Model Collapse Demystified: The Case of Regression

In the era of large language models like ChatGPT, the phenomenon of "model collapse" refers to the situation whereby as a model is trained recursively on data generated from previous generations of itself over time, its performance degrades until the model eventually becomes completely useless, i.e the model collapses. In this work, we study this phenomenon in the simplified setting of kernel regression and obtain results which show a clear crossover between where the model can cope with fake data, …

case chatgpt cs.ai cs.lg data eventually generated language language models large language large language models model collapse performance regression stat.ml work