"Interestingly, the base pre-trained [GPT-4] model is highly calibrated (its predicted confidence in an answer generally matches the probability of being correct). However, through our current post-training process, the calibration is reduced."[1] The graph is striking.[2]

[1] https://openai.com/research/gpt-4

[2] https://i.imgur.com/cxPgkhD.jpg

The key piece will be when it queries multiple services by default and compares the answers to its own inferences, and is prompted to trust majority opinion or report that there isn't consensus. The iterative question about moons larger than Mercury in the Wolfram Alpha thread is a simple example of iterative tool use.

The fact that the model does not have to rely on its internal knowledge anymore but can communicate literally with any external system makes me feel it may significantly reduce the hallucination.

A plug-in can detect when text comes up that is in a specific domain and whether or not ChatGPT believes it is hallucinating, the plugin can be invoked to provide additional context to ChatGPT. That is, in order to fix the problem, ChatGPT doesn't even need to know that it has a problem.