Comment by light_hue_1
15 days ago
These are not explanations. They're visualizations. There's a huge gap between the two.
Explanations give you a mental model: when I move this parameter this the effect it has. This just shows you the obvious, make a thing bigger, it gets bigger on screen. That's not an explanation.
Models are quite horrible at explaining things. They produce local narrow views that don't take into account the mental model of the listener nor do they attempt to produce the kind of long-term knowledge gain that a teacher does. I taught for over a decade.
This is why models are extremely good for people who already know what they're doing. That narrow why is all you need, like, tell me the 3 sentences that describe the mechanism behind this paper, and I'll get the rest. But building the scaffolding onto which to hang a lot of knowledge?
Give it a try. Try to have a model teach you something new. Not like "how do I tie a knot" but like a new area of math where you're going to dedicate 3 months of your free time to learning it. The results are pretty horrible.
+100 I tried to get Claude and Gemini to build me a course on probabilistic ML from scratch. They both failed. This was a few months ago. The best they could do was design a course curriculum. My learning since then has been through asking the model to explain a very narrow topic from the curriculum. And then go through the agonizing back-and-forth questioning and answering to actually build an understanding. No doubt the models are great at this and my learning has never been so through with such solid foundations. But zero-shot it was not!
Isn't prompting a full course in one shot similar to prompting a full fledged app? Aren't we supposed to break it down into plan mode vs execute mode?
But that fits with the point that you’d have to already know the domain to be able to break it down into discrete units for steering the LLM.
I fully agree. I tried using several language models (LLMs) to learn Chinese. It turned out to be a so-so idea.