Ah, to clarify: Model Collapse is still an issue - one for which mitigation techniques are already being developed and applied, and have been for a while. While yes currently LLM content is harder to train against, there’s no reason that must always hold true - this paper actually touches on that weird aspect! Right now, we have to be careful to design with model collapse in mind and work to mitigate it manually, but as the technology improves it’s theorized that we’ll hit a point at which models coalesce towards stability, not collapse, even when fed training data that was generated by an LLM. I’ve seen the concept called Generative Bootstrapping or the Bootstrap Ladder (it’s a new enough concept that we haven’t all agreed on a name for it yet. we can only hope someone comes up with something better because wow the current ones suck…). We’re even seeing some models that are starting to do this coalesce-towards-stability thing, though only in some extremely niche applications. Only time will tell if all models are able to do this stable-coalescing or if it’s only possible in some cases.
My original point though was just that this headline is fairly sensationalist, and that people shouldn’t take too much hope from this collapse because we’re both aware of it, and are working to mitigate it (exactly like the paper itself cautions us to do)