Ilia Shumailov and colleagues demonstrate that training AI models using data generated by previous models can lead to 'model collapse', where the models increasingly detach from real-world information, producing nonsensical outputs.
In iterative training cycles, language models tend to generate sentences that seem probable but are increasingly divorced from human-like coherence, resulting in meaningless sequences that challenge the value of the generated content.
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