arXiv:2605.23054v1 Announce Type: cross Abstract: Model collapse, the progressive degradation of LLMs trained on their own outputs, has been characterized statistically but lacks a linguistic explanation for which structures degrade, in what order, and why. We show that iterated learning theory from cultural evolution fills this gap. We derive five falsifiable predictions, distinguish those uniquely discriminative for the theory from confirmatory ones, and test them by self-training LLaMA-2-7B and Mistral-7B over 10 generations in English, German, and Turkish. The critical discriminative findi
The proliferation of LLMs and their increasing reliance on synthetic data for training makes model collapse an immediate and critical concern for AI development. This research addresses a fundamental challenge emerging from current AI training paradigms.
Understanding model collapse is crucial for the sustainable development and deployment of advanced AI models, impacting the quality and reliability of future AI systems. It highlights a hard technical constraint in the scaling and self-improvement of LLMs.
This research provides a linguistic and cultural evolutionary framework to explain model collapse, moving beyond statistical characterizations to offer actionable insights for mitigation. It shifts the approach to diagnosing and potentially preventing degradation in AI models.
- · AI researchers
- · Organizations developing robust LLMs
- · Companies with diverse and high-quality proprietary data
- · AI companies reliant solely on synthetic data
- · Generative AI models with poor self-correction mechanisms
- · Platforms with weak data curation practices
Further research and development into new training methodologies that explicitly counteract model degradation will accelerate.
The value of diverse, high-fidelity human-generated data will increase significantly, potentially impacting data acquisition strategies and costs.
New regulatory frameworks or industry standards may emerge around data provenance and training data transparency for critical AI applications to prevent unreliability due to model collapse.
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