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 refer to this effect as Model Collapse and show that it can occur in Variational Autoencoders, Gaussian Mixture Models 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（Stable Diffusion）革新了根据描述性文本生成图像的技术。GPT-2、GPT-3(.5)及GPT-4在多种语言任务中展现出惊人性能。ChatGPT将此类语言模型引入公众视野。显然，大型语言模型（LLM）将持续存在，并将彻底改变在线文本与图像的整体生态。本文展望未来可能面临的境况：当LLM贡献了互联网上的大部分语言内容后，GPT-{n}将何去何从？我们发现，在训练中使用模型生成内容会导致最终模型出现不可逆的缺陷，原始内容分布的尾部特征将逐渐消失。我们将此效应称为"模型坍缩"（Model Collapse），并证明其在变分自编码器（VAE）、高斯混合模型（GMM）及LLM中均可能发生。我们从理论层面阐述这一现象的内在机理，并揭示其在所有可学习生成模型中的普遍性。研究表明，若想维持从网络海量数据中训练所获得的益处，必须严肃对待此问题。事实上，当互联网抓取数据中包含LLM生成的内容时，关于人类与系统真实交互的采集数据价值将愈发珍贵。