To answer your question, think about how we train LLMs: We have them learn the statistical distribution of all written human language, such that given a chunk of text (a prompt, etc.) it then samples its output distribution to produces the next most likely token (word, sub-word, etc.) that should be produced and keeps doing that. It never learns how to judge what is true or false and during training it never needs to learn "Do I already know this?" It is just spoon fed information that it has to memorize and has no ability to acquire metacognition, which is something that it would need to be trained to attain. As humans, we know what we don't know (to an extent) and can identify when we already know something or don't already know something, such that we can say "I don't know." During training, an LLM is never taught to do this sort of introspection, so it never will know what it doesn't know.
I have a bunch of ideas about how to address this with a new architecture and a lifelong learning training paradigm, but it has been hard to execute. I'm an AI professor, but really pushing the envelope in that direction requires I think a small team (10-20) of strong AI scientists and engineers working collaboratively and significant computational resources. It just can't be done efficiently in academia where we have PhD student trainees who all need to be first author and work largely in isolation. By the time AI PhD students get good, they graduate.
I've been trying to find the time to focus on getting a start-up going focused on this. With Terry Sejnowski, I pitched my ideas to a group affiliated with Schmidt Sciences that funds science non-profits at around $20M per year for 5 years. They claimed to love my ideas, but didn't go for it....