Like many people I've been arguing about the nature of LLMs a lot over the last few years. There is a particular set of arguments that I found myself having to recreate from scratch over and over again in different contexts, so finally put it together in a larger post, and this is that post.
The crux of it is that I think both the maximalist and minimalist claims about what LLMs can do/are doing are simultaneously true, and not in conflict with one another. A mind made out of text can vary along two axes, the quantity of text it has absorbed, which here I call "coverage," and the degree to which that text has been unified into a coherent model, which here I call "integration." As extreme points on that spectrum, a search engine is high coverage, low integration, and an individual person is low coverage, high integration, and LLMs are intermediate between the two. And most importantly, every point on that spectrum is useful for different kinds of tasks.
I'm hoping this will be a more useful way of thinking about LLMs than the ways people have typically talked about them so far.
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Notes -
I don't think anyone trained for uncertainty as such, it seemed that a sense of internal calibration was an emergent phenomenon in the base LLM, which was mauled by RLHF.
So as long as you don't do the latter, training for the above simply involves training as usual.
Right, I guess I'm saying if you wanted to train a specific response to a level of uncertainty, it would be difficult to construct the training samples.
Evidently, the model has figured out that something should be hooked up to its uncertainty. But I have no clue how you'd make that happen intentionally.
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