this post was submitted on 04 Dec 2023
644 points (92.8% liked)

Technology

60123 readers
3677 users here now

This is a most excellent place for technology news and articles.


Our Rules


  1. Follow the lemmy.world rules.
  2. Only tech related content.
  3. Be excellent to each another!
  4. Mod approved content bots can post up to 10 articles per day.
  5. Threads asking for personal tech support may be deleted.
  6. Politics threads may be removed.
  7. No memes allowed as posts, OK to post as comments.
  8. Only approved bots from the list below, to ask if your bot can be added please contact us.
  9. Check for duplicates before posting, duplicates may be removed

Approved Bots


founded 2 years ago
MODERATORS
 

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

https://arxiv.org/abs/2311.07590

top 50 comments
sorted by: hot top controversial new old
[–] tinsuke@lemmy.world 219 points 1 year ago (4 children)

"cheat", "lie", "cover up"... Assigning human behavior to Stochastic Parrots again, aren't we Jimmy?

[–] Hamartiogonic@sopuli.xyz 8 points 1 year ago

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.

[–] yesman@lemmy.world 7 points 1 year ago* (last edited 1 year ago) (2 children)

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?

[–] 0ops@lemm.ee 13 points 1 year ago* (last edited 1 year ago) (9 children)

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?

[–] rambaroo@lemmy.world 7 points 1 year ago* (last edited 1 year ago) (2 children)

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.

load more comments (2 replies)
load more comments (8 replies)
[–] bilb@lem.monster 7 points 1 year ago

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.

load more comments (2 replies)
[–] theluddite@lemmy.ml 122 points 1 year ago (5 children)

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.

[–] Sekoia@lemmy.blahaj.zone 34 points 1 year ago (3 children)

This is a really solid explanation of how studies finding human behavior in LLMs don't mean much; humans project meaning.

[–] theluddite@lemmy.ml 23 points 1 year ago (1 children)

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.

[–] dannym@lemmy.escapebigtech.info 10 points 1 year ago

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.

load more comments (2 replies)
[–] TrickDacy@lemmy.world 14 points 1 year ago (1 children)

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

[–] theluddite@lemmy.ml 18 points 1 year ago* (last edited 1 year ago) (5 children)

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.

load more comments (5 replies)
[–] jwt@programming.dev 5 points 1 year ago

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

load more comments (2 replies)
[–] hoshikarakitaridia@sh.itjust.works 69 points 1 year ago (5 children)

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.

[–] tdawg@lemmy.world 13 points 1 year ago (1 children)

Ya it's the fundamental issue with all of computing: Do what I mean not what I say

load more comments (1 replies)
[–] JohnEdwa@sopuli.xyz 11 points 1 year ago

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.

load more comments (3 replies)
[–] rtxn@lemmy.world 61 points 1 year ago* (last edited 1 year ago) (2 children)

Study finds nonintelligent pattern-generating algorithm to be nonintelligent and only capable of generating patterns.

[–] Quik@infosec.pub 5 points 1 year ago

Crazy stuff

load more comments (1 replies)
[–] AWittyUsername@lemmy.world 59 points 1 year ago (9 children)

I've never had ChatGPT just say "actually I don't know the answer" it just gives me confidently correct wrong information instead.

[–] canihasaccount@lemmy.world 22 points 1 year ago* (last edited 1 year ago) (2 children)

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.

[–] butterflyattack@lemmy.world 7 points 1 year ago (1 children)

Interestingly, the answer from bard sounds like it could be true. I don't know shit about fluid dynamics but it seems pretty plausible.

load more comments (1 replies)
load more comments (1 replies)
[–] CoggyMcFee@lemmy.world 18 points 1 year ago* (last edited 1 year ago)

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.

[–] EnderMB@lemmy.world 11 points 1 year ago (3 children)

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...

load more comments (3 replies)
[–] Speculater@lemmy.world 8 points 1 year ago

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."

[–] SasquatchBanana@lemmy.world 4 points 1 year ago

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.

load more comments (4 replies)
[–] ristoril_zip@lemmy.zip 50 points 1 year ago (1 children)

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.

load more comments (1 replies)
[–] bassad@jlai.lu 43 points 1 year ago (1 children)

Ahah it is ready to take the job of pur politicians

load more comments (1 replies)
[–] SlopppyEngineer@discuss.tchncs.de 28 points 1 year ago (2 children)

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.

[–] Strobelt@lemmy.world 5 points 1 year ago

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.

load more comments (1 replies)
[–] gandalf_der_12te@feddit.de 27 points 1 year ago (1 children)

Bullshit.

It should instead read:

"Humans were stupid and taught a ChatBot how to cheat and lie."

[–] Lemminary@lemmy.world 5 points 1 year ago

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

[–] LWD@lemm.ee 24 points 1 year ago* (last edited 1 year ago)
[–] paddirn@lemmy.world 18 points 1 year ago

They learn so quick (sniff), it’s almost all grown-up now.

[–] crsu@lemmy.world 18 points 1 year ago

Created in our image

[–] PlatinumSf@pawb.social 17 points 1 year ago* (last edited 1 year ago)

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 😂

[–] DirigibleProtein@aussie.zone 11 points 1 year ago (2 children)

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.

load more comments (2 replies)
[–] Olhonestjim@lemmy.world 9 points 1 year ago (1 children)

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.

[–] uriel238@lemmy.blahaj.zone 8 points 1 year ago* (last edited 1 year ago) (1 children)

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.

[–] turmacar@lemmy.world 5 points 1 year ago

"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.

[–] NevermindNoMind@lemmy.world 8 points 1 year ago* (last edited 1 year ago)

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.

[–] tweeks@feddit.nl 7 points 1 year ago (1 children)

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

[–] Octopus1348@lemy.lol 3 points 1 year ago* (last edited 1 year ago)

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

[–] flop_leash_973@lemmy.world 6 points 1 year ago

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

[–] AlexWIWA@lemmy.ml 4 points 1 year ago

Huh, I guess it is human.

[–] guywithoutaname@lemm.ee 3 points 1 year ago

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

load more comments
view more: next ›