Short Wave When AI Cannibalizes Its Data
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Feb 18, 2025 
Ilya Shumailov, a computer scientist researching AI model collapse, dives into the intriguing world of generative AI. He explains how large language models, like ChatGPT, are beginning to consume their own synthetic content, leading to potential quality declines. Ilya reveals the risks and errors that arise from this self-referential data usage, comparing it to a game of telephone. He emphasizes the importance of high-quality data and outlines strategies to combat model collapse, shedding light on the future of AI-generated content.
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Sources of LLM Errors
- Large language models (LLMs) can make errors due to limited data, training methods, and model design.
- These errors can compound, leading to inaccurate or biased outputs, especially for rare events.
Baby Peacock Example
- Googling "baby peacock" often shows AI-generated images, not real ones.
- This highlights how LLMs can perpetuate misinformation when trained on insufficient or inaccurate data.
Model Collapse Explained
- Repeatedly training LLMs on their own synthetic data leads to model collapse.
- Improbable events disappear, and data converges towards the average, reducing diversity.