What happens when AI learns from AI? Researchers warn of impending chaos with ‘model collapse’

(Image credits: “The Curse of Recursion: Training on Generated Data Makes Models Forget” research paper)

This can become a possibility with the growing use of artificial intelligence tools. Widespread AI usage will likely eventually lead to AI-generated content being fed to large language models as training data, leading to inaccuracies and distortions in their output.

This problem was observed in large language models, Variational Autoencoders, and Gaussian Mixture Models, which over time begin to “forget the true underlying data distribution,” leading to an inaccurate depiction of reality because the data they’re trained on becomes so polluted that it loses any semblance to real-world data.

Given the serious risk of model collapse, the researchers highlight the importance of access to the original distribution data, which generally is human-produced. After all, AI language models are designed to interact with humans, and therefore, need to be in touch with reality to properly simulate our world.

To solve this problem, the researchers have proposed several smarter approaches to training large language models. One approach is the “first-mover advantage,” which stresses that access to the original human-generated data source is preserved.

However, since differentiating AI-generated data from human-produced data is difficult, the research paper articulates that “community-wide coordination” is essential to ensure that different parties involved in LLM creation and deployment share the information needed to determine the source of data.

“Otherwise, it may become increasingly difficult to train newer versions of LLMs without access to data that was crawled from the Internet prior to the mass adoption of the technology, or direct access to data generated by humans at scale,” adds the paper.

But amid the rising use of generative AI and concerns around the technology taking over jobs, there is a silver lining for human creators. The research paper theorises that as the internet gets populated with AI-generated data, human-created content will grow increasingly valuable – even if it’s only as a source for uncontaminated data to train large language models.