The Curse of Recursion: Training on Generated Data Makes Models Forget

TL;DR

This paper investigates how training models on generated data leads to irreversible degradation of original content diversity, a phenomenon termed Model Collapse, affecting various generative models including LLMs.

Contribution

It introduces the concept of Model Collapse, demonstrating its occurrence across multiple generative models and providing theoretical insights into this widespread issue.

Findings

Model Collapse causes loss of original content distribution tails.

The phenomenon is observed in VAEs, GMMs, and LLMs.

Training on generated data can irreversibly degrade model quality.

Abstract

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.