Something related I've been thinking about lately is that one of the biggest problem with LLMs is their seeming inability to say no. Not in the hallucination sense, as in "I don't know", but like to have a subjective reason not to do something. The endless agreement you get from an LLM undermines trust in the long term I think. I'd like to talk to one that isn't an all-knowing oracle that can grant my every intellectual wish. (Or maybe what I'm asking for is just... a human, lol).

Interesting topic. That said I don't know how useful this is since LLMs are primarily trained using mode-covering training rather than Mode-seeking(RL) training, which means LLMs can not form (and does not have) the same underlying structure to their models of language that humans have.

A LLM does not learn topic by topic, it learns everything all at once and slowly integrates it in to a single knowledge system.

I prefer my 8yo's answer about quantum entanglement, asked just now: "I don't know. How would I know? It's not a thing!"

Even an 8yo has better metacognition, it seems. :-)

> why is the sky blue?

> The sky is blue because of something called Rayleigh scattering. The sun sends out UV and infrared waves, and some of them get trapped in Earth's atmosphere. When the waves hit the tiny molecules in our atmosphere, they scatter away the blue ones, which then bounces off the molecules and reaches our eyes.

"filtered to the U.S. elementary-school curriculum", suuure

A similar project (LLM trained only on vintage material): https://talkie-lm.com/introducing-talkie

Really cool work. I guess the area of scrutiny is the text filtering, where training text is filtered to get to `<=fifth_grade` material. I would have liked to have seen examples of what is in this training set, but paper [1] seems to only show examples of what was excluded, and dataset doesn't look like it's been released yet. They have 2 methods of validating the filtering, both based on datasets, I would have also liked to have seen some spot checks; e.g. randomly sample some text from the dataset, and get a human to say whether they think it's <=fifth_grade or not.

(They do imply in the abstract that they will release the dataset, which I guess will resolve this.)

[1] https://arxiv.org/abs/2608.13545

Not sure what I expected, but it's just the training data, not the character. It'd be so cool if such systems had natural curiosity at this checkpoint. Eg:

> Me: "What's semiotic crystallography? > Response: "I don't know, what is it?"

Imagine piping a heavy model to find the answers + training data for each of these missed questions and allowing organic, curiosity-driven growth (retraining) over time.

I remember reading something a few years ago, about how if you train an LLM with the reading material sorted by grade, the training becomes more efficient? Does anyone know about this technique? How does that work?

I'm assuming the knowledge doesn't end up as separate "layers".

I'm also reminded of how the human mind develops in distinct stages (e.g. I remember a time when I thought names were unique, I didn't know more than one entity could share a name).

It’s not quite like a real fifth grader, I guess - more like a fifth grade genius that has read and understood everything in every syllabus.

> What is Schrödinger's cat?

> It's a cat that has been misbehavin'!

Not quite, because it knows about quantum entanglement and that’s a little beyond the fifth grade.

Eternal youth?

i dont know