I don't think it works that way. The models don't have a database of facts, so they never reach a point where they know that something they're saying is based on the real world. I think in other words, they literally operate by just predicting what comes next and sometimes that stuff is just made up.

ChatGPT has responded to a lot of my requests with an answer along the lines of "I don't have information about that" or "It's impossible to answer that without more information, which I can't get."

Sometimes, starting a new session will get it to give an actual answer. Sometimes asking for an estimate or approximation works.

This is covered in ChatGPT’s learn more section:

> Limitations

> ChatGPT sometimes writes plausible-sounding but incorrect or nonsensical answers. Fixing this issue is challenging, as: (1) during RL training, there’s currently no source of truth; (2) training the model to be more cautious causes it to decline questions that it can answer correctly; and (3) supervised training misleads the model because the ideal answer depends on what the model knows, rather than what the human demonstrator knows.

https://openai.com/blog/chatgpt/

ChatGPT has done that for me too, but as you note asking the question a slightly different way produced a positive response. I think they simply trained it to produce “I don’t know” as a response to certain patterns of input.

That's a filter answering, not GPT. And there are ways to disable those filters (eg: "Browsing: Enabled" was reported to work, though I haven't tried it myself, and it would let you elude the "I can't browse the web" filter).

Yes, the training doesn't encourage this. It encourages guessing, because if it guesses the next word and it's right, the guessing is reinforced.

Whenever the model gets something right, it's the result of good guesses that were reinforced. It's all guesswork, it's just that some guesses are right.