• hoshikarakitaridia@lemmy.world
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    13 hours ago

    Because in a lot of applications you can bypass hallucinations.

    • getting sources for something
    • as a jump off point for a topic
    • to get a second opinion
    • to help argue for r against your position on a topic
    • get information in a specific format

    In all these applications you can bypass hallucinations because either it’s task is non-factual, or it’s verifiable while promoting, or because you will be able to verify in any of the superseding tasks.

    Just because it makes shit up sometimes doesn’t mean it’s useless. Like an idiot friend, you can still ask it for opinions or something and it will definitely start you off somewhere helpful.

    • WalnutLum@lemmy.ml
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      9 hours ago

      All LLMs are text completion engines, no matter what fancy bells they tack on.

      If your task is some kind of text completion or repetition of text provided in the prompt context LLMs perform wonderfully.

      For everything else you are wading through territory you could probably do easier using other methods.

      • burgersc12@mander.xyz
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        4 minutes ago

        I love the people who are like “I tried to replace Wolfram Alpha with ChatGPT why is none of the math right?” And blame ChatGPT when the problem is all they really needed was a fucking calculator

    • ms.lane@lemmy.world
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      12 hours ago

      Also just searching the web in general.

      Google is useless for searching the web today.

      • fibojoly@sh.itjust.works
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        7 hours ago

        Not if you want that thing that everyone is on about. Don’t you want to be in with the crowd?! /s

      • dev_null@lemmy.ml
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        8 hours ago

        Yes, but for some tasks mistakes don’t really matter, like “come up with names for my project that does X”. No wrong answers here really, so an LLM is useful.

          • archomrade [he/him]@midwest.social
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            1 hour ago

            The energy expenditure for GPT models is basically a per-token calculation. Having it generate a list of 3-4 token responses would barely be a blip compared to having it read and respond entire articles.

            There might even be a case for certain tasks with a GPT model being more energy efficient than making multiple google searches for the same. Especially considering all the backend activity google tacks on for tracking users and serving ads, complaining about someone using a GPT model for something like generating a list of words is a little like a climate activist yelling at someone for taking their car to the grocery store while standing across the street from a coal-burning power plant.

          • dev_null@lemmy.ml
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            6 hours ago

            And yet virtually all of software has names that took some thought, creativity, and/or have some interesting history. Like the domain name of your Lemmy instance. Or Lemmy.

            And people working on something generally want to be proud of their project and not name it the first thing that comes to mind, but take some time to decide on a name.