Stable Diffusion revolutionised image creation from descriptive text. GPT-2, GPT-3(.5) and GPT-4 demonstrated astonishing performance across a variety of language tasks. ChatGPT introduced such language models to the general public. It is now clear that large language models (LLMs) are here to stay, and will bring about drastic change in the whole ecosystem of online text and images. In this paper we consider what the future might hold. What will happen to GPT-{n} once LLMs contribute much of the language found online? We find that use of model-generated content in training causes irreversible defects in the resulting models, where tails of the original content distribution disappear. We call this effect model dementia and show that it can occur in Variational Autoencoders (VAEs), Gaussian Mixture Models (GMMs) and LLMs. We build theoretical intuition behind the phenomenon and portray its ubiquity amongst all learned generative models. We demonstrate that it has to 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 content generated by LLMs in data crawled from the Internet.

翻译：稳定扩散模型彻底革新了基于描述性文本的图像生成方式。GPT-2、GPT-3(.5)与GPT-4在多种语言任务中展现出惊人的性能。ChatGPT将这些语言模型引入公众视野。如今显而易见的是，大型语言模型（LLMs）将持续存在，并将彻底改变在线文本与图像构成的整个生态系统。本文探讨了未来可能的发展趋势：当LLMs贡献了互联网上大部分语言内容时，GPT-{n}将会面临何种命运？我们发现，在训练中使用模型生成的内容会导致最终模型出现不可逆的缺陷，其中原始内容分布的尾部特征将消失。我们将此效应称为"模型痴呆症"，并证明该现象会出现在变分自编码器（VAEs）、高斯混合模型（GMMs）以及LLMs中。我们构建了该现象背后的理论直觉，并描绘其在所有可学习生成模型中的普遍性。研究表明，若要维持从大规模网络抓取数据中训练模型所获得的收益，必须严肃对待这一问题。事实上，在互联网爬取数据中充斥着LLMs生成内容的背景下，关于人类与系统真实交互行为所采集的数据将愈发珍贵。