This seems impossible to me. Many of the tasks I use GPT for inherently require understanding and thinking.
The comparison to the underpinnings of human cognition are not really valid, because regardless of the underlying substrate, human beings are indeed building mental models of reality itself via sensory input, so even if human minds and LLMs operated on the exact same process of inference (which we cannot state with confidence is true), then human minds would be generating inferences based on correlations of actual empirical experience, whereas LLMs are only building correlations between words, regardless of what empirical reality those words may or may not correspond to.
So all else being equal, human minds are modeling reality, while LLMs are modeling another model.
This is true by definition, since a 'hallucination' isn't a failure condition in which the system isn't working as designed, it's just a post-hoc term for when probabilistic output doesn't meet our expectations, which is inherent to the nature of all probabilistic systems.
Within the system, there is no distinction between 'hallucination' and 'non-hallucination'.LLMs are applying the same stochastic process in all cases, and the criteria of whether we call its output a 'hallucination' is entirely external to their functioning.
Strictly speaking, LLMs are always hallucinating, since they are always generating inferences based on hard-coded statistical models and have no awareness of the semantic meaning of the tokens they correlate, nor any internal criteria of external correctness.
> I think people just have to learn that LLMs are not divine oracles that are always correct.
We all need to keep repeating the mantra "all models are wrong; some models are useful."