Unverified60% confidenceFactExact time
In 2023, researchers from Oxford University and other institutions published a study finding that after only 5–10 generations of recursive training, the degradation in model output quality became visible to the naked eye.
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8/31/2026
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VerifiedA 2024 study indicates that even when AI-generated content accounts for only 10–20% of training data, measurable quality degradation can occur after multiple generations of training.80% similarUnverifiedIn 2023, researchers demonstrated that contaminating less than 1% of training data is sufficient to alter model behavior under specific trigger conditions77% similarUnverified牛津大学2023年的一项研究量化了模型坍缩效应，仅经过五代自我训练，文本多样性指标下降了约40%76% similarVerifiedA 2023 joint study by Oxford University, Cambridge University, and other institutions elaborated on the model collapse effect: when a model is iteratively trained on its own generated data, the statistical variance of outputs continuously decreases, rare long-tail knowledge disappears first, and the model ultimately converges to a cultural mean state.75% similarUnverifiedExperiments conducted by the research team on models such as GPT-2 showed that after only 5–9 generations of recursive training, the quality of model outputs degrades significantly.74% similar
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