Large language models like ChatGPT are trained using extensive datasets from human writing, often sourced from all over the internet. However, a growing paradox arises as generative AI creates its own text, potentially causing future AI models to train on this synthetic material rather than original, human-written content. This self-referential training can lead to errors and a decline in model performance. The implications of this cycle raise important questions about the nature and reliability of AI-generated information.
Large language models, such as ChatGPT, can generate content by learning from extensive human-written text examples, highlighting potential issues from training data.
As AI-generated content fills the internet, newer models risk training on their own synthetic material, which may degrade their performance.
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