4 ms·
Well, if we’re using the output of AIs, writing blog post articles with it and using AI to post comments on Reddit/X etc (as some do), and a few years later Ope
by m11a 2y ago
Well, if we’re using the output of AIs, writing blog post articles with it and using AI to post comments on Reddit/X etc (as some do), and a few years later OpenAI et al refresh their datasets to train a new model, then you’re doing exactly that aren’t you? Using lossy model outputs to put into the function again, that is
- dale_glass 2y agoIt's harder with LLMs, but we still have metrics for a lot of text. On Reddit/HN/etc we have comment scores, replies, reposts, external references, etc that we can use to estimate whether a given comment/post was any good. An entity like Google that indexes the whole web has visibility into when a given piece of content showed up, if it changed, if it got referenced elsewhere later, etc. LLMs can be used to analyze the page and work out things like "the comments answering this comment are saying it's wrong" We can also of course pick and choose, Reddit has some completely garbage subreddits and very tightly moderated ones. It's of course by no means foolproof, but it doesn't have to be. It just has to be good enough to be usable for whatever purpose we need. Also, perfection isn't a thing and such issues happen even without LLMs. Like all the cases of something wrong being added to Wikipedia, getting repeated on some news site, and then Wikipedia using that as a reference to backup the incorrect claim.