[3cats-in-a-coat]

We already had databases of facts, like Wolfram Alpha, decades before LLM, and we largely ignored them. It's ironic that when trying to solve AI problems we keep reverting to these old patterns we've tried since the 80s and they kept failing. Habits die hard, I guess.

There's a categorical difference between knowing a fact, and looking up a fact. When you know a fact you can recognize it in a situation where you wouldn't know to look it up, and you'd know to utilize it in a larger solution rather than simply parrot it when specifically asked about it.

Databases of facts have and will still have their place, but that is absolutely not the solution to LLM telling apart fact from truth. They have to innately have this in their model. I don't believe the nature of LLM is to hallucinate. It's instead a side effect of how we train them. We train them to guess, to be close, but not to be correct necessarily. And why is it a surprise that's precisely what they do?

Also LLM are too small in order to be accurate. They're tiny. GPT4 is roughly 40 times smaller than a human brain. And GPT4 is very large compared to GPT-3, and GPT-3 is very large compared to LLaMA 2.

We'll need for hardware to catch up so we can scale things up pragmatically and see what happens to their ability to grasp facts. But also architectural changes, of course.

[kfrzcode]

Not to mention our wetbrain software is analog, resonant with the environment, continuous and has a single uptime, in most cases. We should consider developing llms in proto human history style, random noise meets environs, evolve useful signs and symbols based on clusters of semantic embedding. Uno reverse it through a dynamic parallel narrative simulator circuit with range of values for interpretative feedback of context analysis. Assign allomorphic symbols to conceptual clusters. Refine resolution. Add modules for memory and inputs for updating knowledge, shine it up with some polish and you've got AGI

[mjburgess]

> I don't believe the nature of LLM is to hallucinate. It's instead a side effect of how we train them. We train them to guess, to be close, but not to be correct necessarily.

Thoughout this comment you speak about LLMs as-if they're animals, or real physical objects. An LLM is a formal model which is just to generate a sequence of tokens maximally probabilistically consistent with a corpus of historical text.

A digital machine running a LLM program is a physical object which necessarily generates text based on "guessing" because that's the algorithm it's running. LLMs are "guessing algorithms", all of Machine Learning is -- it is dumb brute-force analysis of conditional probability.

> GPT4 is roughly 40 times smaller than a human brain

This doesn't make any sense. GPT4 is an abstract algorithm with no "size". The brain has 10^{big number} cells, and GPT4 can be specified with a single real number. Is that the comparison to make? No, both comparisons are incoherent.

A physical device running GPT4 can be given a "size", but it would again have nothing to do with a brain.

LLMs arent living things where we can "measure their size" and "train them to know, rather than to guess". They are just the equation, `max P(answer|propmt, historical_corpus)`

A machine running GPT4 is just an electrical device generating text according to the rule given above. There is no sense of "training it to do something other than guesswork", and no sense of "size"

[prometheus76]

Larger models are not always more accurate. Overbuilding a model often leads to "overfitting" the dataset. A good example: the iphone text prediction model. It now has so much data that the suggested completed words are often useless and irrelevant in context.