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Epistemic diversity across language models mitigates knowledge collapse

As artificial intelligence (AI) becomes more widely used, concerns are growing that model collapse could lead to knowledge collapse, i.e. a degradation to a narrow and inaccurate set of ideas. Prior work has demonstrated single-model collapse, defined as performance decay in an AI model trained on its own outputs. Inspired by ecology, we ask whether increasing AI ecosystem diversity (i.e., the number of distinct models) can mitigate such collapse. To study the effect of diversity on model performance, we extend the single-model approach by segmenting the training data across an increasing number of language models and evaluating the resulting ecosystems of models over ten self-training iterations. We find that training a single model on the entire dataset improves performance only in the short term but amplifies collapse over longer horizons. Specifically, we observe that the optimal diversity level (i.e., the level that maximizes performance) increases monotonically with the number of self-training iterations. The observed effect is robust across various experimental settings, including different model families, parameter sizes, mixing human- and model-generated data, and temperature sampling methods, demonstrating the significance of ecosystem diversity for mitigating collapse. Moreover, our experiments with increased model and dataset sizes indicate that scaling up the system can amplify collapse in highly homogeneous ecosystems, thereby increasing the diversity benefits. In the presence of AI monoculture, our results suggest the need to monitor (dis)agreement among AI systems and to incentivize more domain- and community-specific models to ensure successful knowledge production in the long run.

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