Position: the Stochastic Parrot in the Coal Mine. Model Collapse is a Threat to Low-Resource Communities
arXiv:2605.04127v2 Announce Type: replace-cross Abstract: Model collapse, the degradation in performance that arises when generative models are trained on the outputs of prior models, is an increasing concern as artificially generated content proliferates. Related critiques of large language models have highlighted their tendency to reproduce frequent patterns in training data, their reliance on vast datasets, and their substantial environmental cost. Together, these factors contribute to data degradation, the reinforcement of cultural biases, and inefficient resource use. In this position pap
The proliferation of AI-generated content and the increasing reliance on LLMs for data synthesis are making model collapse a more immediate and visible threat.
Model collapse, data degradation, and reinforcement of biases directly threaten the long-term viability and ethical deployment of generative AI, particularly for communities with fewer resources.
This highlights the urgent need for new data strategies, robust model evaluation, and ethical AI development practices to prevent the degradation of AI systems and exacerbation of digital divides.
- · Ethical AI developers
- · Data provenance solutions
- · Synthetic data pioneers
- · Low-resource communities
- · Unregulated AI model providers
- · Data-hungry generative AI models
Reduced utility and trustworthiness of AI-generated content as model performance degrades.
Increased investment in human-curated datasets and techniques to mitigate synthetic data reliance.
Widening of the digital and information gap between well-resourced entities (who can afford quality data) and low-resource communities.
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