I think, while i agree to "problem with LLMs is just with training" i also think to a certain degree we need to step back from LLM's as in text processors and to achieve "AI" as in something really intelligent we need to go more abstract back to NN and build a self learning "entity". While LLM's accomplish fascinating results, we are trying to force speech as the primary way of learning, tho this is a really limiting factor. If we would accomplish to create an NN driven AI in a virtual space which would have an simulated environment and learn from a base state like a "newborn" it still could accomplish the skills to understand language as we humans prefer to use it, tho it wouldn't be limited in "thinking" in and only based on this.

I know this is a very simple and abstract way to explain it but i think you get my point.

Towards the simulated AI learning environment, theres this interview with Jensen Huang that i can recommend in which he touches on the topic and how nvidia is working on such https://www.youtube.com/watch?v=7ARBJQn6QkM

While im not a "expert" in this topic, i might have spend quite a portion of the past 10 years in my freetime to think about it and tinker, and ill stick with the point - we need a free self-trained system to actually call it AI, and while LLM's as GPT's nowadays are powerfull tools, for me those are not "Artificial Intelligence" (intelligence from my pov must include reasoning, understanding of its own action, pro-active acting, self-awareness). And even tho the LLM's we use can "answer" to certain questions as if they would have any of those, its just pre-trained answers and they dont bring any of those (we work on reasoning but lets be fair its not that great yet).

Just my two cents.