Alemohammad, S., et al. (n.d.).
OpenReview.
Abstract:
Seismic advances in generative AI algorithms for imagery, text, and other data types have led to the temptation to use AI-synthesized data to train next-generation models. Repeating this process creates an autophagous ("self-consuming") loop whose properties are poorly understood. We conduct a thorough analytical and empirical analysis using state-of-the-art generative image models of three families of autophagous loops that differ in how fixed or fresh real training data is available through the generations of training and whether the samples from previous-generation models have been biased to trade off data quality versus diversity. Our primary conclusion across all scenarios is that without enough fresh real data in each generation of an autophagous loop, future generative models are doomed to have their quality (precision) or diversity (recall) progressively decrease. We term this condition Model Autophagy Disorder (MAD), by analogy to mad cow disease, and show that appreciable MADness arises in just a few generations.
Here are some thoughts:
This study explored the potential consequences of autophagous loops in generative models, where models train future models using synthetic data. This phenomenon, known as Model Autophagy Disorder (MAD), can lead to a degradation of model quality and diversity, ultimately poisoning the entire Internet's data quality and diversity if left uncontrolled.
The researchers identified three families of autophagous loops and found that sampling bias plays a crucial role in the development of MAD. Without sufficient fresh real data, future generative models will inevitably suffer from MAD, leading to decreased quality and diversity. This has significant implications for practitioners working with generative models, particularly those using synthetic training data.
To mitigate the risks of MAD, practitioners can take steps to control the ratio of real-to-synthetic training data and identify synthetic data through watermarking or other methods. However, watermarking introduces hidden artifacts that can be amplified by autophagy, highlighting the need for autophagy-aware watermarking techniques. Future research should focus on developing these techniques, examining the effects of MADness on downstream tasks, and exploring the implications for other data types, such as language models.
The study's conclusions serve as a warning for practitioners, highlighting the need for careful consideration of the potential risks and consequences of autophagous loops. As generative models become increasingly ubiquitous, it is essential to address the risks associated with MAD to prevent a decline in data quality and diversity. By understanding the causes and consequences of MAD, practitioners can take steps to prevent its occurrence and ensure the continued development of high-quality generative models.
