AI models collapse when trained on recursively generated data

Ilia Shumailov (), Zakhar Shumaylov (), Yiren Zhao, Nicolas Papernot, Ross Anderson and Yarin Gal ()
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Ilia Shumailov: University of Oxford
Zakhar Shumaylov: University of Cambridge
Yiren Zhao: Imperial College London
Nicolas Papernot: University of Toronto
Ross Anderson: University of Cambridge
Yarin Gal: University of Oxford

Nature, 2024, vol. 631, issue 8022, 755-759

Abstract: Abstract Stable diffusion revolutionized image creation from descriptive text. GPT-2 (ref. 1), GPT-3(.5) (ref. 2) and GPT-4 (ref. 3) demonstrated high performance across a variety of language tasks. ChatGPT introduced such language models to the public. It is now clear that generative artificial intelligence (AI) such as large language models (LLMs) is here to stay and will substantially change the ecosystem of online text and images. Here we consider what may happen to GPT-{n} once LLMs contribute much of the text found online. We find that indiscriminate use of model-generated content in training causes irreversible defects in the resulting models, in which tails of the original content distribution disappear. We refer to this effect as ‘model collapse’ and show that it can occur in LLMs as well as in variational autoencoders (VAEs) and Gaussian mixture models (GMMs). We build theoretical intuition behind the phenomenon and portray its ubiquity among all learned generative models. We demonstrate that it must be taken seriously if we are to sustain the benefits of training from large-scale data scraped from the web. Indeed, the value of data collected about genuine human interactions with systems will be increasingly valuable in the presence of LLM-generated content in data crawled from the Internet.

Date: 2024
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DOI: 10.1038/s41586-024-07566-y

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