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The paper demonstrates that supervised fine-tuning can correct mode collapse and over-dispersion in large language models by showing that diversity converges to the target distribution with sufficient data, supported by theoretical bounds and experiments.
This paper analyzes LLM outputs over three years using open-ended tasks and creativity assessments, finding a statistically significant decrease in diversity that suggests creative substance is converging across models, which could diminish human agency in co-creative work.