# A Note on Shumailov et al. (2024): `AI Models Collapse When Trained on Recursively Generated Data'

arXiv: https://arxiv.org/abs/2410.12954 · HTML: https://mlsanity.com/paper/2410.12954/

## Key facts

- Cited by 115 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 1 references that are in the map.
- First posted on arXiv 16 Oct 2024.

## Built on by (most cited first, 30 of 115)

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## Abstract (arXiv)

The study conducted by Shumailov et al. (2024) demonstrates that repeatedly training a generative model on synthetic data leads to model collapse. This finding has generated considerable interest and debate, particularly given that current models have nearly exhausted the available data. In this work, we investigate the effects of fitting a distribution (through Kernel Density Estimation, or KDE) or a model to the data, followed by repeated sampling from it. Our objective is to develop a theoretical understanding of the phenomenon observed by Shumailov et al. (2024). Our results indicate that the outcomes reported are a statistical phenomenon and may be unavoidable.