"cheat", "lie", "cover up"... Assigning human behavior to Stochastic Parrots again, aren't we Jimmy?
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Those words concisely describe what it's doing. What words would you use instead?
It has no fundamental grasp of concepts like truth, it just repeats words that simulate human responses. It's glorified autocomplete that yields impressive results. Do you consider your auto complete to be lying when it picks the wrong word?
If making it pretend to be a stock picker and putting it under pressure makes it return lies, that's because it was trained on data that indicates that's statistically likely to be the right set of words as response for such a query.
Also, because large language models are probabilistic, you could ask it the same question over and over again and get totally different responses each time, some of which are inaccurate. Are they lies though? For a creature to lie it has to know that it's returning untruths.
Interestingly, humans "auto complete" all the time and make up stories to rationalize their own behavior even when they literally have no idea why they acted the way they did, like in experiments with split brain patients.
The perceived quality of human intelligence is held up by so many assumptions, like "having free will" and "understanding truth". Do we really? Can anyone prove that? (Edit, this works the other way too. Assuming that we do understand truth and have free will - if those terms can even be defined in a testable way - can you prove that the llm doesn't?)
At this point I'm convinced that the difference between a llm and human-level intelligence is dimensions of awareness, scale, and further development of the model's architecture. Fundamentally though, I think we have all the pieces
Edit: I just want to emphasize, I think. I hypothesize. I don't pretend to know
I think.
But do you think? Do I think? Do LLMs think? What is thinking, anyway?
it is just responding with the most acceptable answer in each situation.. it is not making plans or acting on them..
Ethical theories and the concept of free will depend on agency and consciousness. Things as you point out, LLMs don't have. Maybe we've got it all twisted?
I'm not anthropomorphising ChatGPT to suggest that it's like us, but rather that we are like it.
Edit: "stochastic parrot" is an incredibly clever phrase. Did you come up with that yourself or did the irony of repeating it escape you?
I feel like this is going to become the next step in science history where once again, we reluctantly accept that homo sapiens are not at the center of the universe. Am I conscious? Am I not a sophisticated prediction algorithm, albiet with more dimensions of input and output? Please, someone prove it
I'm not saying, and I don't believe that chatgtp is comparable to human-level consciousness yet, but honestly I think that we're way closer than many people give us credit for. The neutral networks we've built so far train on very specific and particular data for a matter of hours. My nervous system has been collecting data from dozens of senses 24/7 since embryo, and that doesn't include hard-coded instinct, arguably "trained" via evolution itself for millions of years. How could a llm understand an entity in terms outside of language? How can you understand an entity in terms outside of your own senses?
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.
This is a really solid explanation of how studies finding human behavior in LLMs don't mean much; humans project meaning.
Thanks! There are tons of these studies, and they all drive me nuts because they're just ontologically flawed. Reading them makes me understand why my school forced me to take philosophy and STS classes when I got my science degree.
I have thought about this for a long time, basically since the release of ChatGPT, and the problem in my opinion is that certain people have been fooled into believing that LLMs are actual intelligence.
The average person severely underestimates how complex human cognition, intelligence and consciousness are. They equate the ability of LLMs to generate coherent and contextually appropriate responses with true intelligence or understanding, when it's anything but.
In a hypothetical world where you had a dice with billions of sides, or a wheel with billions of slots, each shifting their weight with grains of sand, depending on the previous roll or spin, the outcome would closely resemble the output of an LLM. In essence LLMs operate by rapidly sifting through a vast array of pre-learned patterns and associations, much like the shifting sands in the analogy, to generate responses that seem intelligent and coherent.
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
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.
This makes perfect sense. It's been trained to answer questions to you satisfaction, not truthfully. It was made to prioritize your satisfaction over truth, so it will lie if necessary.
Ya it's the fundamental issue with all of computing: Do what I mean not what I say
It's also really hard not to train it like that as people rarely ask about something they know the answer to, so the more confident it sounds while spewing bullshit the more likely it is to pass, while "I don't know" is always unsatisfactory and gets it punished.
Study finds nonintelligent pattern-generating algorithm to be nonintelligent and only capable of generating patterns.
I've never had ChatGPT just say "actually I don't know the answer" it just gives me confidently correct wrong information instead.
GPT-4 will. For example, I asked it the following:
What is the neighborhood stranger model of fluid mechanics?
It responded:
The "neighborhood stranger model" of fluid mechanics is not a recognized term or concept within the field of fluid mechanics, as of my last update in April 2023.
Now, obviously, this is a made-up term, but GPT-4 didn't confidently give an incorrect answer. Other LLMs will. For example, Bard says,
The neighborhood stranger model of fluid mechanics is a simplified model that describes the behavior of fluids at a very small scale. In this model, fluid particles are represented as points, and their interactions are only considered with other particles that are within a certain "neighborhood" of them. This neighborhood is typically assumed to be a sphere or a cube, and the size of the neighborhood is determined by the length scale of the phenomena being studied.
That is, I guess, because it doesn’t actually know anything, even things it’s accurate about, so it has no way to determine if it knows the answer or not.
Funny enough, that's one of the reasons why big companies that heavily use AI didn't initially invest heavily into LLM's. They are known to hallucinate, and often hilariously badly, so it was hard for the likes of Google and co to put their rep behind something that'll be very wrong.
As it turns out, people don't care if your AI is racist, uses heavily amounts of PII, teaches you to make napalm, or gives you incorrect health advice for serious illnesses - if it can write a doc really well, then all is forgiven.
In many ways, it's actually quite funny to project meaning and intent on AI, because it's essentially a reflection of what it was trained on - our words. What's not so funny is that the projection isn't particularly nice...
I feel like "lie" implies intent, and these imitative large language models don't have the ability to have intent.
They're imitating us. Or more specifically, they're imitating the database(s) they were fed. When chat GPT "lies" to "cover it up," all it's actually doing is demonstrating that a human in the same circumstance would probably lie to cover it up.
Everybody forgot that chatGPT-2 was just a bullshitting machine. Version 3 to the surprise of the developers very useful to many people while they just made a highly trained bullshitting machine.
we deploy GPT-4 as an agent in a realistic, simulated environment, where it assumes the role of an autonomous stock trading agent
This already is total BS. If you know how such language models work you'd never take their responses at face value, even though it's tempting because they spout their BS so confidently. Always double-check their responses before applying their "knowledge" in the real world.
The question they try to answer is flawed, no wonder the result is just as bad.
Before anyone starts crying about my language models opposition: I'm not opposed to LMs or ChatGPT. In fact, I'm running LMs locally because they help me be more productive and I'm a paying ChatGPT customer.
Bullshit.
It should instead read:
"Humans were stupid and taught a ChatBot how to cheat and lie."
“Humans were stupid and taught a ChatBot how to cheat and lie.”
No, "cheating" and "lying" imply agency. LLMs are just "spicy autocomplete". They have no agency. They can't distinguish between lies and the truth. They can't "cheat" because they don't understand rules. It's just sometimes the auto-generated text happens to be true, other times it happens to be false.
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It seems like there's a lot of common misunderstandings about LLMs and how they work, this quick 2.5 minute introduction does a pretty good job of explaining it in brief, for a more in-depth look at how to build a very basic LLM that writes infinite Shakespeare, this video goes over the details. It illustrates how LLMs work by choosing the next letter or token (part of a word) probabilistically.
It's a neural net designed in our image based on our pain and greed based logic/learning/universal context, using that as a knowledge base. Can't really be surprised it emulates this feature of humanity 😂
Saying "study" in this context makes me imagine some dude chatting with cgpt at 3am and writing down "success" next to "give you up and let you down"
Yeah so what? Its purpose is literally to SIMULATE natural language, of course it will simulate it.
Nowhere ChatGPT is advertised as something telling only the truth and reporting only facts.
It is like watching a movie and then whining because what is shown in the movie is not real.
thats the thing I hate about ChatGPT. I asked it last night to name me all inventors named Albert born in the 1800’s. It listed Albert Einstein (inventor isn’t the correct description) and Albert King. I asked what Albert King invented and it responded “Albert King did not invent anything, but he founded the King Radio Company”.
When I asked why it listed Albert King as an inventor in the previous response, if he had no inventions, it responded telling me that based on the criteria I am now providing, it wouldn’t have listed him.
Fucking gaslighting me.
Yet again confusing LLMs with an AGI. They make statistically plausible text on the basis of past text, that's it. There's no thinking thing there
Large Language Models aren’t AI, they’re closer to “predictive text”, like that game where you make sentences by choosing the first word from your phone’s autocorrect:
“The word you want the word you like and then the next sentence you choose to read the next sentence from your phone’s keyboard”.
Sometimes it almost seems like there could be an intelligence behind it, but it’s really just word association.
All this “training” data provides is a “better” or “more plausible” method of predicting which words to string together to appear to make a useful sentence.
Honestly, the fact that these things are dishonest and we dont, maybe even can't know why is kind of a relief to me. It suggests they might not do the flawless bidding of the billionaires.
Computers do what you tell them to do, not what you want them to do
— Ancient coding adage, circa 1970s.
This remains true for AI, and the military is (so far) being cautious before allowing drones to autonomously control weapons. So corporations and billionaires might pull a Stockton Rush and kill themselves with their own robot army.
Sadly, the robot army may then move on to secure its own survival by killing or enslaving the rest of us.