3 ms·

They can say they don't know things already. How are you defining "know" here?

• nyrikki 3y ago
  Ah the fun of known knowns, known unknowns, unknown unknowns, and weak negation. https://blog.research.google/2024/01/can-large-language-models-identify-and.html https://blog.research.google/2024/01/can-large-language-mode... Negation in the training data, exhaustion, or unknowable unknowns like future events is about an LLM will ever be able to say it "doesn't know" reliably. LLMs have no concept of truth or correctness really, they are just probabilistic appending to output to match learned ideal patterns.
  • humansareok1 3y ago
    >LLMs have no concept of truth or correctness really, they are just probabilistic appending to output to match learned ideal patterns. I think they do have those concepts, like they have many others but they are not bound to them in any way.
• fhd2 3y ago
  They're trained (and possibly scripted) to not "know" things or not be able to do things. But at their core, they're going to generate the most plausible continuation, even if that isn't very plausible (like non-existent functions). I think this can be improved (playing with the temperature in self hosted models depending on the nature of the task e.g.), but it doesn't seem fully solvable with current LLMs.