Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training
arXiv:2602.16065v1 Announce Type: new Abstract: Generative Artificial Intelligence (AI), such as large language models (LLMs), has become a transformative force across science, industry, and society. As these systems grow in popularity, web data becomes increasingly interwoven with this AI-generated material and it is increasingly difficult to separate them from naturally generated content. As generative models are updated regularly, later models will inevitably be trained on mixtures of human-generated data and AI-generated data from earlier versions, creating a recursive training process with data contamination. Existing theoretical work has examined only highly simplified settings, where both the real data and the generative model are discrete or Gaussian, where it has been shown that such recursive training leads to model collapse. However, real data distributions are far more complex, and modern generative models are far more flexible than Gaussian and linear mechanisms. To fill
arXiv:2602.16065v1 Announce Type: new Abstract: Generative Artificial Intelligence (AI), such as large language models (LLMs), has become a transformative force across science, industry, and society. As these systems grow in popularity, web data becomes increasingly interwoven with this AI-generated material and it is increasingly difficult to separate them from naturally generated content. As generative models are updated regularly, later models will inevitably be trained on mixtures of human-generated data and AI-generated data from earlier versions, creating a recursive training process with data contamination. Existing theoretical work has examined only highly simplified settings, where both the real data and the generative model are discrete or Gaussian, where it has been shown that such recursive training leads to model collapse. However, real data distributions are far more complex, and modern generative models are far more flexible than Gaussian and linear mechanisms. To fill this gap, we study recursive training in a general framework with minimal assumptions on the real data distribution and allow the underlying generative model to be a general universal approximator. In this framework, we show that contaminated recursive training still converges, with a convergence rate equal to the minimum of the baseline model's convergence rate and the fraction of real data used in each iteration. To the best of our knowledge, this is the first (positive) theoretical result on recursive training without distributional assumptions on the data. We further extend the analysis to settings where sampling bias is present in data collection and support all theoretical results with empirical studies.
Executive Summary
This article presents a theoretical framework for analyzing the convergence of generative artificial intelligence (AI) models under recursive training with data contamination. The authors demonstrate that recursive training still converges, even when the data distribution is unknown and the generative model is a general universal approximator. The convergence rate is determined by the minimum of the baseline model's convergence rate and the fraction of real data used in each iteration. This result has important implications for the development and deployment of large language models (LLMs) and other generative AI systems. The authors also extend their analysis to settings with sampling bias and provide empirical studies to support their theoretical results.
Key Points
- ▸ Generative AI models can converge under recursive training with data contamination
- ▸ The convergence rate depends on the baseline model's convergence rate and the fraction of real data used
- ▸ The framework allows for general universal approximators and unknown data distributions
Merits
Strength of Theoretical Framework
The authors develop a rigorous theoretical framework that can be applied to a wide range of generative AI models and data distributions. This framework provides a foundation for understanding the behavior of these models under recursive training with data contamination.
Empirical Support
The authors provide empirical studies to support their theoretical results, which helps to demonstrate the practical relevance of their findings.
Extension to Sampling Bias
The authors extend their analysis to settings with sampling bias, which is an important consideration in many real-world applications.
Demerits
Limitation of Simplified Settings
The authors acknowledge that their framework may not capture all the complexities of real-world data distributions, which could limit its applicability in certain contexts.
Assumption of Universal Approximation
The authors assume that the generative model is a universal approximator, which may not be realistic in all cases.
Expert Commentary
This article presents a significant contribution to the theoretical understanding of generative AI models under recursive training with data contamination. The authors' framework provides a rigorous and generalizable analysis of the convergence behavior of these models. The empirical studies and extension to sampling bias further strengthen the results. However, the authors' assumptions about the generative model and data distribution may limit the applicability of their framework in certain contexts. Nevertheless, this work has important implications for the development and deployment of generative AI systems, and it provides a foundation for future research in this area.
Recommendations
- ✓ Further research should be conducted to investigate the robustness of the authors' framework to more complex data distributions and generative models.
- ✓ The authors' results should be extended to other types of generative AI models, such as those based on probabilistic programming or reinforcement learning.
