3 ms·

That last part reminds me of this: https://en.m.wikipedia.org/wiki/Low-background_steel https://en.m.wikipedia.org/wiki/Low-background_steel tl;dr steel (exce

• visarga 4y ago
  That makes sense only if LLMs will remain sub-human level for a very long time. But probably in 2-3 years generated content will be better than 99% of the human content. The RLHF reward model is effectively a good/bad content filter. Human==good AI==bad doesn't always hold, there is good AI generated content and bad human written content out there, we should filter by content quality and novelty, not origin. If I had the resources I would turn GPT-4 onto its own dataset to mine all the facts from all the source documents. Then flip the index grouping by fact, and for each fact it should write a page listing the supporting and contradicting evidence. This would be super useful in determining - 1. existence of a fact, 2. its controversiality level and 3. the distribution of answers. With these pieces of information at hand GPT would get much better, it would at least stop hallucinating. This fact index would probably double the size of the training corpus as well. Google, with its years of search logs, is the best positioned to create this fact index. They already had simpler attempts at creating a knowledge graph for a long time. But if they don't move to exploit the data they sit on, others will take the lead anyway. It costs only money to run so much text over the model, no slow & expensive human involvement necessary.