The Curse of Recursion: Training on Generated Data Makes Models Forget

The paper discusses the potential impact of large language models (LLMs) like GPT-3 and ChatGPT on the future of online content and the models themselves, introducing the concept of "Model Collapse." This phenomenon occurs when LLMs are trained on content previously generated by other models, leading to irreversible issues in the new models, such as the disappearance of unique or rare elements of the original data distribution. This issue is not exclusive to LLMs but can occur in other generative models like Variational Autoencoders and Gaussian Mixture Models.

The authors provide a theoretical explanation for Model Collapse and demonstrate its ubiquity across different types of learned generative models. As LLMs become more prevalent, the value of data collected from genuine human interactions will become increasingly important. The data used to train these models must be carefully curated to avoid Model Collapse and ensure the models continue to provide the benefits we've come to expect from large language models.

Key takeaways:

The paper explores the concept of 'Model Collapse' in large language models (LLMs) like GPT-3 and ChatGPT, where training these models on content generated by other models can lead to irreversible issues.
This phenomenon can cause unique or rare elements of the original data distribution to disappear, making the models less diverse and representative of genuine human-generated content.
The issue of Model Collapse is not limited to LLMs, but can also occur in other generative models like Variational Autoencoders and Gaussian Mixture Models.
As LLMs become more prevalent, the value of data collected from genuine human interactions will become increasingly important to avoid Model Collapse and maintain the benefits and diversity of these models.

The Curse of Recursion: Training on Generated Data Makes Models Forget | AI Research Paper Details

Key takeaways:

Comments (0)

Newsletter