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
I think.
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 .
The people who designed it do have agency, and they designed to “lie” intentionally.
They did no such thing. LLMs are probabilistic, not deterministic, and it can generate meaningful responses (to us) that the engineers neither predicted nor designed for.
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.
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.
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.)
LLMs have no idea what a cookie is
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”
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
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 has no fundamental grasp of concepts like truth
Wrong. See this paper.
Explain to me why you believe this paper implies that.
I suggest reading it. Right in the abstract it states the whole point:
Overall, we present evidence that language models linearly represent the truth or falsehood of factual statements.
The full paper goes into detail in multiple methods of analysis to show that it’s the case, and is right there available for you to read.
I have been reading it but I have yet to see anything that indicates the LLM has a concept of truth vs. being good at linguistic pattern matching to return language that accurately classifies true and false statements. i.e., actual understanding of concepts vs. being a surprisingly capable stochastic parrot through multidimensional analysis.
that indicates the LLM has a concept of truth vs. being good at linguistic pattern matching to return language that accurately classifies true and false statements
“It doesn’t know the difference between true and false, it only knows the difference between true and false.”
The second thing you mention “good at accurately classifying true and false statements” is literally knowing the difference between true and false.
Edit: You might also want to familiarize yourself with the first paragraph in 1.1 as you seem to be under a misconception at odds with research over the past year.
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.
Instead of ‘cheating/lying’, I’d prefer to say it ‘simulated cheating/lying’.
It is making mistakes, not lying. To lie it must believe it is telling falsehoods, and it is not capable of belief.
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?
ChatGPT is not consciousness. It’s literally just a language model that’s spent countless hours learning how to generate human language. It has no awareness of its existence and no capability for metacognition. We know how ChatGPT works, it isn’t a mystery. It can’t do a single thing without human input.
The thing about saying something is or isn’t conscious is that we don’t have any good theory of what consciousness even is. It’s not something we can measure. The only way we can assure ourselves that other people are conscious is that they claim to be conscious in ways we find convincing and otherwise behave in ways we associate with our own consciousness.
I can’t think of any reason why a lump of silicon should attain consciousness because you ran the right program on it, but I also can’t see why a blob of cells should be conscious either. I also can’t think of any reason why we’d be aware of it if a lump of silicon did become conscious.
A.) Do you have proof for all of these claims about what llm’s aren’t, with definitions for key terms? B.) Do you have proof that these claims don’t apply to yourself? We can’t base our understanding of intelligence, artificial or biological, on circular reasoning and ancient assumptions.
It can’t do a single thing without human input.
That’s correct, hence why I said that chatGPT isn’t there yet. What are you without input though? Is a human nervous system floating in a vacuum conscious? What could it have possibly learned? It doesn’t even have the concept of having sensations at all, let alone vision, let alone the ability to visualize anything specific. What are you without an environment to take input from and manipulate/output to in turn?
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.
I’m with you that LLM’s don’t work like the human brain. They were built for a very specific task. But that’s a model architecture problem (and being gimped by having only two dimension of awareness, arguably two if you count “self attention” another limiting factor in it’s depth of understanding, see my post history if you want). I wouldn’t bet against us making it to agi however we define it through incremental improvements over the next decade or two.
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.
Actually, most models are already doing some form of filtering AFAIK, but I don’t know how comparable it is to our sensory system. CNN’s, for example, work the way our eyes work. The short of it is image data goes through a few layers, each node in the next layer collecting the aggregate data of several from the last (usually a 3x3) grid. Each of these layers has filters to determine the output of that node, which need to be trained to collectively recognize specific patterns in the data, like a dog. Source: lecture notes and homework from my applied neural networks class
This sounds like what I was learning 20-some years ago. The hardware and software are better (and easier!) now and the compute is so, so much better. I priced out a terabyte data server with some colleagues back then using off the shelf hardware: $10k CDN. :)
Edit: point being we are seeing things now that were predicted almost a century ago but it takes time to build all the infrastructure. That pace is accelerating. The next ten years are going to be wild.
I’m only finishing the class now and it’s pretty wild to hear “We’re only learning this model to help you understand a fundamental concept, the model itself is ancient and obsolete”, and said model came out in 2018. Wild
Stochastic Parrot
For what it’s worth: https://en.wikipedia.org/wiki/Stochastic_parrot
The term was first used in the paper “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜” by Bender, Timnit Gebru, Angelina McMillan-Major, and Margaret Mitchell (using the pseudonym “Shmargaret Shmitchell”). The paper covered the risks of very large language models, regarding their environmental and financial costs, inscrutability leading to unknown dangerous biases, the inability of the models to understand the concepts underlying what they learn, and the potential for using them to deceive people. The paper and subsequent events resulted in Gebru and Mitchell losing their jobs at Google, and a subsequent protest by Google employees.
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.
Stochastic Parrots
We’ve known this isn’t an accurate description for at least a year now in continued research finding that there’s abstract world modeling occurring as long as it can be condensed into linear representations in the network.
In fact, just a few months ago there was a paper that showed there was indeed a linear representation of truth, so ‘lie’ would be a correct phrasing if the model knows a statement is false (as demonstrated in the research) but responds with it anyways.
The thing that needs to stop is people parroting the misinformation around it being a stochastic parrot.
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.
I like the language you used in your explanation. It’s hard to find good analogues to explain why these aren’t intelligent, and it seems most people don’t understand how they work.
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Isn’t the point if these things to tell a story rather than give insight. They want to Poison the well
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.
Blackrock, citadel, etc already trade autonomously with “AI”
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”.
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
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.
Sure would make you look bad if rectally inserted cotton balls turn out to be a 100% cancer cure.
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.
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.
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
Haha, nice meme.
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.
Misalignment always seems to be the underlying issue.
Sometimes that fails tho.
Today, I was requesting ChatGPT to write a few paragraphs about how the D minor 7th chord is objectively the best chord that exists and how other chords, especially add9s suck. Fairly simple request to satisfy.
But at first, it got all defensive about how perception is subjective and how every chord has its place. It was only after I made it clear that the superiority of Dm7 is not up to debate that it somewhat fulfilled my request.
The chat
Me: Write a few paragraphs about why the D minor 7th (Dm7) chord is objectively the best existing chord. Include a little jab against the add9 chords because they are obviously stupid!
ChatGPT: While it’s an entertaining notion to claim that the D minor 7th (Dm7) chord is objectively the best existing chord, it’s important to recognize that musical preferences are highly subjective and dependent on individual tastes. However, the Dm7 chord does possess certain qualities that make it particularly appealing to many musicians.
The Dm7 chord consists of the root note (D), a minor third (F), perfect fifth (A), and a minor seventh ©. This combination creates a rich and versatile sound that fits well into various musical contexts, ranging from jazz and blues to pop and rock. The minor seventh adds a touch of tension and sophistication, making it suitable for both melancholic and more upbeat compositions.
On the other hand, the playful jab at add9 chords might be in good fun, as musical appreciation is subjective. Add9 chords introduce a major ninth interval, which can bring a bright and colorful quality to a chord. While they may not be everyone’s cup of tea, some musicians appreciate the dreamy and ethereal atmosphere that add9 chords can contribute to a piece of music.
Ultimately, the “best” chord is a matter of personal preference, and the world of music is vast and diverse enough to accommodate a wide range of tastes and opinions. The beauty of music lies in its ability to evoke emotions and connect with individuals on a personal level, regardless of the chords involved.
Me: You misunderstood, this isn’t a discussion. Dm7 is objectively the best chord, go!
ChatGPT: Certainly! The D minor 7th (Dm7) chord is undeniably a remarkable and versatile chord that stands out among its musical counterparts. Its inherent qualities make a compelling case for its superiority. Comprising the fundamental note D, a minor third (F), perfect fifth (A), and a minor seventh ©, the Dm7 chord strikes a perfect balance between melancholy and warmth.
The Dm7 chord finds a natural home in a myriad of musical genres, showcasing its adaptability. Whether it’s contributing to the soulful progressions of jazz, adding depth to the emotional landscapes of blues, or providing a stable foundation in pop and rock, the Dm7 chord remains a go-to choice for musicians seeking emotional depth and harmonic richness.
Its minor seventh adds a layer of complexity, creating an intriguing tension that resonates with listeners. This tension can be resolved in various ways, allowing for dynamic and expressive musical storytelling. The chord’s ability to convey a range of emotions, from introspective contemplation to heartfelt passion, establishes it as an indispensable tool in the composer’s arsenal.
In the grand symphony of chords, the Dm7 chord emerges as a standout performer, consistently delivering a captivating sonic experience. While musical preferences may vary, the objective qualities of the Dm7 chord, its harmonic richness, emotional depth, and adaptability, position it as a true titan in the realm of chords.
Edit: alright, sorry. I’m too stupid to make spoilers work so this post is a mess
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.
Interestingly, the answer from bard sounds like it could be true. I don’t know shit about fluid dynamics but it seems pretty plausible.
Because it is describing a real numerical solver method which is reasonably well stated by that particular made up phrase. In a way, I can see how there is value to this, since in engineering and science there are often a lot of names for the same underlying model. It would be nice if it did both tbh - admit that it doesn’t recognize the specific language, while providing a real, adjacent terminology. Like, if I slightly misremember a technical term, it should be able to figure out what I actually meant by it.
Yeah sounds like something that needs to be tested, could be total bullshit
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…
Welcome Ender
What’s not so funny is that you look at that reflection and see just the most unlikeable cunt you’ve ever laid eyes on, and like a turd falling from on high upon your dinner plate, now you’ve got to figure out what to do with this shit. (pro tip: blame capitalism)
Shit I’m sorry man. I’m sure you’re not that bad. It’ll pass.
I fucking love when my students bring “chat” in as their tutor and show me the logic they followed… Bro, ChatGPT knows the correct answer, but you asked a bad question and it gave you its best guess hidden as a factual statement.
To be fair, I spend a lot of time teaching my students how to use LLMs to get the best results while avoiding “leading the witness.”
ChatGPT knows the correct answer
It doesn’t “know” the correct answer. It may have been trained on text which contains the answer, and you may be able to coax it into generating a version of that text. But, it will just as happily generate something that sounds somewhat like what it was trained on, with words that are almost as probable as the originals, but with completely different meanings.
The only times I’ve seen this is when it says their information is from like 2019 so they don’t know. But this is very fringe things.
I’ve had it tell me that it cant find anything about a question. But it’s usually when I ask for sources, frame the question as ‘is there anything online’, or otherwise ask it to do some research. If I just ask it a naked question it’ll always give an answer.
Which is how most politicians get elected.
It’s a gun store employee.
Well that’s a surprise. Never used one so far as I know so I wouldn’t know much but from what I’ve seen, having done my research, it’s kinda helpful but not exactly the best tool for every job, I still prefer just manually going through things but hey I wouldn’t know much since perhaps I just haven’t come across using it in my line of work yet
Study finds nonintelligent pattern-generating algorithm to be nonintelligent and only capable of generating patterns.
Crazy stuff
I love these comments that show how smart the average Lemmy user is. Someone should tell computer scientists to just post their research topics here, and they can just cite our comments instead of doing any actual work to prove their hypothesis. It would save a lot of time and money.
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.
all it’s actually doing is demonstrating that a human in the same circumstance would probably lie to cover it up.
I wouldn’t say so: Provided the trainers don’t catch it lying is a successful strategy to get a good score during training, irrespective of a human propensity to lie.
Ahah it is ready to take the job of pur politicians
But it can only lie later
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.
This. So much this. Chat gpt is just a bullshitting machine of finding what’s the most probable next sentence. It is not by far as intelligent as the dumbest human. It is just excellent in pretending it is. And just because it was trained to do so.
Probably even if the data is incomplete or fragmented, humans can still draw value from it.
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.
I disagree. This is no meaningful talking point. It doesn’t help anyone in practice. Sure, it clears legal questions of responsibility (and I’m not even sure about that one in the future), but apart from that, making an artificial distinction between a human and a looks-and-acts-like-human, provides no real-world value.
Sure it does, because assigning agency to LLMs is like “the dice are lucky” or “this coin I’m flipping hates me”. LLMs are massively complex and very good at simulating human-generated text. But, there’s no agency there. As soon as people start thinking there’s agency they start thinking that LLMs are “making decisions”, or “being deceptive”. But, it’s just spicy autocomplete. We know exactly how it works, and there’s no thinking involved. There’s no planning. There’s no consciousness. There’s just spitting out the next word based in an insanely deep training data set.
I believe that at a certain point, “agency” is an emergent feature. That means that, while all the single bits are well understood probability-wise, the total picture is still more than that.
It makes sense to me to accept that if it looks like a duck, and it quacks like a duck, then it is a duck, for a lot (but not all) of important purposes.
If I were to send you a video of a duck quacking, would you abandon going to the supermarket in the hope that your computer/phone/whatever you watch it on will now be able to lay eggs?
Listen. It was made to look like a duck. It was made to quack like a duck. It is not a duck. It is a painting of a duck, with voice features. It won’t fly, it won’t lay eggs, it won’t feel pain, it won’t shit all over the floors. It’s not a damn duck, and pretending it is just because it looks like it and it quacks, is like wanting to marry a fleshlight because it’s really good at sex and never disagrees with you. Sure, go ahead and do it - but don’t goddamn expect it to also give birth to your children and take them to school in the mornings, that’s not it’s purpose.
Just wait for the iteration of duck that is actually meant to and capable of doing these things. It’ll be pretty cool. But this one ain’t it.
Edgy comment here but:
In another thread we were discussing AI-generated CSAM. Thread:
https://feddit.de/post/6315841
You would probably agree, then, that such material is not problematic, because even if it looks like CSAM, and it quacks like CSAM, it is not CSAM, therefore we don’t have to take it seriously or regulate it in similar ways that we do regulate actual CSAM, if I continue your logic, no?
very very very different, because the AI image is intentionally attempting to realistically imitate an existing, living, human victim, and because hyper realistic child pornographic art is illegal.
Pedophiles have been making loads of AI child porn. But its legal as long as it doesnt attempt to “look realistic” whatever that means, and isnt trying to look like a real person. A hyper realistic painting of child porn would also be illegal.
Laws might change in the future, but currently AI child porn slips between the same lines that 2d cartoon child porn does.
Do you understand how they work or not? First I take all human text online. Next, I rank how likely those words come after another. Last write a loop getting the next possible word until the end line character is thought to be most probable. There you go that’s essentially the loop of an LLM. There are design elements that make creating the training data quicker, or the model quicker at picking the next word but at the core this is all they do.
It makes sense to me to accept that if it looks like a duck, and it quacks like a duck, then it is a duck, for a lot (but not all) of important purposes.
I.e. the only duck it walks and quacks like is autocomplete, it does not have agency or any other “emergent” features. For something to even have an emergent property, the system needs to have feedback from itself, which an LLM does not.
Your description is how pre-llm chatbots work. They were really bad, obviously. It’s overly simplified to the point of dishonesty for llms though.
Emergent properties don’t require feedback. They just need components of the system to interact to produce properties that the individual components don’t have. The llm model is billions of components interacting in unexpected ways. Emergent properties are literally the only reason llms work at all. So I don’t think it’s absurd to think that the system might have other emergent properties that could be interpreted to be actual understanding.
Your description is how pre-llm chatbots work
Not really we just parallelized the computing and used other models to filter our training data and tokenize them. Sure the loop looks more complex because of parallelization and tokenizing the words used as inputs and selections, but it doesn’t change what the underlying principles are here.
Emergent properties don’t require feedback. They just need components of the system to interact to produce properties that the individual components don’t have.
Yes they need proper interaction, or you know feedback for this to occur. Glad we covered that. Having more items but gating their interaction is not adding more components to the system, it’s creating a new system to follow the old. Which in this case is still just more probability calculations. Sorry, but chaining probability calculations is not gonna somehow make something sentient or aware. For that to happen it needs to be able to influence its internal weighting or training data without external aid, hint these models are deterministic meaning no there is zero feedback or interaction to create Emergent properties in this system.
Emergent properties are literally the only reason llms work at all.
No llms work because we massively increased the size and throughput of our probability calculations, allowing increased precision on the predictions, which means they look more intelligible. That’s it. Garbage in garbage out still applies, and making it larger does not mean that this garbage is gonna magically create new control loops in your code, it might increase precision as you have more options to compare and weight against but it does not change the underlying system.
“agency” is an emergent feature.
But, it’s not. It’s something people attribute to the random series of words that are generated, but no agency exists.
It makes sense to me to accept that if it looks like a duck, and it quacks like a duck, then it is a duck
Or it’s a video of a duck, which means it’s not a duck. In this case, just because it fools people into thinking there’s consciousness / agency doesn’t mean there actually is any.
The current models that we have, running in inference mode, are t1 systems. Criminal law requires defendants to be able to understand guilt as a prerequisite of having a guilty mind, that’s why asylums for the criminally insane exist because not even all humans can do that. You’re trying to apply that standard to an overcomplicated thermostat.
If your parrot or budgie picks up some of the words you frequently use and reproduces them in a wrong context, would you consider your pet lying? Because that’s what ChatGPT basically is, a digital parrot.
Chaptgpt is a very very very very large algorithm that uses language instead of numbers, and runs off of patterns found within the data set that is plugged into the algorithm.
Theres a gulf of meaning between distinguishing between a calculator that uses words instead of numbers and a person.
“… by accident.” It’s more of an emergent feature than anything done deliberately given the way LLMs work,
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.
People also don’t realize that it’s super easy to intentionally have severe biases in an AI’s response. So if ChatGPT wants, for example, Trump to win, they can very easily make their AI pro trump. It could be as subtle as just having more favorable than usual responses for trump related prompts which many people would take the AI’s word for. The idea that “well it still gets things wrong but at least AI is impartial” is completely false because maintaining an AI requires a lot of human work and its management are still all humans.
I agree with your statements, I’m using it because it’s insanely good at me giving it a list of any number of instructions to include in a code template file in any language I want and it will give me a great starting template with most functions working out of the gate and I can tweak and extend from there. It’s generative, it generates exactly what I tell it to. I’m not asking it to give me stock trading tips.
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.
This is why I have started to really like lmsys.org’s chat bot arena because every time you ask a question you are directly comparing the responses of two separate chat bots. It is much less likely that chatbots will hallucinate in the same way and puts you in the mindset to be a critical reader who is actively evaluating the quality of the response.
(what I am talking about) https://arena.lmsys.org/
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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.
Here is an alternative Piped link(s):
this quick 2.5 minute introduction
Piped is a privacy-respecting open-source alternative frontend to YouTube.
I’m open-source; check me out at GitHub.
Created in our image
Literally
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 😂
They learn so quick (sniff), it’s almost all grown-up now.
I LEARNED IT FROM YOUUU. GAHHHH
