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Model collapse is a phenomenon noted in artificial intelligence studies, where machine learning models gradually degrade due to errors coming from uncurated synthetic data, or training on the outputs of another model, such as prior versions of itself. It is unclear to what extent the phenomenon threatens the long-term development of such models, and some techniques have been proposed to mitigate the effect.
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Search interest data over the past 12 months indicates that this topic periodically attracts global attention. Sudden spikes often correlate with major news events, public statements, or geopolitical developments.