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@eliasulrich.hachyderm.io.ap.brid.gy Sep 27, 2026, 2:28 PM

#LLMs & #ModelColapse : "According to the #research, indiscriminately using model-generated content in training causes "irreversible defects" in the resulting #ai. the model loses the "tails of the original content distribution." in other words, it begins to […]

[Original post on hachyderm.io]

AI models collapse when trained on recursively generated data Ilia Shumailov et al. Nature. 2024 Jul. Show details Full text links Cite Linked Discoveries Erratum in Author Correction: AI models collapse when trained on recursively generated data. Shumailov I, et al. Nature. 2025. PMID: 40119087 Free PMC article. No abstract available. 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).

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