"Repeated synthesis by language models can accumulate information distortion across successive generations."
Mohamed and colleagues studied iterative LLM generation using chained translation experiments and observed accumulated information degradation. Their results show that passing generated material through repeated model transformations can introduce progressively greater divergence from its original content, supporting the warning about distortion in automated processing loops.
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