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SynthSentry: Detecting Synthetic Data Contamination in Language Model Training Data
Large language models trained recursively on their own or other models' outputs undergo model collapse, in which distributional tails and factual accuracy deteriorate while fluency survives. Prior work diagnoses collapse after training; the actionable problem is screening a corpus of unknown provena
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