You misunderstand me -- my position is that we are completely ignorant as to how LLMs do what they do, and it is delusional to think that we understand what their capabilities are or how they are limited.
Until we understand why they sometimes don't hallucinate we can't begin to approach the problem of why the do. And above I directly refute the notion that no model "knows what it doesn't know" -- they are manifestly capable of demonstrating ignorance.
As to next steps in this I have no concrete thoughts, and I don't even know whether LLMs are a dead end. What I suspect is that as we apply more computational power to the problem, as we've seen in the past, computational power plus network depth plus training data means that the structure of the models (like the attention mechanism or the tokenization) will eventually be unnecessary and the structures will be self-discovered and more capable for it.
As models get deeper and more expressive I am confident that things like a broader capability to understand the limitations of its own knowledge or to test its knowledge against objective reality will emerge organically. We may be able to make leaps by creating shortcuts for some of these mechanisms, but in the end depth and computation power will trump all.
> my position is that we are completely ignorant as to how LLMs do what they do
That's simply wrong.
Whole teams of very smart people at places such as Anthropic are working on this exact problem (the field of "mechanistic interpretability"), and have made considerable headway, and have published their results.
Until we understand why they sometimes don't hallucinate we can't begin to approach the problem of why the do. And above I directly refute the notion that no model "knows what it doesn't know" -- they are manifestly capable of demonstrating ignorance.
As to next steps in this I have no concrete thoughts, and I don't even know whether LLMs are a dead end. What I suspect is that as we apply more computational power to the problem, as we've seen in the past, computational power plus network depth plus training data means that the structure of the models (like the attention mechanism or the tokenization) will eventually be unnecessary and the structures will be self-discovered and more capable for it.
As models get deeper and more expressive I am confident that things like a broader capability to understand the limitations of its own knowledge or to test its knowledge against objective reality will emerge organically. We may be able to make leaps by creating shortcuts for some of these mechanisms, but in the end depth and computation power will trump all.