The problem of model collapse: why recursive training might lead to the decline of large language models

Abstract

The paper examines the phenomenon of model collapse in Large Language Models caused by recursive training on synthetic data. It analyses the statistical mechanics of distributional decay, identifies the progressive stages of knowledge degradation, evaluates the sociological impact on digital platforms, and proposes technical mitigation strategies to preserve the integrity of the artificial intelligence ecosystem.