Asked ChatGPT anything lately? Talked with a customer service chatbot? Read the results of Google's "AI Overviews" summary feature? If you've used the Internet lately, chances are, you've consumed content created by a large language model. These models, like DeepSeek-R1 or OpenAI's ChatGPT, are kind of like the predictive text feature in your phone on steroids. In order for them to "learn" how to write, the models are trained on millions of examples of human-written text. Thanks in part to these same large language models, a lot of content on the Internet today is written by generative AI. That means that AI models trained nowadays may be consuming their own synthetic content ... and suffering the consequences.View the AI-generated images mentioned in this episode.Have another topic in artificial intelligence you want us to cover? Let us know my emailing [email protected]!Listen to every episode of Short Wave sponsor-free and support our work at NPR by signing up for Short Wave+ at plus.npr.org/shortwave.Learn more about sponsor message choices: podcastchoices.com/adchoicesNPR Privacy Policy
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You're listening to Shortwave from NPR. It seems like these days generative AI is everywhere. It's in my Google searches. It's suggested as a tool on TikTok. It's running customer service chats. And there's a lot of forms that generative AI can take, like it can create images or video.
But the ones that have been in the news recently, DeepSeek R1, OpenAI's ChatGBT, Google Gemini, Apple Intelligence, all of those are large language models. A large language model is kind of like the predictive text feature in your phone, but on steroids.
Large language models are statistical beasts that learn from example of human written text and learn to produce text that is similar to the ones that the model was taught.
That's Ilya Shumailov. He's a computer scientist and he says in order to teach these models, scientists have to train them on a lot of human written examples. Like, they basically make the models read the entire internet. which works for a while. But nowadays, thanks in part to these same large language models, a lot of the content on our internet is written by generative AI.
If you were today to sample data from internet randomly, I'm sure you'll find that a bigger proportion of it is generated by machines. But this is not to say that the data itself is bad. The main question is how much of this data is generated
In the spring of 2023, Elio was a research fellow at the University of Oxford. And he and his brother were talking over lunch. They were like, OK, if the Internet is full of machine-generated content and that machine-generated content goes into future machines, what's going to happen?
Quite a lot of these models, especially back at the time, they're relatively low quality. So there are errors and there are biases. There are systematic biases inside of those models. And thus, you can kind of imagine the case where rather than learning useful contexts, and useful concepts, you can actually learn things that don't exist. They are purely hallucinations.
Ilya and his team did this research study indicating that eventually, any large language model that learns from its own synthetic data would start to degrade over time, producing results that got worse and worse and worse.
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