Under recursive synthetic training, a 1B-parameter LLM loses factual accuracy while preserving fluent, confident output, with collapse timing depending on prompt format and domain-aligned training.
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Knowledge Collapse in LLMs: When Fluency Survives but Facts Fail under Recursive Synthetic Training
Under recursive synthetic training, a 1B-parameter LLM loses factual accuracy while preserving fluent, confident output, with collapse timing depending on prompt format and domain-aligned training.