If this is true, why is GPT-4 better in that regard than GPT-3.5? Or why do questions about Python yield much less hallucinations than questions about Rust, or other less popular tech?
You've claimed that LLMs create most probable output, which does not necessarily align with truth. So a bigger LLM will be better at creating the most probable output, but that would not translate into being more truthful. That could be interpreted as "better LLMs are expected to be better bullshitters".
That is not what we've observed though. Quite the opposite - we're seeing that the bigger LLM is and the more domain-specific material it digested, the more truthful it becomes.
Yes it can still make an error and be unable to spot it, but so can I.
> You've claimed that LLMs create most probable output, which does not necessarily align with truth.
No, that is not my claim. That is part of the explanation for it.
My claim is this: An LLM is incapable of knowing when it produces false information, as it simply doesn't have a concept of "truthfulness". It deals in probabilities, not alignment with objective reality.
And it doesn't matter how big you make them...this fact cannot change, as it is rooted in the basic MO of language models.
So, now that we have covered what my claim actually is...
> That is not what we've observed though. Quite the opposite - we're seeing that the bigger LLM is and the more domain-specific material it digested, the more truthful it becomes.
...I can ask what this observation has to do with it, and the answer is: Nothing at all. LMs with more params may produce untruthful statements less often, but what does this change about their ability to recignize when they do produce them? And the answer is: Nothing. They still can't.
