Model collapse
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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.
References[edit]
- ^ 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].
- ^ Ozsevim, Ilkhan (2023-06-20). "Research finds ChatGPT & Bard headed for 'Model Collapse'". Retrieved 2024-03-06.
- ^ Self-Consuming Generative Models Go MAD. The Twelfth International Conference on Learning Representations.
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