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.

介绍了大型语言模型的发展并探讨了模型生成内容在训练中产生的影响，它导致原始内容分布的尾部消失并称之为模型失忆现象 (model dementia)，此现象在各种生成模型中普遍存在，为了利用从网络中爬取的大规模数据所获得的收益，我们必须认真对待它。