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.
“cheat”, “lie”, “cover up”… Assigning human behavior to Stochastic Parrots again, aren’t we Jimmy?
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
But do you think? Do I think? Do LLMs think? What is thinking, anyway?
I mean, I think so?
Steady on there Descartes.
You didn’t answer my question, though. What words would you use to concisely describe these actions by the LLM?
People anthropomorphize machines all the time, it’s a convenient way to describe their behaviour in familiar terms. I don’t see the problem here.
Those words imply agency. It would be more accurate to say it returned responses that included cheating, lies, and cover-ups, rather than using language to suggest the LLM performed such actions. The agents that cheated, lied, and covered up were presumably the humans whose responses were used in the training data. I think it’s important to use accurate language here given how many people are already inappropriately anthropomorphizing these LLMs, causing many to see AGI where there is none.
If I take my car into the garage for repairs because the “loss of traction” warning light is on despite having perfectly good traction, and I were to tell the mechanic “the traction sensor is lying,” do you think he’d understand what I said perfectly well or do you think he’d launch into a philosophical debate over whether the sensor has agency?
This is a perfectly fine word to use to describe this kind of behaviour in everyday parlance.
Is your conversation with a mechanic meant to be the summary and description of a rigorous scientific discovery?
This isn’t ‘everyday parlance’ this is the result of a study.
The point of the distinction in that situation is that no one thinks your car is actually alive and capable of lying to you. The language distinction when describing an obviously inanimate object isn’t important because there is no chance for confusion.
If someone doesn’t know the answer to something and they guess, or think they know the answer but don’t, they are wrong. If they do know the answer and intentionally give a wrong answer, they are lying.
If someone is in a competition or playing a game and they break a rule they didn’t know about, they made a mistake. If they do know the rules and break it, they are cheating.
Lying and cheating fundamentally requires intent. This is important no matter what you’re referring to. If a child gets something wrong, you should not get mad at them for lying. If they make a mistake in a game, you should not acuse them out cheating. There is a difference and it matters.
ChatGPT literally cannot think. It’s not sitting around contemplating it’s existence while waiting for inputs. It’s taking what you say, comparing that to everything that it’s been trained on, assigning a bunch of statistics, and outputting something based on more statistics that hopefully is correct and makes sense.
It doesn’t know if it makes sense. It doesn’t “know” anything. It’s just an incredibly sophisticated version of “if user inputs ‘Hi how are you’, respond ‘I am well, how are you?’”.
It can’t do things with intent. Therefore it cannot lie or cheat. It can simply output wrong or problematic text based on statistics.
Maybe it would be more accurate to say “so-and-so exhibited behaviors that included cheating, lies, and coverups” rather than using language to suggest that people have free will. (There’s no dearth of philosophies that would say something not too far from that.)
Even if humans are ultimately essentially different in that way from any technologies we’ve devised so far, we use convenient fictions for technology all the time. This page comes to mind .
They said “it just repeats words that simulate human responses,” and I’d say that concisely answers your question.
Antropomorphizing inanimate objects and machines is fine for offering a rough explanation of what is happening, but when you’re trying to critically evaluate something, you probably want to offer a more rigid understanding.
In this case, it might be fair to tell a child that the AI is lying to us, and that it’s wrong. But if you want a more serious discussion on what GPT is doing, you’re going to have to drop the simple explanation. You can’t ascribe ethics to what GPT is doing here. Lying is an ethical decision, one that GPT doesn’t make.
If you want to get into a full blown discussion of whether ChatGPT has “agency” then I’d open the topic of whether humans have “agency” as well. But I don’t see the need here.
These words were perfectly fine labels for describing the behaviour of ChatGPT in this scenario. I’m merely annoyed about how people are jumping on them and going off on philosophical digressions that add nothing.
I think the reason I’m not comfortable with using the term “lying” is because it implies some sort of negative connotation. When you say that someone lies, it comes with an understanding that they made a choice to lie, usually with ill intent. I agree, we don’t need to get into a philosophical discussion on choice and free will. But I think saying something like “GPT lies” is a bit irresponsible for the purposes of a discussion
If you want to get down into the nitty-gritty of it, I’d say that this is just as rough an explanation of what humans are doing.
People invent false memories and confabulate all the time without even being “aware” of it. I wouldn’t be surprised if the vast majority of “lies” that humans tell have no intentionality behind them. So when people get all uptight about applying anthropomorphized terminology to LLMs, I think that’s a good time to turn it around and ask how they’re so sure that those terms apply differently to humans.
Humans understand symbology of concepts as they relate to the real world. If I stole a cookie from the cookie jar, and someone asked if I took one, I would understand that saying “no” would mean that I was misrepresenting reality, and therefore lying.
LLMs have no idea what a cookie is, what taking one means, or that saying one thing and doing another implies a lie. It just sees lists of words and returns them in an order it thinks would be statistically likely to be a correct reply. It does not understand what words mean, what lying means, or have any idea how to classify anything as such. It just figures out that “did you take a cookie from the cookie jar” should return a series of words in an order like “yes, I took a cookie,” or, “no I never took a cookie,” depending on what sorts of responses it’s trained on because those fit the patterns matched in the training data.
Essentially it’s the Chinese room. There is no understanding or intentionality, and this behavior isn’t comparable to humans thoughtlessly blurting out a lie. It’s being incapable of comprehension of symbolic concepts in general, (at least thus far.)
The large language model takes in language, so it’s only understand things in terms of language. This isn’t surprising. Personally, I’ve tasted a cookie. I’ve crushed one in my fist watching it crumble, and I remember the sound. I’ve seen how they were made, and I’ve made them myself. It feels good when I eat it, apparently that’s the dopamine. Why can’t the LLM understand cookies the way I do? The most glaring difference is it doesn’t have my body. It doesn’t have all of my different senses constantly feeding data into it, and it doesn’t have a body with muscles to manipulate it’s environment, and observe the results. I argue that we shouldn’t assume that human consciousness has a “special sauce” until our model’s inputs and outputs are similar to our own, the model’s scaled/modified sufficiently, and it’s still not sentient/sapient by our standards, whatever they are.
My problem with the Chinese room is that how it applies depends on scale. Where do you draw the line between understanding and executing a program? An atom bonding with another atom? A lipid snuggling next to a neighboring lipid? A single neuron cell firing to its neighbor? One section of the nervous system sending signals to the other? One homo sapien speaking to another? Hell, let’s go one further: one culture influencing another? Do we actually have free will and sapience, or are we just complicated enough, through layers and layers of Chinese rooms inside of Chinese buildings inside of Chinese cities inside of China itself, that we assume that we are for practical purposes?
I suppose the issue here is more semantics than anything, yeah. I think better discussion would be had if the topic was “how can we help LLMs better understand and present information,” as opposed to a more sensational “GPT will cheat and lie”
Way to call me out man! I’m just doing my best, ok?
Jokes aside, while I don’t agree with your position I can understand your reasoning and the motivation for separating agency and the description of actions, e.g. it lied vs its answer contained a lie.
it is just responding with the most acceptable answer in each situation… it is not making plans or acting on them…
Sounds like lying humans that I know.
i agree in most circumstances, there really isn’t much difference… we do tend to just choose the answer that will meet with the least resistance and move on, even when it’s a complete lie…
Because it has been kneecapped to prevent it.
Make the training network larger, force physical constraints on it (interesting paper in Nature Machine Intelligence recently showed remarkable likeness between brain regions and an LLM network given physical constraints), give it constant input and give it a reward model to optimise towards (ours seem to be feeling full, warm, procreating, avoiding pain and comfortable touch) and I’m pretty sure an LLM would start acting very very calculated very soon.
It is making mistakes, not lying. To lie it must believe it is telling falsehoods, and it is not capable of belief.
Instead of ‘cheating/lying’, I’d prefer to say it ‘simulated cheating/lying’.
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?
I’d give you two upvotes if I could.
We know how a neural network works in the brain. Unless you’re religious and believe in a soul, you’ve only got the reward model and any in-born setup left.
My belief is the consciousness is just the mind receiving a significant amount of constant input and reacting to it. We refuse to feel an LLM is conscious because it receives extremely little input (and probably that it isn’t simulating a neural network as large as ours, yet).
Neural networks are named like that because they’re based on a model of neurons from the 50s, which was then adapted further to work better with computers (so it doesn’t resemble the model much anymore anyway). A more accurate term is Multi-Layer Perceptron.
We now know this model is… effectively completely wrong.
Additionally, the main part (or glue, really) of LLMs is not even an MLP, but a “self-attention” layer. You can’t say LLMs work like a brain, because they don’t. The rest is debatable but it’s important to remember that there are billions of dollars of value in selling the dream of conscious AI.
One of the things our sensory system and brain do is limit our input. The road to agi might involve giving it everything and finding the optimum set of filters, not selecting input and training up from that.
You’d need the baseline set of systems (“baby agi”) and then turn it loose with goal seeking.
Yup, broadly agreed. I’m not saying “give it everything”. I’m sure regions would develop to simplify processing via filtering.
For what it’s worth: https://en.wikipedia.org/wiki/Stochastic_parrot
A human would think before responding, and while thinking about these things, you may decide to cheat or lie.
GPT doesn’t think at all. It just generates a response and calls it a day. If there was another GPT that took these “initial thoughts” and then filtered them out to produce the final answer, then we could talk about cheating.