> the web doesn't provide it in sufficient quantity and quality
Do you seriously think that "an AI-generated encyclopedia" would provide a better-quality training set? What would the "AI generator's" articles be derived from?
The idea is that you can standardise the quality of the training data by taking source articles and synthesizing new data with the same "voice" and structure, as well as being able to collate insights from multiple sources.
This is the line of thinking behind the Phi lineup of models [0], as well as efforts to generate synthetic textbooks for training [1].
So the way I see it, in the first stage the model can take all concepts in Wikipedia and other knowledge bases, and do web search, collect a bunch of references, study and compile a report. That's straight forward search + summarization. The advantage would be that models get to bring together information sitting in separate examples and synthesize or draw conclusions.
The second stage would be to generate research questions, then solve them with LLM+web search+code execution+other tools. The results would be compiled in reports. So it's a loop of problem generation, problem solving and validation. You can validate with highly trusted sources, or you can run code or simulations, ensemble multiple attempts, or even leave it to ranking by a preference model.
This is the line of thinking behind the Phi lineup of models [0], as well as efforts to generate synthetic textbooks for training [1].
[0]: https://arxiv.org/abs/2309.05463
[1]: https://twitter.com/ocolegro/status/1712327588255809667