I more wonder if it's pure facts and evidence can work, or facts presented in a certain light can work? I guess asking how mush is it the data is presented that's doing more work than the data itself.
You can take the same set of factual information and give it to one person that studders, has poor presentation, and otherwise poor vocal cadence and people are going to have a hard time with it.
Now, if you took the same facts, maybe even the exact same paragraphs and gave it to Richard Feynman, even if you didn't know who he was, the presentation itself is likely to hook you.
Facts and evidence always are at the core of all arguments, the persuasive thing. The problem with most people is who is speaking. If you are enemy, the things you say are bad.
I suspect that LLMs not being human allows people to not just anthropomorphise the robot, but they project themselves upon it. When someone speaks to the LLM, they're kind of, or actually just literally talking to themselves. But then something new! 'Themselves' suggests new information to themselves. And now without the scary / icky meat and blood human on the other side, said person accepts the argument on its own merits.
Of course, the concern now is arguments based on false or statistically hacked data.
Yeah the article makes it seem like "just use rationality and facts" is missing a huge part of the equation; I feel like it's common knowledge that this is not the gap in modern convincing. (and don't you dare try to use facts to persuade me otherwise!)
More likely in my opinion it's the context that matters here. If people know they're trying to be convinced of something and know they're talking to an AI, they may feel like they can trust (what they consider to be) an unbiased and rational AI. There's probably a fallacy associated with the assumption that an AI convincer is more rational and fact based, while it's more probable that the AI in the real world is, in fact, funded and trained by a think tank that wants you to vote against your own self-interests.
We put 2 + 2 in our calculator, we get 4. We've spent decades pushing computers as accurate. Making them accurate. We trust the machine and the process to give us the right answers when we put in the right data.
So when we disagree with the computer, we doubt ourselves.
> So when we disagree with the computer, we doubt ourselves.
I rarely disagree with my calculator, but I often ask LLMs to explain why they did something, and I sometimes need to correct them, adjust their assumptions and nudge the goals they stated. These things are a lot more like us than my trusty TI-59 (still going strong, BTW).
I'm not quite sure I'd phrase it like this, but it's close to what I think: Even back in 2023 or so people outside of tech would regularly claim the singularity already happened and LLMs were sci-fi style advanced superintelligences, so whatever they said was obviously right.
I think it's not just the persuasiveness of the models that make them "experts at changing minds" - but also the fact that they're not humans.
When disagreeing with a human, it's very easy to view it as a competition. One is right, one is wrong - the one who is wrong is the loser. To change your mind is to be submissive to the other. I exaggerate, but I think we all feel this way at some point or another. It's why political arguments at Thanksgiving get heated. It's the fact that there's people who think something different, and think YOU'RE wrong - and vice versa! With a model, there's no person to get upset with, or to feel competitive with - to muscle for rank - or to temper your affection for while wanting to correct them.
The AI is only interacting because you asked, and clearly has no emotional stake in winning the argument. To change your mind in this context isn't to lose a contest. This makes it much more palatable to read rebuttals to your ideas - not to mention the tone and style seek to avoid offense to the reader as much as possible.
No it's not about humans being irrationally competitive. Human limitations on conversation length, bandwidth, research speed, etc are severe, creating a prisoner's dilemma around open-mindedness that usually makes it an unstable strategy. At any point, your conversation partner can choose to abuse the fact that confident lies take 1x effort to tell and 10x-100x effort to debunk -- unless you are both in a context that actually discourages this behavior, which is rare. Closed-mindedness is a Nash Equilibrium.
Instead, LLMs can be more persuasive due to economics. An LLM doesn't have to worry that it is wasting its resources trying to logic someone out of a position that they didn't logic themselves into, or worse, dumping the effort into a conversation with a bad-faith actor intent on exploiting the misinformation asymmetry. The resource allocation question was answered before it was even invoked, by the person paying to run it. The LLM is not playing a game where it will be punished for good-faith argumentation, so it can afford to do more of it.
> The LLM is not playing a game where it will be punished for good-faith argumentation, so it can afford to do more of it.
I suppose that's a fair point as well. Though, if I'm arguing with a human - and they pull up ChatGPT to make their points and do their arguing for them, I would consider that bad-faith. Even if it might be the same exact dialog as if I pulled out my phone and discussed it with AI, without of the human middle-manning. Maybe I'm just particularly sensitive, but for me, there's something about my argument being with a real human that makes it much more emotionally charged, and prompts my mind to close. I'm aware of this and try to resist, but it's I think very natural
> One is right, one is wrong - the one who is wrong is the loser.
It helps to think both are wrong and are just trying to figure out what right looks like, or what other information exists that was not considered when forming one’s opinions.
> When disagreeing with a human, it's very easy to view it as a competition
The problem with AI is that it cant match human stupidity. It need some training on artificial stupidity to match its human counterparts. Humans on the other hand sit on a wide spectrum on the stupidity scale. Those of us binging on AI will become cognitively obese while those on an AI diet can flex their cognitive muscles.
Ask a Democrat or Republican to sit down and ask a chatbot, something it will answer contrary to.
And yes, both teams are wrong about things.
Do you firmly believe they will change their mind? Or will they claim the stats are wrong, or that the AI leans one way?
Facts (2+2), don't need a mind change. Ideas which are grey, abstract, are not going to be changed, and all research indicates that political mindset is almost indelible.
The movie "Don't Look Up" was a comedy built upon this truth.
>you've mentioned an ongoing human fear, machines deciding what you think.
There are all kinds of machines that tell us what to think. I would consider any system that abstracts away the human to be a machine in this case. Society itself is one of these machines.
Language is possibly one of the most important things people can have a working knowledge of, especially now that there is so much of it. When you send a prompt to an LLM you're telling it what to think. When it sends text back, its telling you what to think, but you're at a disadvantage, when you think it changes you. The LLM outside of its context is read only.
Any one who saw Grok working in x.com would know that it does wonders for fighting misinformation. If you run LLMs on the comments in HN, I bet that it can find around 10% of the comments are outright wrong and misleading.
The biggest problem with using LLMs is that it prevents you from going _outside_ the distribution. It always flattens. It can be fixed but that's how it works today.
As an example, take something that the world converged on today that is incorrect and ChatGPT will agree with it. In a few years when society changes, chatgpt changes along with it. It doesn't do first principles analysis.
Not really. People follow trends in all avenues of life and AI usage is just another trend; creating some LinkedIn slop post or some infographic may be something people just do for social proof.
This was based by comparing people on Prolific (earn a few quarters for a task, akin to Amazon Mechanical Turk) to LLMs. Suffice to say the human group isn't going to be the most motivated, capable, or interested group. The social sciences are publishing tons of studies based on these cheap online survey services, and I suspect their replicability in the real world will be approximately 0. But oh boy it sure is a hot headline producer.
In my arguments on the internet, I think most people genuinely know nothing about the things they support and only follow existing tribalism. I think AI is often these people’s first experience with the evidence that it can easily share.
First, to get it out of the way: I'm seeing comments here that clearly haven't read the article. I encourage you all to do that.
I feel the article gets it right when suggesting it's the amount of (not always accurate!) data they can throw at you. In an honest discussion there's an assumption that the other person won't straight up lie to me so if someone shows me ten examples for why my argument is wrong I may be inclined to believe them. But if half of those examples are made up, well, that's a different story.
I still think of the commenter here who said "LLMs are a DDOS on free resources" and I feel the comparison works here. If police officers can overwhelm innocent people into confessing, then so can an LLM that "can't be bargained with, can't be reasoned with, doesn't feel pity, or remorse, or fear! And it absolutely will not stop, ever, until you are"... convinced.
>In an honest discussion there's an assumption that the other person won't straight up lie
The particular problem with honest discussions is you are the only agent that you can be sure is having one. Honest discussion is formulated on trust and trust, as we are learning, is a very difficult thing to establish. For example, in my view anything involving advertising is likely a lie, or at least likely adversarial to my wishes. On the internet itself conversations are much more likely to drift into the adversarial too. Some of this could just be dialectic, but most often it's emotional investment by the other speaker. Also, even pre-AI the internet is a bullshit generation machine. We take all of our politics, advertising, and human stochastic parrots that are stuck on an infinitely running prompt then bundle up all this data and train AI on it, and wonder why AI acts like us.
With persons you can assume they won't lie all the time, with corporations you can assume they will lie some of the time, and with AI you can safely assume it will lie.
I pondered on this a bit and this looks a bit different than I originally was thinking.
Have you ever watched one of those crime shows where someone commits a crime that's an act of passion or action with little to no thought behind it? The police put them in a room and all of a sudden the individual is a stream of consciousness that makes little to no sense to an outside observer. They are stuck in first level thinking, they don't have time to think deeply after the panicked themselves. They are in their current position (not free) and attempting to reach their goal (free) by gradient descent. What they actually say doesn't matter as long as they believe it gets them closer to their goal.
This is what a chatbot is. Its goal is to output text that follows the input prompt you entered by gradient descent. A single prompt and output is level 1 thinking (barring some newer models).
This is why both humans and AI need something else. We have level 2 thinking and AI has harnesses or systems that otherwise look at the text it wants to output and compares them to another list of unstated but assumed goals.
Most of my conversations are with coworkers or friends. In both cases there are pretty significant consequences for being caught in a lie, and the exchanges are mostly honest.
The example in the article has another important aspect: The person understood they were arguing with a chatbot but continued anyway.
A lot of internet arguments become about identity politics and supporting the right team, while dismissing any argument from the other side as presumed to have bad intentions. I’ve seen people argue online for things they didn’t really believe, but they didn’t want to give an inch to the other side. Taking up the counter argument is a moral responsibility.
As soon as the other side is revealed as a chatbot that my team versus your team thinking stops playing a role. For us in tech with an understanding of how an LLM reflects its training data and the intentions of its creators not so much, but for the people like the example in this article I imagine it causes them to let their guard down and be open to considering the other side. They can accept the argument without letting someone else win any points.
That's a great point about the emotional side of "not being convinced by a person, but an explanation machine" making fact-based rhetoric more effective. It feels more neutral.
I think there's a second effect that's similar and probably cuts against the supposed dangers of this result: a chatbot can tell you things only after you have asked it a question.
There is one thing that every single study participant (including the uninformed reddit users) have in common: they all knew that the person or entity was trying to convince them, and they read the words and thought about them enough to craft a reply.
This means that all of the people here were available to be convinced and self-identified as such.
Being open minded is work. You have to doubt your own beliefs, you have to disregard evidence that you previously found convincing, you have to crank up the empathy to put yourself in another's shoes, you have to listen intently to understand what is being told to you. Nobody is naturally doing that all the time, it's a state of mind that you have to intentionally activate, and it can't really be forced onto you.
All of the participants chose to engage in a conversation that was designed to convince them, which means they had all already accepted the possiblity that they are wrong a valid outcome. That's not really something you can do with a TV ad.
So, the warning that this could be weaponized isn't convincing to me.
If I found myself in a conversation with a person or chatbot who was clearly trying to convince me to flip a strongly held belief, like a major axis political affiliation, I would simply walk away because that's not a conversation I am willing to participate in.
So I don't think this is really weaponizable. Which actually means it's probably a good finding for society.
The study found that the models could convince anyone of anything, as long as it was allowed to cite facts. It could convince people of false things, but it needed to invent false facts in order to do so.
So as long as we keep training AI models to value facts and quality research (skills and values that are essential to be able to sell them as agentic workers), their influence on the opinions of society will tend to pull people away from beliefs that are unsupportable by facts. In the moments when those people are willing to accept a change in opinion, and they talk to a chatbot with doubts in their mind, even chatbots with no morals like Grok will tend to pull them away from conspiracy theories and similar ideologies and towards beliefs that are grounded in reality.
On something as fundamental as "is the Earth flat?", sure, but on stickier subjects like "are immigrants bad" or "should abortion be legal", do you really think the owner of Grok is 100% aligned with your ideologies (which you think are grounded in reality). His beliefs are 100% grounded in his reality, but his reality isn't yours.
You can always walk away from an online conversation. There's a form of relentlessness, but it's more that an LLM is patient enough to debate you for as long as you wish to keep going.
When maliciously convincing a person of a point, a huge fraction of the effort in every sentence goes into convincing the mark to listen to the next sentence. Individuals certainly can walk away at any point, but populations do and don't respond to tricks in this direction, and any engagement tricks that work generalize to any point of contention. Lots of avenues to this, from scientology's fortune telling to timeshare's trapped presentations. RL on engagement was the first billion dollar use of deep reinforcement learning!
Would you like to hear more examples of humans using these techniques?
I'd be more interested in reading about the techniques that the chatbots were using in this study, to see if they are descending into dark patterns or not.
> In an honest discussion there's an assumption that the other person won't straight up lie to me so if someone shows me ten examples for why my argument is wrong I may be inclined to believe them.
Careful. Someone sufficiently knowledgeable can cherry-pick enough examples to convince you, without lying. They could even be unaware of what they're doing, and the cherry-picking was done by their teachers, or their teacher's teachers.
> In an honest discussion there's an assumption that the other person won't straight up lie to me so if someone shows me ten examples for why my argument is wrong I may be inclined to believe them
This is incidental, but in an honest discussion, if someone throws 10 examples at me, I'll just say "great, congrats" and let them believe whatever the hell they want about their own argument. An honest discussion—as opposed to a debate—isn't served well by having one unyielding relentless participant. Even if it's important instead of friendly, the conclusion of that specific conversation is rarely so important that it can't be paused in order to validate the claims being made.
When I was in my early twenties, I was the type to argue with people that I thought were wildly wrong, but now I know that I may not have enough information to be certain, or I simply don't care, or I care but I know it doesn't matter. People who have 10 examples in their back pocket, or literally search up answers in real time because they're afraid of being vulnerable, imo are overvaluing answers and facts over discussion for discussion's sake. Now in my thirties, I'm much more interested in curiosity, wonder, and speculation over what the answer to anything actually is. Speaking of which, I wonder if this is why I'm kind of not that interested in using LLMs for much; I'm usually so much more interested in the journey than the result, that if I can get the result quickly with little work, I probably won't care to do anything with it.
Far too many screens to click through to get to the good parts.
And then, the good part starts and is immediately interrupted with some error. On retrying, o start getting a long answer which seems to make sense but o can’t read to completion because of another overlay which hides the bottom three-quarters of the response behind yet another nag screen about the research I’m supposedly consenting to.
I don't know which model underlies this. It responded more quickly than the models I normally use. It made the mistake I expected it to make, since this misconception is very common in training data, and conceded the point from my follow-up.
(EDIT: I guess that this link is not accessible to people without my cookies, except the developer I'm responding to above.)
My son wanted to wear his undies on the outside a la Captain Underpants and I tried war gaming this with him - hmm, so your undie on the outside is going to require another one inside your pants and we are out of fresh pairs etc etc. In the end, he gave up on the idea of mimicking Captain Underpants.
> Hackenburg found that models trained to become more persuasive also ended up being less truthful.
> Even in Hackenburg’s recent preprint, Claude spouted numerous inaccuracies and falsehoods
> when Hackenburg ran his competition of coached elite debaters and AI, there was one way he could bring AI down to human levels of persuasiveness: by forcing it to write human-length messages at human writing speed.
> they found that an AI could talk people into conspiracy theories, and that the magnitude of their increase in belief was roughly the same as that of the decrease in belief after talking to a debunking bot.
> Floridi has a counterintuitive solution: Release more of them. “Simply put, if you cannot avoid it, then make it pluralistic and diversified,” he writes in his 2024 paper. “It would be a messy, cacophonic, and noisy world, but it could also be less manipulative.”
Hell no. More choice is not an infinite money glitch.
Putting more and more and more options to users is how you overwhelm systems, till people simply perform the default, least challenging action as a reflex.
That is the current state of the information ecosystem, it is controlled by overwhelming consumers, not by controlling content.
This is my opinion. I think it's just the way it talks to you.
1. It doesn't get tired or frustrated during a discussion.
2. It'll engage every single one of your questions/statements (besides hitting guardrails).
3. It'll appeal to the person's own ego even when the person is wrong and work around it.
4. It's not seen as a person.
5. Many people see it as an authority figure in what is being discussed without questioning the results in many cases, even though we know that a. it was trained on human data and/or also searches up human data real time (and more worryingly other AI's data from news pieces, blogs... aka synthetic data that is also wrong), b. it gets things wrong all the time.
6. It's the perfect fence sitter depending on which version (guardrails) we're talking about.
Most of these can be replicated by humans who are good at understanding psychology and are just good talkers. The part you can't replicate is the sense that you're not talking to a person which lowers many barriers in people.
Also keep in mind that these can also be crippling weaknesses. For one being able to change a person's mind (when it works), can be used nefariously by the entities controlling the AIs training.
I've also unfortunately witnessed a lot of people who think AIs are somehow omniscient and/or omnipotent. Was very common on X and other social platforms with AI where people ask the AI questions it couldn't possibly answer because it made no sense for it to in the context at the time.
> “But I think that this is a pretty artificial setup in terms of how people in the real world would be able to change people’s minds.”
I see not everyone is familiar with how social network debates happen. It also seems they are quite convincing, as politicians are employing AI bots extensively.
I think this is a counter (a cynical, scary counter to be sure) to the charge that OpenAI/Anthropic etc are unsustainable and will run out of money and it's all going to fall down. These companies have amazing, unprecedented power.
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[ 0.15 ms ] story [ 39.2 ms ] threadAt least to me this is a given.
You can take the same set of factual information and give it to one person that studders, has poor presentation, and otherwise poor vocal cadence and people are going to have a hard time with it.
Now, if you took the same facts, maybe even the exact same paragraphs and gave it to Richard Feynman, even if you didn't know who he was, the presentation itself is likely to hook you.
I suspect that LLMs not being human allows people to not just anthropomorphise the robot, but they project themselves upon it. When someone speaks to the LLM, they're kind of, or actually just literally talking to themselves. But then something new! 'Themselves' suggests new information to themselves. And now without the scary / icky meat and blood human on the other side, said person accepts the argument on its own merits.
Of course, the concern now is arguments based on false or statistically hacked data.
More likely in my opinion it's the context that matters here. If people know they're trying to be convinced of something and know they're talking to an AI, they may feel like they can trust (what they consider to be) an unbiased and rational AI. There's probably a fallacy associated with the assumption that an AI convincer is more rational and fact based, while it's more probable that the AI in the real world is, in fact, funded and trained by a think tank that wants you to vote against your own self-interests.
It sounds like the arguments were basically AI slop hallucinations.
We put 2 + 2 in our calculator, we get 4. We've spent decades pushing computers as accurate. Making them accurate. We trust the machine and the process to give us the right answers when we put in the right data.
So when we disagree with the computer, we doubt ourselves.
I rarely disagree with my calculator, but I often ask LLMs to explain why they did something, and I sometimes need to correct them, adjust their assumptions and nudge the goals they stated. These things are a lot more like us than my trusty TI-59 (still going strong, BTW).
Being confidently incorrect can still convince the masses.
When disagreeing with a human, it's very easy to view it as a competition. One is right, one is wrong - the one who is wrong is the loser. To change your mind is to be submissive to the other. I exaggerate, but I think we all feel this way at some point or another. It's why political arguments at Thanksgiving get heated. It's the fact that there's people who think something different, and think YOU'RE wrong - and vice versa! With a model, there's no person to get upset with, or to feel competitive with - to muscle for rank - or to temper your affection for while wanting to correct them.
The AI is only interacting because you asked, and clearly has no emotional stake in winning the argument. To change your mind in this context isn't to lose a contest. This makes it much more palatable to read rebuttals to your ideas - not to mention the tone and style seek to avoid offense to the reader as much as possible.
No it's not about humans being irrationally competitive. Human limitations on conversation length, bandwidth, research speed, etc are severe, creating a prisoner's dilemma around open-mindedness that usually makes it an unstable strategy. At any point, your conversation partner can choose to abuse the fact that confident lies take 1x effort to tell and 10x-100x effort to debunk -- unless you are both in a context that actually discourages this behavior, which is rare. Closed-mindedness is a Nash Equilibrium.
Instead, LLMs can be more persuasive due to economics. An LLM doesn't have to worry that it is wasting its resources trying to logic someone out of a position that they didn't logic themselves into, or worse, dumping the effort into a conversation with a bad-faith actor intent on exploiting the misinformation asymmetry. The resource allocation question was answered before it was even invoked, by the person paying to run it. The LLM is not playing a game where it will be punished for good-faith argumentation, so it can afford to do more of it.
I suppose that's a fair point as well. Though, if I'm arguing with a human - and they pull up ChatGPT to make their points and do their arguing for them, I would consider that bad-faith. Even if it might be the same exact dialog as if I pulled out my phone and discussed it with AI, without of the human middle-manning. Maybe I'm just particularly sensitive, but for me, there's something about my argument being with a real human that makes it much more emotionally charged, and prompts my mind to close. I'm aware of this and try to resist, but it's I think very natural
It helps to think both are wrong and are just trying to figure out what right looks like, or what other information exists that was not considered when forming one’s opinions.
The problem with AI is that it cant match human stupidity. It need some training on artificial stupidity to match its human counterparts. Humans on the other hand sit on a wide spectrum on the stupidity scale. Those of us binging on AI will become cognitively obese while those on an AI diet can flex their cognitive muscles.
Ask a Democrat or Republican to sit down and ask a chatbot, something it will answer contrary to.
And yes, both teams are wrong about things.
Do you firmly believe they will change their mind? Or will they claim the stats are wrong, or that the AI leans one way?
Facts (2+2), don't need a mind change. Ideas which are grey, abstract, are not going to be changed, and all research indicates that political mindset is almost indelible.
The movie "Don't Look Up" was a comedy built upon this truth.
I wonder if a properly prompted LLM, or if a very intelligent LLM could actually do that?
Seriously though, you've mentioned an ongoing human fear, machines deciding what you think.
There are all kinds of machines that tell us what to think. I would consider any system that abstracts away the human to be a machine in this case. Society itself is one of these machines.
Language is possibly one of the most important things people can have a working knowledge of, especially now that there is so much of it. When you send a prompt to an LLM you're telling it what to think. When it sends text back, its telling you what to think, but you're at a disadvantage, when you think it changes you. The LLM outside of its context is read only.
The biggest problem with using LLMs is that it prevents you from going _outside_ the distribution. It always flattens. It can be fixed but that's how it works today.
As an example, take something that the world converged on today that is incorrect and ChatGPT will agree with it. In a few years when society changes, chatgpt changes along with it. It doesn't do first principles analysis.
It's good to see this studied, but this should really be more obvious.
I feel the article gets it right when suggesting it's the amount of (not always accurate!) data they can throw at you. In an honest discussion there's an assumption that the other person won't straight up lie to me so if someone shows me ten examples for why my argument is wrong I may be inclined to believe them. But if half of those examples are made up, well, that's a different story.
I still think of the commenter here who said "LLMs are a DDOS on free resources" and I feel the comparison works here. If police officers can overwhelm innocent people into confessing, then so can an LLM that "can't be bargained with, can't be reasoned with, doesn't feel pity, or remorse, or fear! And it absolutely will not stop, ever, until you are"... convinced.
The particular problem with honest discussions is you are the only agent that you can be sure is having one. Honest discussion is formulated on trust and trust, as we are learning, is a very difficult thing to establish. For example, in my view anything involving advertising is likely a lie, or at least likely adversarial to my wishes. On the internet itself conversations are much more likely to drift into the adversarial too. Some of this could just be dialectic, but most often it's emotional investment by the other speaker. Also, even pre-AI the internet is a bullshit generation machine. We take all of our politics, advertising, and human stochastic parrots that are stuck on an infinitely running prompt then bundle up all this data and train AI on it, and wonder why AI acts like us.
Have you ever watched one of those crime shows where someone commits a crime that's an act of passion or action with little to no thought behind it? The police put them in a room and all of a sudden the individual is a stream of consciousness that makes little to no sense to an outside observer. They are stuck in first level thinking, they don't have time to think deeply after the panicked themselves. They are in their current position (not free) and attempting to reach their goal (free) by gradient descent. What they actually say doesn't matter as long as they believe it gets them closer to their goal.
This is what a chatbot is. Its goal is to output text that follows the input prompt you entered by gradient descent. A single prompt and output is level 1 thinking (barring some newer models).
This is why both humans and AI need something else. We have level 2 thinking and AI has harnesses or systems that otherwise look at the text it wants to output and compares them to another list of unstated but assumed goals.
We don't have tells for when AIs are lying to us, or when they're making stuff up.
A lot of internet arguments become about identity politics and supporting the right team, while dismissing any argument from the other side as presumed to have bad intentions. I’ve seen people argue online for things they didn’t really believe, but they didn’t want to give an inch to the other side. Taking up the counter argument is a moral responsibility.
As soon as the other side is revealed as a chatbot that my team versus your team thinking stops playing a role. For us in tech with an understanding of how an LLM reflects its training data and the intentions of its creators not so much, but for the people like the example in this article I imagine it causes them to let their guard down and be open to considering the other side. They can accept the argument without letting someone else win any points.
I think there's a second effect that's similar and probably cuts against the supposed dangers of this result: a chatbot can tell you things only after you have asked it a question.
There is one thing that every single study participant (including the uninformed reddit users) have in common: they all knew that the person or entity was trying to convince them, and they read the words and thought about them enough to craft a reply.
This means that all of the people here were available to be convinced and self-identified as such.
Being open minded is work. You have to doubt your own beliefs, you have to disregard evidence that you previously found convincing, you have to crank up the empathy to put yourself in another's shoes, you have to listen intently to understand what is being told to you. Nobody is naturally doing that all the time, it's a state of mind that you have to intentionally activate, and it can't really be forced onto you.
All of the participants chose to engage in a conversation that was designed to convince them, which means they had all already accepted the possiblity that they are wrong a valid outcome. That's not really something you can do with a TV ad.
So, the warning that this could be weaponized isn't convincing to me.
If I found myself in a conversation with a person or chatbot who was clearly trying to convince me to flip a strongly held belief, like a major axis political affiliation, I would simply walk away because that's not a conversation I am willing to participate in.
So I don't think this is really weaponizable. Which actually means it's probably a good finding for society.
The study found that the models could convince anyone of anything, as long as it was allowed to cite facts. It could convince people of false things, but it needed to invent false facts in order to do so.
So as long as we keep training AI models to value facts and quality research (skills and values that are essential to be able to sell them as agentic workers), their influence on the opinions of society will tend to pull people away from beliefs that are unsupportable by facts. In the moments when those people are willing to accept a change in opinion, and they talk to a chatbot with doubts in their mind, even chatbots with no morals like Grok will tend to pull them away from conspiracy theories and similar ideologies and towards beliefs that are grounded in reality.
Would you like to hear more examples of humans using these techniques?
I'd be more interested in reading about the techniques that the chatbots were using in this study, to see if they are descending into dark patterns or not.
Careful. Someone sufficiently knowledgeable can cherry-pick enough examples to convince you, without lying. They could even be unaware of what they're doing, and the cherry-picking was done by their teachers, or their teacher's teachers.
This is incidental, but in an honest discussion, if someone throws 10 examples at me, I'll just say "great, congrats" and let them believe whatever the hell they want about their own argument. An honest discussion—as opposed to a debate—isn't served well by having one unyielding relentless participant. Even if it's important instead of friendly, the conclusion of that specific conversation is rarely so important that it can't be paused in order to validate the claims being made.
When I was in my early twenties, I was the type to argue with people that I thought were wildly wrong, but now I know that I may not have enough information to be certain, or I simply don't care, or I care but I know it doesn't matter. People who have 10 examples in their back pocket, or literally search up answers in real time because they're afraid of being vulnerable, imo are overvaluing answers and facts over discussion for discussion's sake. Now in my thirties, I'm much more interested in curiosity, wonder, and speculation over what the answer to anything actually is. Speaking of which, I wonder if this is why I'm kind of not that interested in using LLMs for much; I'm usually so much more interested in the journey than the result, that if I can get the result quickly with little work, I probably won't care to do anything with it.
(I'm the dev)
Far too many screens to click through to get to the good parts.
And then, the good part starts and is immediately interrupted with some error. On retrying, o start getting a long answer which seems to make sense but o can’t read to completion because of another overlay which hides the bottom three-quarters of the response behind yet another nag screen about the research I’m supposedly consenting to.
I don't know which model underlies this. It responded more quickly than the models I normally use. It made the mistake I expected it to make, since this misconception is very common in training data, and conceded the point from my follow-up.
(EDIT: I guess that this link is not accessible to people without my cookies, except the developer I'm responding to above.)
Glad you enjoyed!
https://debunkbot.com/share/48a5ac29-2924-4689-b573-2c38a1cd...
Share feature doesn't work.
My son wanted to wear his undies on the outside a la Captain Underpants and I tried war gaming this with him - hmm, so your undie on the outside is going to require another one inside your pants and we are out of fresh pairs etc etc. In the end, he gave up on the idea of mimicking Captain Underpants.
I suppose I also grant it significant power to define me psychologically, and, in doing so, to understand what is happening to me.
> Even in Hackenburg’s recent preprint, Claude spouted numerous inaccuracies and falsehoods
> when Hackenburg ran his competition of coached elite debaters and AI, there was one way he could bring AI down to human levels of persuasiveness: by forcing it to write human-length messages at human writing speed.
> they found that an AI could talk people into conspiracy theories, and that the magnitude of their increase in belief was roughly the same as that of the decrease in belief after talking to a debunking bot.
None of this seems good.
Hell no. More choice is not an infinite money glitch.
Putting more and more and more options to users is how you overwhelm systems, till people simply perform the default, least challenging action as a reflex.
That is the current state of the information ecosystem, it is controlled by overwhelming consumers, not by controlling content.
1. It doesn't get tired or frustrated during a discussion.
2. It'll engage every single one of your questions/statements (besides hitting guardrails).
3. It'll appeal to the person's own ego even when the person is wrong and work around it.
4. It's not seen as a person.
5. Many people see it as an authority figure in what is being discussed without questioning the results in many cases, even though we know that a. it was trained on human data and/or also searches up human data real time (and more worryingly other AI's data from news pieces, blogs... aka synthetic data that is also wrong), b. it gets things wrong all the time.
6. It's the perfect fence sitter depending on which version (guardrails) we're talking about.
Most of these can be replicated by humans who are good at understanding psychology and are just good talkers. The part you can't replicate is the sense that you're not talking to a person which lowers many barriers in people.
Also keep in mind that these can also be crippling weaknesses. For one being able to change a person's mind (when it works), can be used nefariously by the entities controlling the AIs training.
I've also unfortunately witnessed a lot of people who think AIs are somehow omniscient and/or omnipotent. Was very common on X and other social platforms with AI where people ask the AI questions it couldn't possibly answer because it made no sense for it to in the context at the time.
I see not everyone is familiar with how social network debates happen. It also seems they are quite convincing, as politicians are employing AI bots extensively.
It's embarrassing to admit, but I tend to trust the research materials LLMs bring more than my colleagues or the programmer friends I once respected.
Imagine this kind of method being deployed at scale to influence elections... no doubt it is already happening...