• booty [he/him]@hexbear.net
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    3 months ago

    I don’t see how it could be measured except from looking at inputs&outputs.

    Okay, then consider that when you input something into an LLM and regenerate the responses a few times, it can come up with outputs of completely opposite (and equally incorrect) meaning, proving that it does not have any functional understanding of anything and instead simply outputs random noise that sometimes looks similar to what one would output if they did understand the content in question.

    • frightful_hobgoblin@lemmy.ml
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      3 months ago

      Right. Like if I were talking to someone in total delirium and their responses were random and not a good fit for the question.

      LLMs are not like that.

        • frightful_hobgoblin@lemmy.ml
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          3 months ago

          when you input something into an LLM and regenerate the responses a few times, it can come up with outputs of completely opposite (and equally incorrect) meaning

          Can you paste an example of this error?

          • booty [he/him]@hexbear.net
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            3 months ago

            Have you ever used an LLM?

            Here’s a screenshot I took after spending literally 10 minutes with chatgpt very confidently stating incorrect answers to a simple question over and over. (from this thread) Not only is it completely incapable of coming up with a very simple correct answer to a very simple question, it is completely incapable of responding in a coherent way to the fact that none of its answers are correct. Humans don’t behave this way. Nothing that understands what is being said would respond this way. It responds this way because it has no understanding of the meaning of anything that is being said. It is responding based on statistical likelihoods of words and phrases following one another, like a markov chain but slightly more advanced.