Tech experts are starting to doubt that ChatGPT and A.I. 'hallucinations' will ever go away: 'This isn’t fixable'
fortune.com
Even OpenAI CEO Sam Altman was skeptical a few weeks ago: "I probably trust the answers that come out of ChatGPT the least of anybody on Earth."

Tech experts are starting to doubt that ChatGPT and A.I. ‘hallucinations’ will ever go away: ‘This isn’t fixable’::Experts are starting to doubt it, and even OpenAI CEO Sam Altman is a bit stumped.

  • @[email protected]English
    42•2 years ago

    That’s called context. For chatgpt it is a bit less than 4k words. Using api it goes up to a bit less of 32k. Alternative models goes up to a bit less than 64k.

    Model wouldn’t know anything you said before that

    That is one of the biggest limitations of current generation of LLMs.

    • @[email protected]English
      3•2 years ago

      Thats not 100% true. they also work by modifying meanings of words based on context and then those modified meanings propagate indefinitely forwards. But yes, direct context is limited so things outside it arent directly used.

      • @[email protected]English
        1•2 years ago

        They don’t really chance the meaning of the words, they just look for the “best” words given the recent context, by taking into account the different possible meanings of the words

        • @[email protected]English
          1•2 years ago

          No they do, thats one of the key innovations of LLMs the attention and feed forward steps where they propagate information from related words into each other based on context. from https://www.understandingai.org/p/large-language-models-explained-with?r=cfv1p

          For example, in the previous section we showed a hypothetical transformer figuring out that in the partial sentence “John wants his bank to cash the,” his refers to John. Here’s what that might look like under the hood. The query vector for his might effectively say “I’m seeking: a noun describing a male person.” The key vector for John might effectively say “I am: a noun describing a male person.” The network would detect that these two vectors match and move information about the vector for John into the vector for his.

          • @[email protected]English
            1•2 years ago

            That’s exactly what I said

            They don’t really chance the meaning of the words, they just look for the “best” words given the recent context, by taking into account the different possible meanings of the words

            The word’s meanings haven’t changed, but the model can choose based on the context accounting for the different meanings of words

            • @[email protected]English
              1•2 years ago

              The key vector for John might effectively say “I am: a noun describing a male person.” The network would detect that these two vectors match and move information about the vector for John into the vector for his.

              This is the bit you are missing, the attention network actively changes the token vectors depending on context, this is transferring new information into the meanings of that word.

              • @[email protected]English
                1•2 years ago

                The network doesn’t detect matches, but the model definitely works on similarities. Words are mapped in a hyperspace, with the idea that that space can mathematically retain conceptual similarity as spatial representation.

                Words are transformed in a mathematical representation that is able (or at least tries) to retain semantic information of words.

                But different meanings of the different words belongs to the words themselves and are defined by the language, model cannot modify them