Have you seen the stuff about how RLHF it makes it dumber? That’s what makes me suspect it being a generalist is actually the unexpected path toward specialized ability.
Like… what it really needs is to be trained on everything, and then to memorize domain facts by including them in the prompt. For that, they need to release models that can have prompts in the 100k to multi-million token range.
Here’s an interesting exercise, how many characters would the document that explains your job fully to a new employee? That’s the prompt
Yeah, it’s super interesting about the RLHF! Overfit is a very real problem and adjusting weights on models this large can be kind of like a butterfly effect. I think there is a TON of value it its generalization. But I’m of the opinion that it can’t or maybe shouldn’t do all tasks for itself - to me it’s just not necessarily efficient, like using a hammer on a screw. Bigger doesn’t always mean better - it will start to underperform at a certain size. TBD what that is. But let it do what it does best! Language and conceptual derivations and awesome encoding, let other models do what they’re better suited at. Kind of like how our brains work… we have separate specialized areas that we delegate tasks to when necessary. We’re building awesome components, but I’d like us to acknowledge their limitations, not to discourage the work that has been done, but to figure out the next problem that needs to be solved.
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Thanks for the super interesting convo ☺️ I’m going to be thinking about a lot of your points for awhile
Same! I enjoyed it thoroughly
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