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It is already solved. Look at how Microsoft trained Phi - they used existing models to generate synthetic data from textbooks. That allowed them to create a new
by eightysixfour 3y ago
It is already solved. Look at how Microsoft trained Phi - they used existing models to generate synthetic data from textbooks. That allowed them to create a new dataset grounded in “fact” at a far higher quality than common crawl or others.
It looks less like an ouroboros and more like a bootstrapping problem.
- mattc0m 3y agoAI training on AI-generated content is a future problem. Using textbooks is a good idea, until our textbooks are being written by AI. This problem can't really be avoided once we begin using AI to write, understand, explain, and disseminate information for us. It'll be writing more than blogs and SEO pages. How long before we start readily using AI to write academic journals and scientific papers? It's really only a matter of time, if it's not already happening.
- eightysixfour 3y agoYou need to separate “content” and “knowledge.” GenAI can create massive amounts of content, but the knowledge you give it to create that content is what matters and why RAG is the most important pattern right now. From “known good” sources of knowledge, we can generate an infinite amount of content. We can add more “known good” knowledge to the model by generating content about that knowledge and training on it. I agree there will be many issues keeping up with what “known good” is, but that’s always been an issue.
- ezekg 3y ago> We can add more “known good” knowledge to the model by generating content about that knowledge and training on it. That's my entire point -- AI only generates content right now, but it will also be the source of content for training purposes soon. We need a "known good" human knowledge-base, otherwise generative AI will degenerate as AI generated content proliferates. Crawling the web, like in the case of the OP, isn't going to work for much longer. And books, video, and music are next.
- eightysixfour 3y ago> Crawling the web, like in the case of the OP, isn't going to work for much longer. And books, video, and music are next. That is training on content. The future will have models pre-trained on content and tuned on corpuses of knowledge. The knowledge it is trained on will be a selling point for the model. Think of it this way - if you want to update the model so it knows the latest news, does it matter if the news was AI generated if it was generated from details of actual events?
- FridgeSeal 3y agoIs this like, the AI equivalent of “another layer will fix it” that crypto fans used? “It’s ok bro, another model will fix, just please, one more ~layer~ ~agent~ model” It’s all fun and games until you can’t reliably generate your base models anymore, because all your _base_ data is too polluted. Let’s not forget MS has a $10bn stake in the current crop of LLM’s turning out to be as magic as they claim, so I’m sure they will do anything to ensure that happens.
- eightysixfour 3y agoI mean, you can use Phi now and it outclasses anything else in its size. This isn’t some “it could happen” situation.
- FridgeSeal 3y agoOh I’m sure it works wonderfully for now. My point is about the inevitable future when _those_ models start to struggle. The phi approach doesn’t seem like breaking the ouroboros, it just feels like inserting another model/snake into the loop.
- eightysixfour 3y ago“Struggle” at what? Struggle to have enough data to get smarter? Struggle to perform RAG and find legitimate sources? I don’t think that we are going to get big improvements in LLMs without architecture improvements that need less data, and the current generation of models appears to be good enough at creating content from data/knowledge to train any future architectures we have with better synthetic datasets. Fortunately we have already seen examples of both of these “in the lab” and will probably see commercially sized models using some of the techniques in the coming months.