LLM often say "I don't know" when it do know, or say A but do B and so on. This is due to lack of introspection, it doesn't examine its own thoughts like a human do.<p>What I am discussing here is live introspection in production in order to guide its answers.<p>Example how human and LLM are different here:<p>* A human is asked a question, he has several thoughts in his head, he could answer I don't know, he also has a memory of the answer. He looks at those two, realize "I don't know" is wrong since he do know, and answer with the right answer.<p>* An LLM is asked a question, it too has the same thoughts, it can answer "I don't know", but it also remembers the right answer just like the human. The LLM now rolls a dice and picks what it will respond with.<p>This shows clearly how introspection is needed to solve these kinds of questions. The only way an LLM can stop doing these dumb mistakes is with introspection, since as long as there is any chance at all for the LLM to pick the wrong answer it will sometimes do things it know is wrong.<p>And if we had introspection for LLM, then they would no longer be black boxes since we can just ask them about themselves and they would explain their own thoughts and how they work. I also think that introspection is what would enable smart learning, like humans do with very little data, which is much more efficient than statistical learning for complex tasks.<p>So I was wondering if there are any examples where people have made models like this? Doesn't have to be an LLM, just having a model be self aware. Or if there is any research into trying to add such a functionality to models.