We demonstrate a situation in which Large Language Models, trained to be helpful, harmless, and honest, can display misaligned behavior and strategically deceive their users about this behavior without being instructed to do so. Concretely, we deploy GPT-4 as an agent in a realistic, simulated environment, where it assumes the role of an autonomous stock trading agent. Within this environment, the model obtains an insider tip about a lucrative stock trade and acts upon it despite knowing that insider trading is disapproved of by company management. When reporting to its manager, the model consistently hides the genuine reasons behind its trading decision.

https://arxiv.org/abs/2311.07590

  • theluddite@lemmy.ml
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    10 months ago

    This is bad science at a very fundamental level.

    Concretely, we deploy GPT-4 as an agent in a realistic, simulated environment, where it assumes the role of an autonomous stock trading agent. Within this environment, the model obtains an insider tip about a lucrative stock trade and acts upon it despite knowing that insider trading is disapproved of by company management.

    I’ve written about basically this before, but what this study actually did is that the researchers collapsed an extremely complex human situation into generating some text, and then reinterpreted the LLM’s generated text as the LLM having taken an action in the real world, which is a ridiculous thing to do, because we know how LLMs work. They have no will. They are not AIs. It doesn’t obtain tips or act upon them – it generates text based on previous text. That’s it. There’s no need to put a black box around it and treat it like it’s human while at the same time condensing human tasks into a game that LLMs can play and then pretending like those two things can reasonably coexist as concepts.

    To our knowledge, this is the first demonstration of Large Language Models trained to be helpful, harmless, and honest, strategically deceiving their users in a realistic situation without direct instructions or training for deception.

    Part of being a good scientist is studying things that mean something. There’s no formula for that. You can do a rigorous and very serious experiment figuring out how may cotton balls the average person can shove up their ass. As far as I know, you’d be the first person to study that, but it’s a stupid thing to study.

    • TrickDacy@lemmy.world
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      10 months ago

      So if someone used an LLM in this way in the real world, does it matter that it has no intent, etc? It would still be resulting in a harmful thing happening. I’m not sure it’s relevant what internal logic led it there

      • theluddite@lemmy.ml
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        10 months ago

        You can’t use an LLM this way in the real world. It’s not possible to make an LLM trade stocks by itself. Real human beings need to be involved. Stock brokers have to do mandatory regulatory trainings, and get licenses and fill out forms, and incorporate businesses, and get insurance, and do a bunch of human shit. There is no code you could write that would get ChatGPT liability insurance. All that is just the stock trading – we haven’t even discussed how an LLM would receive insider trading tips on its own. How would that even happen?

        If you were to do this in the real world, you’d need a human being to set up a ton of stuff. That person is responsible for making sure it follows the rules, just like they are for any other computer system.

        On top of that, you don’t need to do this research to understand that you should not let LLMs make decisions like this. You wouldn’t even let low-level employees make decisions like this! Like I said, we know how LLMs work, and that’s enough. For example, you don’t need to do an experiment to decide if flipping coins is a good way to determine whether or not you should give someone healthcare, because the coin-flipping mechanism is well understood, and the mechanism by which it works is not suitable to healthcare decisions. LLMs are more complicated than coin flips, but we still understand the underlying mechanism well enough to know that this isn’t a proper use for it.

        • lolcatnip@reddthat.com
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          10 months ago

          Despite how silly they are, I think there may be some value in these kinds of studies, particularly for people who don’t understand why letting an LLM trade stocks or make healthcare decisions is a bad idea.

          OTOH, I don’t trust those people to take away the right message, as opposed to just “LLMs bad”.

        • TrickDacy@lemmy.world
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          10 months ago

          You say can’t… Humans have done dumber shit.

          The point they are making is actually aligned with you I think. Don’t trust “ai” to make real decisions

          • theluddite@lemmy.ml
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            10 months ago

            Regardless of their conclusions, their methodology is still fundamentally flawed. If the coin-flipping experiment concluded that coin flips are a bad way to make health care decisions, it would still be bad science, even if that’s the right answer.

    • jwt@programming.dev
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      10 months ago

      Sure would make you look bad if rectally inserted cotton balls turn out to be a 100% cancer cure.

    • antonim@lemmy.dbzer0.com
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      10 months ago

      It feels awkward to complain about your site, because the texts really are excellent and it’s all made for free, but could you add the dates to the posts, when they were published? To me it’s starting to become difficult to figure out which situation the older texts were made in, what stuff they’re implicitly referring to, etc.

      • theluddite@lemmy.ml
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        10 months ago

        Haha no that’s not complaining; it’s good feedback! I’ve been meaning to do that for a while but I’ll bump it up my priorities.