“You can certainly imagine that the same happens with machine learning models,” he says. “So if the first model has seen half of the internet, then perhaps the second model is not going to ask for half of the internet, but actually scrape the latest 100,000 tweets, and fit the model on top of it.” Additionally, the internet doesn’t hold an unlimited amount of data. To feed their appetite for more, future AI models may need to train on synthetic data—or data that has been produced by AI. “Foundation models really rely on the scale of data to perform well,” says Shayne Longpre, who studies how LLMs are trained at the MIT Media Lab, and who didn’t take part in this research. “And they’re looking to synthetic data under curated, controlled environments to be the solution to that. Because if they keep crawling more data on the web, there are going to be diminishing returns.”
Matthias Gerstgrasser, an AI researcher at Stanford who authored a different paper examining model collapse, says adding synthetic data to real-world data instead of replacing it doesn’t cause any major issues. But he adds: “One conclusion all the model collapse literature agrees on is that high-quality and diverse training data is important.” Another effect of this degradation over time is that information that affects minority groups is heavily distorted in the model, as it tends to overfocus on samples that are more prevalent in the training data. In current models, this may affect underrepresented languages as they require more synthetic (AI-generated) data sets, says Robert Mahari, who studies computational law at the MIT Media Lab (he did not take part in the research). One idea that might help avoid degradation is to make sure the model gives more weight to the original human-generated data. Another part of Shumailov’s study allowed future generations to sample 10% of the original data set, which mitigated some of the negative effects.