2024-07-30 14:59:08
Artificial Intelligence
Technology

Insights on AI Model Collapse and Data Accumulation

Recent research sheds light on AI model collapse in large generative models due to accumulating data, contrasting with degradation from data replacement. Experimental studies reveal that data accumulation can prevent or improve model performance, leading to finite error compared to linear degradation with replacement.

Theoretical analyses support the benefits of accumulation in maintaining controlled error levels. While previous studies focused on fixed dataset sizes, this work simulates evolving internet-scale data.

Strategies like using synthetic data safely for training, demonstrated in the Shumailov study, show promise in improving model performance without causing collapse.

marktechpost.com
29. Juli 2024 um 15:20

This AI Paper from Stanford Provides New Insights on AI Model Collapse and Data Accumulation - MarkTechPost

Technology
Large generative AI models face model collapse as their outputs accumulate in web-scale datasets. This Stanford study simulates evolving datasets, finding data accumulation prevents collapse unlike degradation from replacement. Experiments on language, diffusion, and VAE models on TinyStories, GEOM-Drugs, and CelebA show accumulation maintains/improves performance, contrasting linear error increase with replacement. Theoretical analysis proves accumulation yields finite, controlled error, unlike..
marktechpost.com
30. Juli 2024 um 09:00

This AI Paper Shows AI Model Collapses as Successive Model Generations Models are Recursively Trained on Synthetic Data - MarkTechPost

Technology
This paper examines the phenomenon of "model collapse" in AI, where large language models (LLMs) trained on data containing content generated by earlier model versions lose the ability to represent the true underlying data distribution over successive generations. The researchers provide a theoretical framework and empirical evidence demonstrating how models trained on recursively generated data gradually degrade in performance due to statistical approximation error, functional expressivity er..
THE DECODER
30. Juli 2024 um 10:17

AI data isn't destroying AI models after all, researchers say

Technology
The Shumailov study demonstrated cases of model collapse in various AI architectures, including language models, VAEs, and Gaussian mixture models. Researchers argue synthetic data can be used safely to augment training data, with Meta's LLaMA 3.1 model using synthetic data and "Execution Feedback" to improve performance without causing model collapse.
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