>> There's already been the studies that these models quickly break down when they're fed their own generated data as training data Not "these models" if you'r

>> There's already been the studies that these models quickly break down when they're fed their own generated data as training data Not "these models" if you're referring to large language models pretrained and fine-tuned with Transformers. The only study I am aware of that explored the effect you discuss [1] had to make do with very different models as proxies: Variational Autoencoders, Mixture of Gaussians and a smaller LLM with 125 million parameters that hardly qualifies as one. It is not safe to extrapolate those results to really Large-LMs trained with Transformers, as it is not safe to extrapolate any results on one neural net architecture on another. Note the Mixtures of Gaussians aren't even neural nets at all, but a completely different statistical model. The question of what will happen once a few generations of LLMs have trained on their ancestors' outputs is an interesting one that has not yet been clearly answered. In the long run, future developments may even mean it's not really a valid question at all because such feedback retraining can't happen in practice- who knows? I for one remain curious to see how this goes. ______________________ [1] The Curse of Recursion: Training on Generated Data Makes Models Forget https://arxiv.org/abs/2305.17493 https://arxiv.org/abs/2305.17493