Comment by Lerc

That kind of exchange is something I have seen from ChatGPT and I think it represents a specific kind of failure case.

It is almost like Schizophrenic behaviour as if a premise is mistakenly hardwired in the brain as being true, all other reasoning adapts a view of the world to support that false premise.

In the instance if ChatGPT the problem seems to be not with the LLM architecture itself but and artifact of the rapid growth and change that has occurred in the interface. They trained the model to be able to read web pages and use the responses, but then placed it in an environment where, for whatever reason, it didn't actually fetch those pages. I can see that happening because of faults, or simply changes in infrastructure, protocols, or policy which placed the LLM in an environment different from the one it expected. If it was trained handling web requests that succeeded, it might not have been able to deal with failures of requests. Similar to the situation with the schizophrenic, it has a false premise. It presumes success and responds as if there were a success.

I haven't seen this behaviour so much in other platforms, A little bit in Claude with regard to unreleased features that it can perceive via interface but has not been trained to support or told about. It doesn't assume success on failure but it does sometimes invent what the features are based upon the names of reflected properties.