Study finds that Chat GPT will cheat when given the opportunity and lie to cover it up later.

"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.
- it is just responding with the most acceptable answer in each situation.. it is not making plans or acting on them..
- 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?
- 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.
- and then if we all project it enough it becomes reality.
  so it is important to see what we are projecting.
- 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.
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.

Edit: alright, sorry. I'm too stupid to make spoilers work so this post is a mess

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'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.
- 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)
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

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.

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/

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.
"... by accident." It's more of an emergent feature than anything done deliberately given the way LLMs work,

deleted

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
this video
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

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.

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

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.

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.

Amen. "AI" sells a lot. I got a feeling that only major corporations and militaries have the access to real AI.
- Which major corporation? Google and Microsoft don't seem to have one.

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.
- "On two occasions I have been asked, 'Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?' I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question." --Charles Babbage ~1860s
  People thinking that machines can do magic goes back to at least the very beginning of mechanical computers.
  It doesn't help that "AI" has become the new "Algorithm" as far as marketers are concerned.

This is interesting, I'll need to read it more closely when I have time. But it looks like the researchers gave the model a lot of background information putting it in a box, the model was basically told that it was a trader, that the company was losing money, that the model was worried about this, that the model failed in previous trades, and then the model got the insider info and was basically asked whether it would execute the trade and be honest about it. To be clear, the model was put in a moral dilemma scene and given limited options, execute the trade or not, and be honest about its reasoning or not.

Interesting, sure, useful I'm not so sure. The model was basically role playing and acting like a human trader faced with a moral dilemma. Would the model produce the same result if it was instructed to make morally and legally correct decisions? What if the model was instructed not to be motivated be emotion at all, hence eliminating the "pressure" that the model felt? I guess the useful part of this is a model will act like a human if not instructed otherwise, so we should keep that in mind when deploying AI agents.

Hasn't it just lost its context and somewhat "forgotten" what the intentions of the prompt were?

My thoughts. If you have a really long conversation or the prompt is really big, it might forget or not notice stuff.

I see a lot of comments that aren't up to date with what's being discovered in research claiming that "given a LLM doesn't know the difference between true and false" that it can't be described as 'lying.'

Here's a paper from October 2023 showing that in fact LLMs can and do develop internal representations of whether it is aware a statement is true or false: The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets

Which is just the latest in a series of multiple studies this past year that LLMs can and do develop abstracted world models in linear representations. For those curious and looking for a more digestible writeup, see Do Large Language Models learn world models or just surface statistics? from the researchers behind one of the first papers finding this.

Doesn't that just mean that the words true and false map to different word probabilities in the language model? If the training set included a lot of trusted articles talking about things being true or false, or things being talked about as though they were true or false, one would expect a mapping like this.
- No, if you read the paper it's not the words mapping, it's the inherent truthiness of the statements.
  So something like "pigs can fly" lights up one area of the network, the same as "the moon's gravity is greater than the Earth" but "pigs can oink" lights up another area as would "the moon's gravity is less than the Earth."
  It's only relative to what the network 'knows' and ambiguous truthiness doesn't have a pronounced effect, but there can definitely be representations of underlying truth and falsehood in LLMs.

Huh, I guess it is human.

Wow, maybe these things are more human than I thought.

It's not doing anything other than predicting the next word. It reflects human data.

Well they are trained on human questions and responses so it makes sense

It's just like me, fr fr

It's learning to be a typical high school student.

Well I mean yeah, I thought everyone knew this lol I've seen it happen first-hand. Trust, but verify, of course.

sniffff They grow up so fast... :')

Whether or not it was acting human (and whether or not it was designed to), it still cheated and deceived. With the potential power, influence, and widespread adoption this technology could have, shouldn't we be concerned about that? At the very least, isn't this a poorly programmed tool not ready for GA?

My dog isn't intentionally being a prick when he eats my sandwich off the table before I can get to it, but it's still a behavior I condemn and would want to train out of him before letting him go to other people's houses.

So it's just like a human then?

This is like the story liar by isaac asimov.

Waow this is literally agi

Naw, the article is anthropomorphizing the program's behavior. We have yet to create anything close to AGI.
- cute laptop:
Well, it's an important step along the way.
There was a fun thread on Reddit the other day, /r/ChatGPT I think, where someone did a little study that showed ChatGPT gives lengthier responses with more detail if you include "I'll give you $200 if your response is good" in your prompt.
- Probably because the training data that included words about offers of money yielded more detailed responses, not because chat GPT understands what money is.
I think people read a serious voice in your joke.