AI models collapse when trained on recursively generated data
AI models collapse when trained on recursively generated data
Analysis shows that indiscriminately training generative artificial intelligence on real and generated content, usually done by scraping data from the Internet, can lead to a collapse in the ability of the models to generate diverse high-quality output.
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indiscriminate use of model-generated content
Indiscriminate is the key word in this paper. No one trains this way. Synthetic data and filtering out bad data are already very important steps for training and will continue to stay that way. With proper filtering and evaluation, models trained on synthetic data do better then the ones before.
This is not the end of ai, like so many wish it would be.
I don't think this is going to be the end of AI either, and the corpus of data before AI generated content became prevalent is also huge. So, I don't think there's really lack of training data. I personally think this is more interesting from the perspective of how these algorithms work in general. The fact that they end up collapsing when consuming their own content seems to indicate that the quality of content is fundamentally different from that generated by humans.
Yea that's completly fair, I think ai models in general to have lots of interesting characteristics that are very different from humans. I just see a lot of people taking conclusions from papers like this that aren't justified.
Very much agree, and I find the whole hatred of generative AI is largely misguided to begin with. It's interesting technology that has useful applications. Most of the problems associated with it ultimately trace back to capitalism, as opposed to any inherent problem with LLMs themselves.