Model Collapse in AI: Implications and Insights

AI Models and Recursive Data Training

This study explores "model collapse," where AI models degrade when trained on data generated by previous models. Recursive training causes the models to lose information, particularly in low-probability events, leading to convergence on less accurate distributions over time. This phenomenon affects large language models (LLMs) and other generative models, emphasizing the need for diverse, high-quality training data to maintain AI performance and fairness.