This is not obvious to me at all. LLMs are capable of amazing feats of reverse engineering but are terrible at explaining and abstracting things well.
I think this is an artefact of how we train them, and they could be trained to be better at this if we focused on it, but at the same time, it shows its possible for these things to be able to do things they cant explain to us (granted, they can't explain them well - you can bash your way to understanding with them anyway but they aren't good at it)
It's amazing to me that language models are still such poor writers and communicators. The mathematics community wrestled with this recently, when many solutions dropped into their laps but were not explained very well...
Thing is, (1) generic LLMs will improve, and (2) their lack of ability in this area has a lot to do with the rhetorical and structural habits they pick up during RLHF/instruction-tuning, which trained them for general-purpose comms. Zero-training base models can presumably be trained specifically to communicate scientific concepts. Interesting times.
(Edited to add: With respect to (1), compare Opus 5 to 5.5. 5 was a terrible communicator, even of software/coding concepts, whereas 5.5 is excellent and leaves very little to be desired.)
Many people don't like the style, probably by association with slop and fatigue more than the style being fundamentally wrong.
Being a good communicator is an entirely different thing, you can have terrible spelling and grammar and still be good at getting ideas across. It is a common situation when the communicator in question is not using his native language.
That LLMs are so good at writing and so bad at communicating is not surprizing. The key to good communication is getting into the other people's mind, LLMs can't do that, because they "think" in a way that is completely alien to us. We use words to express our thoughts, thoughts that are based on a physical world model: touch, movement, sound, light, etc... LLMs have no idea about how the physical world work, everything the see is just words, they have no intuitive idea of, say, objects falling down, just that "falling" and "down" go well together. And for explaining things, they will go "someone asks for an explanation, explanations usually use these words, I will be using the same kind of words".
Hm, that may have to do with (faking) confidence. I've often enough with 5.5 still run into cases where it confidently explained something, at the surface it kinda made sense, but the more I dug and verified, the more it became apparent that it was entirely wrong with the most crucial core concepts. It has gotten to a point where you really need a domain expert though to realize this, which makes it tough for using it in areas where you are not an expert and don't have easy access to one either.
Verification isn't knowledge. Far from it. You can verify statements without knowing why they're true. Variants of that concept are e.g. used in cryptology.
You need humans to truly understand (to know the context) in order to make correct judgements. Without actually knowing the context, you have no way to compare.
The same goes for our own time. A powerful AI can tell us things at the edge of what we can grasp -- or beyond what we can grasp -- and it would nevertheless be knowledge that we possess. We can work to verify it, we can have it explained to us as though we were schoolchildren, or we can simply accept it as a static fact of the natural world.
In truth, we're going to have to learn to coexist with minds that are more powerful than ours. At least in some respects, if not in all respects. The good scenario is that they share information with us, and we receive it however we can. "Head in the sand" and "mutual disdain" are bad outcomes.
As far as I know labs try to train the other way, else we'd have monstrously long and useless outputs, but its hard to overcome these natural biases for length
Like a superposition of all kinds of writing styles, all perspectives on every topic and all kinds of ways to do things right and wrong.
In post-training (and of course already by sensibly curating the input data in the first place), people try to select useful submanifolds from that.
Ideally, you get a combination of all the extant strengths of people combined into one model. Self-improvement then leaves the sphere of human capability for good.
And the abstractions do still matter, since in many cases it is an issue of tractability. Perhaps an LLM can force its way to a solution through 3 million mind numbingly boring and convoluted steps, but if the number of steps increases exponentially without the right abstraction that means it can only handle a slightly larger problem than a human with pen and paper.
> https://en.wikipedia.org/wiki/Hierarchy_of_the_sciences
First math and CS, where formal logic dominate, and AI can check/verify its own answers very quickly. Then physics, downstream of math. Then materials science, which in some cases is practically a branch of physics. Then chemistry more generally. Then biology. Then various social sciences.
Math is really super suitable for quick iterations by a machine.