it". Business Insider. Retrieved 2024-03-06.
^Shumailov, Ilia; Shumaylov, Zakhar; Zhao, Yiren; Gal, Yarin; Papernot, Nicolas; Anderson, Ross (2023-05-31). "The Curse of Recursion: Training on Generated Data Makes Models Forget". arXiv:2305.17493 [cs.LG].
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