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What you just wrote makes more sense applied to LLM output than podcasts! You'd just as easily argue that "radio" or "news" is all bad if you don't want to diff
by Martinussen 3y ago
What you just wrote makes more sense applied to LLM output than podcasts! You'd just as easily argue that "radio" or "news" is all bad if you don't want to differentiate between different forms of expression and communication within a medium. (Which, obviously, would be silly)
- WendyTheWillow 3y agoSorry what? Nothing I wrote apples to LLMs; they are not optimized for popularity, they’ve been meticulously designed and built to be as accurate as possible.
- thfuran 3y agoThey absolutely have not been meticulously designed to be as factually accurate as possible.
- WendyTheWillow 3y agohttps://platform.openai.com/docs/guides/fine-tuning https://platform.openai.com/docs/guides/fine-tuning Yes, they have.
- thfuran 3y agoThat link doesn't say anything about the fundamental design goals of the network architecture or training process. It doesn't even mention factual correctness, except in the sense that it may broadly fall under "producing a desired output".
- croon 3y agoNo they're not. If they were, they would default to 0 temperature and have no Top P, frequency/presence penalty, and frankly not have knowledge as a function of language to begin with. They're designed to be convincing as a "presence" and output reasonable sounding language in context, with accuracy as an afterthought.