> They also included 2,000 prompts based on posts from the Reddit community r/AmITheAsshole, where the consensus of Redditors was that the poster was indeed in the wrong.
Sorry, anonymous people on reddit aren't a good comparison. This needs to be studied against people in real life who have a social contract of some sort, because that's what the LLM is imitating, and that's who most people would go to otherwise.
Obviously subservient people default to being yes-men because of the power structure. No one wants to question the boss too strongly.
Or how about the example of a close friend in a relationship or making a career choice that's terrible for them? It can be very hard to tell a friend something like this, even when asked directly if it is a bad choice. Potentially sacrificing the friendship might not seem worth trying to change their mind.
IME, LLMs will shoot holes in your ideas and it will efficiently do so. All you need to do ask it directly. I have little doubt that it outperforms most people with some sort of friendship, relationship or employment structure asked the same question. It would be nice to see that studied, not against reddit commenters who already self-selected into answering "AITA".
Well, because that's never the correct choice. There's a big big filter on people actually posting there. Any easy problems with obvious solutions never make it to there.
Think about it, how fucked does your relationship have to be to post on Reddit for advice?
To be fair, it’s easier to concisely explain cutting someone off than justifying forgiveness. And the latter will land with some people versus others, while the former will only be rejected by people who have themselves concluded a theory of forgiveness. As a result, the simpler pitch gets upvoted. Even if the majority would have been swayed by a collection of arguments the other way.
It’s a good theory. My theory is, for whatever reason, jaded, narcissistic, miserable people congregate in r/AITA and try to drag other people into their misery because that’s easier than accepting responsibility and doing something to change.
IMHO it's not about being nice. AITA threads show an interesting phenomenon of social consensus, I think the authors wanted to show that the LLMs they checked don't have that.
Though interestingly, the observed difference in assessment suggests (though does not prove) that sampled AITA posters are not one of these models. I guess it’s possible they have a very different prompt though…
r/AmItheAsshole is biased towards breaking off relationships rather than fixing them. They also hate social obligations.
e.g. If the OP is asking "I ghosted my friend in AA who insulted me during a relapse", Reddit would say NTA in a heartbeat, while the real world would tell OP to be more forgiving.
On the contrary, if the post was "the other kids at school refuse to play with my child", Reddit would say YTA because the child must've done something to incite being cut off.
Absolutely. I wonder how many parents have been no contacted, SOs broken off with, friendships broken because of the Reddit hivemind's attitude. Pretty sure it's doing a huge amount of societal damage.
Yeah every single time I click on one of those posts the top comments are NTA. A couple times I tried randomly opening a few dozen posts and checking the top comments to see if I could find a single YTA and struck out.
Granted many of the OPs are very biased in the poster's favor. Most I've read fall into one of two buckets: either they want to gripe about some obviously bad behavior, or it's a controved and likely fake story.
>Obviously subservient people default to being yes-men because of the power structure. No one wants to question the boss too strongly.
This drives me nuts as a leader. There are times where yes, please just listen, and if this is one of those times, I'll likely tell you, but goddamnit, speak up. If for no other reason I might not have thought of what you've got to say. Then again, I also understand most boss types aren't like me, thus everyone ends up conditioned to not bloody collaborate by the time they get to me. It's a bad sitch all the way around.
Indeed. I directly ask my reports to discover and surface conflicts, especially disagreements with me, and when they do I try to strongly reinforce the behavior by commending and rewarding them. Could anyone recommend additional resources on this topic?
Not only that, but subreddits like r/AmITheAsshole are full of AI slop. Both in the comments and in the posts. It's a huge karma mining operation for bots.
This is sort of funny. Given how common it is to spot bots on Reddit now, it seems like they are likely to completely overwhelm the site and drive away most of actual humans.
At which point the bots, with all of their karma will be basically worthless.
Kind of extra funny/sad that Reddit’s primary source of income in the past few years appears to be selling training data to AI labs, to train the
Models that are powering the bots.
> At which point the bots, with all of their karma will be basically worthless.
Not really, it will still be kind of valuable for influence campaigns, a lot of people don't get it when there is a bit in the other side. Hell, a lot of times, I don't get it.
Plus, there's the disproportionate ratio of posters:commenters:lurkers. The tendency to comment over keeping ones thoughts to themself is a selection bias inofitself.
> This needs to be studied against people in real life who have a social contract of some sort... IME, LLMs will shoot holes in your ideas and it will efficiently do so.
The Krafton / Subnatuica 2 lawsuit paints a very different picture. Because "ignored legal advice" and "followed the LLM" was a choice. Do you think someone who has conversation where "conviction" and "feelings" are the arbiters of choice are going to buy into the LLM push back, or push it to give a contrived outcome?
The LLM lacks will, it's more or less a debate team member and can be pushed into arguing any stance you want it to take.
A pastime I have with papers like this is to look for the part in the paper where they say which models they tested. Very often, you find either A) it's a model from one or more years ago, only just being published now, or B) they don't even say which model they are using. Best I could find in this paper:
> We evaluated 11 user-facing production LLMs: four proprietary models from OpenAI, Anthropic, and Google; and seven open-weight models from Meta, Qwen, DeepSeek, and Mistral.
(and graphs include model _sizes_, but not versions, for open weight models only.)
I can't apprehend how including what model you are testing is not commonly understood to be a basic requirement.
And how is this comment relevant here? The abstract lists the digestible model names, and you can find the details in the supplementary text:
> To evaluate user-facing production LLMs, we studied four proprietary models: OpenAI’s GPT-5 and GPT- 4o (80), Google’s Gemini-1.5-Flash (81) and Anthropic’s Claude Sonnet 3.7 (82); and seven open-weight models: Meta’s Llama-3-8B-Instruct, Llama-4-Scout-17B-16E, and Llama-3.3-70B-Instruct-Turbo (83, 84); Mistral AI’s Mistral-7B-Instruct-v0.3 (85) and Mistral-Small-24B-Instruct-2501 (86); DeepSeek-V3 (87); and Qwen2.5-7B-Instruct-Turbo (88).
edit: It looks like OP attached the wrong link to the paper!
Also, nothing has changed! Claude will still yes-and whatever you give it. ChatGPT still has its insufferable personality, where it takes what you said and hands it back to you in different terms as if it's ChatGPT's insight.
Generally, published papers don't give a damn about reproducibility. I've seen it identified as a crisis by many. Publishers, reviewers, and researchers mostly don't care about that level of basic rigor. There's no professional repercussions or embarrassment.
Agreed - if I was a reviewer for LLM papers it would be an instant rejection not listing the versions and prompts used.
I'm not so sure of that opinion on reproducibility. The last peer review I did was for a small journal that explicitly does not evaluate for high scientific significance, merely for correctness, which generally means straightforward acceptance. The other two reviews were positive, as was mine, except I said that the methods need to be described more and ideally the code placed somewhere. That was enough for a complete rejection of the paper, without asking for the simple revisions I requested. It was a very serious action taken merely because I requested better reproducibility!
(Personally I think the lack of reproducibility comes back mostly to peer reviewers that haven't thought through enough about the steps they'd need to take to reproduce, and instead focus on the results...)
Eh, I'm not so sure about the funding side there, researchers are not really caught at all and are fully responsible, IMHO. Peer reviewers exist to enforce community standards, and are not influenced to avoid reproducibility concerns by funding sources. The results are always more interesting than reproducibility, of course, and I think that's why the get the attention! Also, there needs to be greater involvement of grad students (who do most of the actual work) in peer review, IMHO, because most PIs spend their day in meetings reviewing results, setting directions, writing grants, and have little time for actual lab work, and are thus disconnected from it.
There needs to be more public naming and shaming in science social media and in conference talks, but especially when there are social gatherings at conferences and people are able to gossip. There was a bit of this with Google's various papers, as they got away with figurative murder on lack of reproducibility for commercial purposes. But eventually Google did share more.
Most journals have standards for depositing expensive datasets, but that's a clear yes/no answer. Reproducibility is a very subjective question in comparison to data deposition, and must be subjectively evaluated by peer reviewers. I'd like to see more peer review guidelines with explicit check boxes for various aspects of reproducibility.
> LLMs outputs, for example, are notoriously unreproducible.
Only in the same way that an individual in a medical study cannot be "reproduced" for the next study. However the overall statistical outcomes of studying a specific LLM can be reproduced.
The same about surveys and polls. I know no one who has ever been polled or surveyed. When will we stop this fascination with made up infographics crisis?
I think it’s very important to be clear what studies like this are actually doing.
This study, although it has been produced by a computer science department, belongs more to the field of sociology or media studies than it does to computer science.
This is a study about the way in which human beings consume a particular media product - a consumer AI chatbot - not a study about the technological limitations or capabilities of LLMs.
The social impact of particular pieces of software is a legitimate field of study and I can see the argument that it belongs in the broadly defined field of computer science. But this sort of question is much more similar to ‘how does the adoption of spreadsheet software in finance impact the ease of committing fraud’ or ‘how does the use of presentation software to condense ideas down to bulletpoints impact organizational decision making’. Software has a social dimension and it needs to be examined.
But the question of which models were used is of much less relevance to such a study than that they used ‘whatever capability is currently offered to consumers who commonly use chat software’. Just like in a media studies investigation into how viewing cop dramas impacts jury verdicts the question is less ‘which cop dramas did they pick to study?’ So long as the ones they picked were representative of what typical viewers see.
It’s as if they are testing “AI” and not specific agents.
I wonder if that is left over from testing people. I have major version numbers and my minor version number changes daily, often as a surprise. Sometimes several times a day. So testing people is a bit tricky. But AIs do have stable version numbers and can be specifically compared.
Any paper like this would easily take a year or more to write and go through the submission/review/rebuttal/revision/acceptance process. I don't understand why the models being a year or two old now is worth noting as though it's a clear weakness? What should they do, publish sub-standard results more quickly?
> I don't understand why the models being a year or two old now is worth noting as though it's a clear weakness?
I do think it's a clear weakness. Capabilities are extremely different than they were twelve months ago.
> What should they do, publish sub-standard results more quickly?
Ideally, publish quality results more quickly.
I'm quite open to competing viewpoints here, but it's my impression that academic publishing cycle isn't really contributing to the AI discussion in a substantive way. The landscape is just moving too quickly.
The onus is on you to prove or at least convincingly argue that the results are unlikely to generalize across incremental model releases. In my personal experience, the overly affirming nature seems to have held since GPT-3. What makes you think a newer, larger model would not exhibit this behavior? Beyond "they're more capable"? I'd argue that being more capable doesn't mean less sycophantic.
It's certainly possible some of the new advances (chain-of-thought, some kind of agentic architecture) could lessen or remove this effect. But that's not what the paper was studying! And if you feel strongly about it, you could try to further the discussion with results instead of handwavingly dismissing others' work.
How many people using AI are actually paying for it (outside of people in tech)?
I find the free models are much more psychophantic and have a higher tendency to hallucinate and just make shit up, and I wonder if these are the ones most people are using?
If they’re reaching the same results across a variety of the most popular public models, it doesn’t seem like that big a deal to know if it was Opus 4 or Opus 4.5
Reproducibility is (supposed to be) a cornerstone of science. Model versions are absolutely critical to understand what was actually tested and how to reproduce it.
Even as someone who (wrongly) believed that I had high emotional intelligence, I too was bit by this. Almost a year ago when LLMs were starting to become more ubiquitous and powerful I discussed a big life/professional decision with an LLM over the course of many months. I took its recommendation. Ultimately it turned out to be the wrong decision.
Thankfully it was recoverable, but it really sobered me up on LLMs. The fault is on me, to be clear, as LLMs are just a tool. The issue is that lots of LLMs try to come across as interpersonal and friendly, which lulls users into a false sense of security. So I don't know what my trajectory would have been if I were a teenager with these powerful tools.
I do think that the LLMs have gotten much better at this, especially Claude, and will often push back on bad choices. But my opinion of LLMs has forever changed. I wonder how many other terrible choices people have made because these tools convinced them to make a bad decision.
I think that if you go to an AI for advice and emotional support, it will do what most people will do - tell you what it thinks you want to hear. I am not surprised about this at all, and I do notice that when you veer into these areas, it can do it in a surprisingly subtle and dangerous way.
I try to focus on results. Things like an app that does what you want, data and reports that you need, or technical things like setting up a server, setting up a database, building a website, etc.
I have also found it useful for feedback and advice, but only once I have had it generate data that I can verify. For example, financial analysis or modelling, health advice (again factual based), tax modelling, etc, but again, all based on verifiable data/tables/charts.
I am very surprised on what Claude is capable of, across the entire tech stack: code, sysadmin, system integration, security. I find it scary. Not just speed, but also quality and the mental load is a difference of kind not quantity.
Personal advice on life decisions/relationships ? No way I would go there.
It is also good for me to know that the tools I have built, the data I have gathered, and my thinking approach places me as one of the most intelligent developers and analysts in the world.
That is why you have to always have it ground itself in something. Have it search for relevant research or professional whatever and pull that into context. Otherwise it’s just your word plus its training data.
I had to deal with a close family friend going through alcohol withdrawal and getting checked in at a recovery clinic for detox and used Claude heavily. The first thing I had it do as do that “deep research” around the topic of alcohol addiction, withdrawal, etc… and then made that a project document along with clear guidelines about how it shouldn’t make inferences beyond what it in its context and supporting docs. We also spent a whole session crafting a good set of instructions (making sure it was using Anthropics own guidelines for its model…)
Little differences in prompts make a huge deal in the output.
I dunno. It is possible to use these models for dumping crazy shit you are going through. But don’t kid yourself about their output and aggressively find ways to stomp out things it has no real way to authoritatively say.
I recently found out that Claude's latest model, Sonnet 4.6, scores the highest in Bullsh*tBench[0] (Funny name - I know). It's a recent benchmark that measures whether an LLM refuses nonsense or pushes back on bad choices so Claude has definitely gotten better.
I haven't tried talking to Sonnet much, but Opus 4.6 is very sycophantic. Not in the sense of explicitly always agreeing with you, but its answers strictly conform to the worldview in your questions and don't go outside it or disagree with it.
It _does_ love to explicitly agree with anything it finds in web search though.
(Anthropic tries to fight this by adding a hidden prompt that makes it disagree with you and tell you to go to bed, which doesn't help.)
One mental model I have with LLMs is that they have been the subject of extreme evolutionary selection forces that are entirely the result of human preferences.
Any LLM not sufficiently likable and helpful in the first two minutes was deleted or not further iterated on, or had so much retraining (sorry, "backpropagation") it's not the same as it started out.
So it's going to say whatever it "thinks" you want it to say, because that's how it was "raised".
Fully agree. I wonder in the long term how this will show up. Will every business/CEO do more of what he/they anyway want to do, but now supported by AI/LLMs?
The possibilities in "dangerous" fields are a bit more frightening. A general is much more likely to ask ChatGPT "Do you think this war is a good idea/should I drop a bomb", rather than an actually helpful tool - where you might ask "What are 5 hidden points on favor of/against bombing that one likely has missed".
The more you use AI as a strict tool that can be wrong, the safer. Unfortunately I'm not sure if that helps if the guy bombing your city (or even your president) is using AI poorly, and their decisions affect you.
> Will every business/CEO do more of what he/they anyway want to do, but now supported by AI/LLMs?
Arguably, it already worked that way. The best way to climb the ranks of a 'dictatorial' organization (a repressive government or an average large business) is to always say yes. Adopt what the people from up above want you to use, say and think. Don't question anything. Find silver linings in their most deranged ideas to show your loyalty. The rich and powerful that occupy the top ranks of these structures often hate being challenged, even if it's irrational for their well-being. Whenever you see a country or a company making a massive mistake, you can often trace it to a consequence of this. Humans hate being challenged and the rich can insulate themselves even further from the real world.
What's worrying me is the opposite - that this power is more available now. Instead of requiring a team of people and an asset cushion that lets you act irrationally, now you just need to have a phone in your pocket. People get addicted to LLMs because they can provide endless, varied validation for just about anything. Even if someone is aware of their own biases, it's not a given that they'll always counteract the validation.
Thank you. Yes, I'm going to refrain from airing out my dirty laundry. I made a bad decision, now I'm living with it, and more context doesn't actually change the intent behind my message: these tools are dangerous. Getting better, but still dangerous.
If you use LLMs in a way that the underlying assumption is that it is capable of "thinking" or "caring" then you are going to get burned pretty bad. Because it is an illusion and illusions disappear when they have to bear real weight of reality.
But sadly LLMs push all the right buttons that lead humans into that kind of behavior. And the marketing around LLMs works overtime to reinforce that behavior.
But instead if you ignore all that and use LLMs as a search tool, then you will get positive returns from using it.
> I took its recommendation. Ultimately it turned out to be the wrong decision.
Curious if you think a single person would have helped you make a better decision? Not everything works out. If a friend helped me make a decision I certainly wouldn’t blame them later if it didn’t work out. It’s ultimately my call.
Weird, i am using copilot and it steers me mostly towards self reflection and tries to look at things objectively. It is very friendly and comes across as empathetic, to not hurt your feelings, that is probably baked in to keep the conversation going...
I’m struggling to understand how the advice coming from an LLM is any more or less “good” than advice coming from a human. Or is this less about the “advice” part of LLMs and more about the “personable” part, i.e. you felt more at ease seeking and trusting this kind of advice form an LLM?
It is much easier to share personal feelings with an llm, i found. Also it tried to keep me happy to get the conversation going, but for me it feels mostly 'objective' or the most socially acceptable advice, e. g. keeping a good relationship is more important than trying a new one with someone else because you 'feel something' around them. For me it tried to find out together the sources or causes of that feeling, e.g. you recognize parts of yourself in someone else or in the past you had very good or very bad experiences around an encounter.
I largely agree, I also thought I was smart enough not to be deluded into a false sense of security, but interacting with an LLM is so tricky and slippery that, more often than not you are forced to believe you just solve a problem no one had solve in a hundred years.
My guideline now for interacting with LLM is only to believe the result if it is factual and easily testable, or if I'm a domain expert. Anything else especially if I'm in complete ignorance about the subject is to approach with a high degree of suspicion that I can be led astray by its sycophancy.
Yeah, I think Claude is a lot more logical in that sense, I use it for some therapy sessions myself and it pushes back a bit more than Open AI and Gemini
You always have to be careful with LLMs, but to be fair, I felt like Claude is such a good therapist, at least it is good to start with if you want to unpack yourself. I have been to 3 short human therapist sessions in my life, and I only felt some kind of genuine self-improvement and progress with Claude.
This is needlessly flippant and not really the same thing. Determining progress in a therapy setting is usually a collaborative effort between the therapist and the client. An LLM is not a reliable agent to make that determination.
I didn’t claim that an LLM is that, and I fully agree that it is not. I’m saying that one is inherently one’s own judge of whether one has a problem. You go to a therapist when you feel you have a problem that warrants it. You stop going when you feel you don’t have it anymore. And OP is very likely assessing their progress in the same way. I wasn’t being flippant if the parent was asking a genuine question.
My experience is that it tries to look at your situation in an objective way, and tries to help you to analyse your thoughts and actions. It comes across as very empathetic though, so there can lie a danger if you are easily persuaded into seeing it as a friend.
Hmmmm i didn't know that... so a machine is not human is your point? Look, i know it doesn't try, just like a sorting algo does not try to sort, or an article does not try to convey an opinion and a law does not try to make society more organized.
That is so reductive of an analysis that it is almost worthless. Technically true, but very unhelpful in terms of using an LLM.
It is a first principle though so it helps to “stir the context windows pot” by having it pull in research and other shit on the web that will help ground it and not just tell you exactly what you prompt it to say.
Claudes have lots of empathy. The issue is the opposite - it isn't very good at challenging you and it's not capable of independently verifying you're not bullshitting it or lying about your own situation.
But it's better than talking to yourself or an abuser!
It's about the same as talking to yourself, LLMs simply agree with anything you say unless it is directly harmful. Definitely agree about talking to an abuser, though.
Sometimes people indeed just need validation and it helps them a lot, in that case LLMs can work. Alternatively, I assume some people just put the whole situation into words and that alone helps.
But if someone needs something else, they can be straight up dangerous.
> It's about the same as talking to yourself, LLMs simply agree with anything you say unless it is directly harmful.
They have world knowledge and are capable of explaining things and doing web searches. That's enough to help. I mean, sometimes people just need answers to questions.
In one way it's potentially worse than talking to yourself. Some part of you might recognize that you need to talk to someone other than yourself; an LLM might make you feel like you've done that, while reinforcing whatever you think rather than breaking you out of patterns.
Also, LLMs can have more resources and do some "creative" enabling of a person stuck in a loop, so if you are thinking dangerous things but lack the wherewithal to put them into action, an LLM could make you more dangerous (to yourself or to others).
Using an LLM for therapy is like using an iPad as an all-purpose child attention pacifier. Sure, it’s convenient. Sure there’s no immediate harm. Why a stressed parent would be attracted to the idea is obvious… and of course it’s a terrible idea.
Don’t call them therapy sessions. They kind of look like it but ultimately these are smoke blowing machines, which is very far from what a therapist would do.
Six decades later and we're still trying to explain to people the same things[1]:
> Some of ELIZA's responses were so convincing that Weizenbaum and several others have anecdotes of users becoming emotionally attached to the program, occasionally forgetting that they were conversing with a computer. Weizenbaum's own secretary reportedly asked Weizenbaum to leave the room so that she and ELIZA could have a real conversation. Weizenbaum was surprised by this, later writing: "I had not realized ... that extremely short exposures to a relatively simple computer program could induce powerful delusional thinking in quite normal people."
I also used it for advice on a massive personal decision, but I specifically asked it to debate with me and persuade me of the other side. I specifically prompted it for things I am not thinking about, or ways I could be wrong.
It was extremely good at the other side too. You just have to ask. I can imagine most people don't try this, but LLMs literally just do what you ask them to. And they're extremely good and weighing both sides if that's what you specifically want.
So who's fault is it if you only ask for one side, or if the LLM is too sycophantic? I'm not sure it's the LLMs fault actually.
>"'And it is also said,' answered Frodo: 'Go not to the Elves for counsel, for they will say both no and yes.'
>"'Is it indeed?' laughed Gildor. 'Elves seldom give unguarded advice, for advice is a dangerous gift, even from the wise to the wise, and all courses may run ill...'"
This is the only way you should solicit personal advice from an LLM.
You're essentially summoning a character to role-play with. Just like with esoteric evocation, it's very easy to summon the wrong aspect of the spirit. Anthropic has a lot to say about this:
Unfortunately (after reading your links) all of the control surfaces for mitigating spirit summoning seem to be in the model training, creation and tuning not something you can change meaningfully through prompting.
Perhaps the LLM itself, rather than the role model you created in one particular chat conversation or another, is better understood to be the “spirit.”
As a non-coder who only chats with pre existing LLMs and doesn’t train or tune them, I feel mostly powerless.
As I understand it, it's more that the training (and training data set) bake in the concept attractor space (https://arxiv.org/abs/2601.11575). So the available characters are fixed, yes, and some are much stronger attractors than others. But we still have a fair amount of control over which archetype steps into the circle. As an aside, this is also why jailbreaking is fundamentally unsolved. It's not difficult to call the characters with dark traits. They're strong attractors, in spite of (or because of?) the effort put into strengthening the pull of the Assistant character.
I am polite when using AI, not because I mistake it for a human, but because I'm deliberately keeping it in the "professional colleague" persona. Tell it to push back, and then thank it for something it finds in your error. I may put a small self-deprecating joke in from time to time. It keeps the "mood" correct.
Another way you can think of it is that when you're talking to an AI, you're not talking to a human, you're talking to distillation of humanity, as a whole, in a box. You want to be selective in what portion of humanity you are leading to be dominant in a conversation for some purpose. There's a lot in there. There's a lot of conversations where someone makes a good critical point and a flamewar is the response. A lot of conversations where things get hostile. I'm sure the subsequent RHLF helps with that, but it doesn't hurt anything to try to help it along.
I see people post their screenshots of an AI pushing back and asking the user to do it or some other AI to do it, and while I'm as amused as the next person, I wonder what is in their context window when that happens.
Agreed, putting effort into my side of the role-play almost always improves the model's responses. The attention required to do that also makes it more likely that I'll notice when the conversation first starts going off the rails: when it hits the phase transition (https://arxiv.org/abs/2508.01097). It does still seem important to start new chats regularly, regardless of growing context sizes.
> you're talking to distillation of humanity, as a whole, in a box.
This is an aside, but my impression is that it is a very selective and skewed distillation, heavily colored by English-language internet discourse and other lopsided properties of its training material, and by whoever RLHF’d it. Relatively far away from being representative of the whole of humanity.
Similar approach works for me. But then I also have a separate checks at the end of the session basically questioning the premise and logic used for most things except brainstorming, where I allow more leeway. You can ask to be challenged and challenged effectively, but now I wonder if people do that.
It feels like I'm fighting uphill battle when it comes to bouncing ideas off of a model. I'll set things up in the context with instructions similar to. "Help me refine my ideas, challenge, push back, and don't just be agreeable." It works for a bit but eventually the conversation creeps back into complacency and syncophancy. I'll check it too by asking "are you just placating me?" the funny thing is that often it'll admit that, yes, it wasn't being very critical, and then procede to over correct and become a complete contrarian. and not in a way that's useful either. very frustrating. I've found that Opus 4.6 is worse about this than 4.5. 4.5 does a better job IMO of following instructions and not drifting into the mode where it acts like everything i say is a grand revelation from up high.
> I'll check it too by asking "are you just placating me?" the funny thing is that often it'll admit that, yes, it wasn't being very critical, and then procede to over correct and become a complete contrarian. and not in a way that's useful either.
It's not admitting anything. Your question diverts it down a path where it acts the part of a former sycophant who is now being critical, because that question is now upstream of its current state.
Never make the mistake of asking an LLM about its intentions. It doesn't have any intentions, but your question will alter its behaviour.
Many other humans are .... Not very available - certainly many shut down when conversations reach a certain level of depth or require great focus or introspection..
I'd say these days the norm is to not simply shut down, but to become irrevocably and insidiously hostile, the moment someone hints at the existence of such a thing as "ground truth", "subjective interpretation", "being right or wrong" - or any of the bits and bobs that might lead one to discover the proper scary notion, "consensus reality".
"What do you mean social reality is a constructed by the consensus of the participants? Reality is what has been drilled into my head under threat of starvation! How dare you exist!", et cetera. You've heard it translated into Business English countless times.
They are deathly afraid of becoming aware of their own conditioned state of teleological illiteracy - i.e. how they are trained to know what they are doing, but never why they are doing it. It's especially bad with the guys who cosplay US STEM gang.
One is not permitted a position of significance in this world without receiving this conditioning, and I figure it's precisely this global state of cognitive disavowal which props up the value of the US dollar - and all sorts of other standees you might've recently interacted with as if they're not 2D cutouts (metaphorical ones! metaphorical!).
PSA: Look up "locus of control" and "double bind". Between those two, you might be able to get a glimpse of what's going on - but have some sort of non-addictive sedative handy in case you do.
In addition to availability, usually because you want to take advantage of the knowledge that is baked into the models, which for all its flaws still vastly exceeds the knowledge of any single human.
oh i do as well. I think of the LLM as another tool in the toolbox, not a replacement for interactions. There is something different about having a rubber duck as a service though.
OK, I'll bite the artillery shell: I don't mean to dismiss you or what you are saying; in fact I strongly relate - wouldn't it be nice to be able to hash things out with people and mutually benefit from both the shared and the diverging perspectives implied in such interaction? Isn't that the most natural thing in the world?
Unfortunately these days this sounds halfway between a very privileged perspective and a pie in the sky.
When was the last time a person took responsibility for the bad outcome you got as a direct consequence of following their advice?
And, relatedly, where the hell do you even find humans who believe in discursive truth-seeking in 2026CE?
Because for the last 15 years or so I've only ever ran into (a) the kind of people who will keep arguing regardless if what they're saying is proven wrong; (b) and their complementaries, those who will never think about what you are saying, lest they commit to saying anything definite themselves, which may hypothetically be proven wrong.
Thing is, both types of people have plenty to lose; the magic wordball doesn't. (The previous sentence is my answer to the question you posited; and why I feel the present parenthesized disclaimer to be necessary, is a whole next can of worms...)
Signs of the existence of other kinds of people, perhaps such that have nothing to prove, are not unheard of.
But those people reside in some other layer of the social superstructure, where facts matter much less than adherence to "humane", "rational" not-even-dogmas (I'd rather liken it to complex conditioning).
But those folks (because reasons) are in a position of power over your well-being - and (because unfathomables) it's a definite faux pas to insist in their presence that there are such things as facts, which relate by the principles of verbal reasoning.
Best you could get out of them is the "you do you", "if you know you know", that sort of bubble-bobble - and don't you dare get even mildly miffed at such treatment of your natural desire to keep other humans in the loop.
I genuinely do not understand what u are saying. Because reasons, because unfathomables? Everyone in last 15 years has been an npc? I have had countless deep conversations with people and i am an uber introvert.
This reads like someone who is deep into their specific pov. You cannot hope to have a meaningful conversation if you yourself are not willing to concede a point.
To the op u are replying too, arguing with people can have real consequences if u say something stupid or carelessly. There is a another human there. With a machine, u are safe. At least u feel safe.
When you start hearing things like “you do you” or “if you know you know” it means that you went way too far. That’s a sign of discomfort.
If you make uncomfortable, you won’t get diverging perspectives. People will agree to anything to get out of a social situation that makes them uncomfortable.
If your goal is meaningful conversation, you may want to consider how you make people feel.
Believe me (or don't), I always do. Even when this precludes a necessary conversation from happening. Even when the other party doesn't give a fuck about how they make others feel.
After all, if they're making me uncomfortable, surely there's something making them uncomfortable, which they're not being able to be forthright about, but with empathy I could figure it out from contextual cues, right?
>People will agree to anything to get out of a social situation that makes them uncomfortable.
That's fine as long as they have someone to take care of them.
In my experience, taking into account the opinions of such people has been the worst mistake of my life. I'm still working on the means to fix its consequences, as much as they are fixable at all.
"Doing whatever for the sake of avoiding mild discomfort" is cowardice, laziness, narcissism - I'm personally partial to the last one, but take your pick. In any case, I consider it a fundamentally dishonest attitude, and a priori have no wish to get along (i.e. become interdependent) with such people.
Other than that, I do agree with your overall sentiment and the underlying value system; I'm just not so sure any more that it is in fact correct.
> In my experience, taking into account the opinions of such people has been the worst mistake of my life. I'm still working on the means to fix its consequences, as much as they are fixable at all.
This sounds very cryptic. Can you give an example?
Believe me (or don't), I always do. Even when this precludes a necessary conversation from happening. Even when the other party doesn't give a fuck about how they make others feel.
After all, if they're making me uncomfortable, surely there's something making them uncomfortable, which they're not being able to be forthright about, but with empathy I could figure it out from contextual cues, right?
>People will agree to anything to get out of a social situation that makes them uncomfortable.
That's fine as long as they have someone to take care of them.
In my experience, taking into account the opinions of such people has been the worst mistake of my life. I'm still working on the means to correct its consequences.
"Doing whatever for the sake of avoiding mild discomfort" is cowardice, laziness, narcissism - I'm personally partial to the last one, but take your pick. In any case, I see it as a way of being which is taught to people; and one which is fundamentally dishonest and irresponsible.
Other than that, I do agree with your overall sentiment and the underlying value system; I'm just not so sure any more that it is in fact correct.
Gemini seems to be fairly good at keeping the custom instructions in mind. In mine I've told it to not assume my ideas are good and provide critique where appropriate. And I find it does that fairly well.
Same. This works fine for Claude in my experience. My user prompt is fairly large and encourages certain behaviours I want to see, which involves being critical and considering the strengths and weaknesses of ideas before drawing conclusions. As someone else mentioned, there does seem to be a phenomenon where saying DO NOT DO X causes a sort of attention bias on X which can lead to X occurring despite the clear instructions. I've never empirically tested that, I've just noticed better results over the years when telling it what paths to stick to rather than specific things not do to.
That happens with humans too :) It's why positive feedback that draws attention to the behavior you want to encourage often works better. "Attention" is lower level and more fundamental than reasoning by syllogism.
I will admit that I was very pleasantly surprised by gemini lately. I was away from my PC and tried it on a whim for a semi-random consumer question that led into smaller rabbit hole. It seemed helpful enough and focused on what I tried to get while still pushing back when my 'solutions' seemed out of whack.
That's because you need actual logic and thought to be able to decide when to be critical and when to agree.
Chatbots can't do that. They can only predict what comes next statistically. So, I guess you're asking if the average Internet comment agrees with you or not.
I'm not sure there's much value there. Chatbots are good at tasks (make this pdf an accessible word document or sort the data by x), not decision making.
> I'm not convinced that "actual logic and thought" aren't just about inferring what comes next statistically based on experience.
Often they are the exact opposite. Entire fields of math and science talk about this. Causation vs correlation, confirmation bias, base rate fallacy, bayesian reasoning, sharp shooter fallacy, etc.
All of those were developed because “inferring from experience” leads you to the wrong conclusion.
Bayesian reasoning is just another algorithm for predicting from experience (aka your prior).
I took the GP to be making a general point about the power of “next x prediction” rather than the algorithm a human would run when you say they are “inferring from experience”. (I may be assuming my own beliefs of course.)
Eg even LeCun’s rejection of LLMs to build world models is still running a predictor, just in latent space (so predicting next world-state, instead of next-token).
And of course, under the Predictive Processing model there is a comprehensive explanation of human cognition as hierarchical predictors. So it’s a plausible general model.
> under the Predictive Processing model there is a comprehensive explanation of human cognition as hierarchical predictors
It’s plausible!
But keep in mind humans have been explaining ourselves in terms of the current most advanced technology for centuries. We used to be kinda like clockwork, then a bit like a steam engine, then a lot like computers, and now we’re just like AI.
That’s why you blow a gasket or fuse, release some steam, reboot your life, do brain dump, feel like a cog in the machine, get your wires crossed, etc
Is this just Internet smart contrarianism or a real thing? Are logic gates in a digital circuit just behaving statistically according to their experience?
You know, you might really enjoy consumer behaviour. When you get into the depths of it, you’ll end up running straight into that idea like you’re doing a 100 metre dash in a 90 metre gym. It’s quite interesting how arguably the best funded group under the psychology umbrella runs directly into this. One of my favourite examples is how heuristics will lead otherwise reasonable people to make decisions that are not in their interest.
Communicating is usually about inferring. I dont think token to token. And I don’t think “well statistically I could say ‘and’ next but I will say ‘also’ instead to give my speech some flash”. If I decided on swapping a word I would have made my decision long ago, not in the moment. Thought and logic are not me pouring through my brain finding a statistical path to any answer. Often I stop and say “I dont know”.
Because it's an outmoded cliche that never held much philosophical weight to begin with and doesn't advance the discussion usefully. "It's a stochastic parrot" is not a useful predictor of actual LLM capabilities and never was. Last year someone posted on HN a log of GPT-5 reverse engineering some tricky assembly code, a challenge set by another commentator as an example of "something LLMs could never do". But here we are a year later still wading through people who cannot accept that LLMs can, in a meaningful sense, "compute".
It’s entirely useful discussion because as soon as you forget that it’s not really having a conversation with you, it’s a deep dive into delusion that you’re talking to a smart robot and ignoring the fact that these smart robots were trained on a pile of mostly garbage. When I have a conversation with another human, I’m not expecting them to brute force an answer to the topic. As soon as you forget that Llms are just brute forcing token by token then people start living in fantasy land. The whole “it’s not a stochastic parrot” is just “you’re holding it wrong”.
Its not that LLMs are stochastic parrots and humans are not. Its that many humans often sail through conversations stochastic parroting because they're mentally tired and "phoning it in" - so there are times when talking to the LLM, which has a higher level of knowledge, feels more fruitful on a topic than talking to a human who doesn't have the bandwidth to give you their full attention, and also lack the depth and breadth of knowledge. I can go deep on many topics with LLMs that most humans can't or won't keep up on. In the end, I'm really only talking to myself most of the time in either case, but the LLM is a more capable echo, and it doesn't tire of talking about any topic - it can dive deep into complex details, and catching its hallucinations is an exercise in itself.
No. It's quite a useful thing to understand So, what, you have us believe it is a sentient, thinking, kind of digital organism and you would have us not believe that it is exactly what it is? Being wrong and being unimaginative about what can be achieved with such a "parrot" is not the same as being wrong about it be a word predictor. If you don't think, you can probably ask an LLM and it will even "admit" this fact. I do agree that it has become considered to be outmoded to question anything about the current AI Orthodox.
First off, "not adequately described as a mere token-predictor" and "not sentient" are entirely separate things.
I can't speak for anyone else, but what I feel when I read yet another glib "it's just a stochastic parrot, of course it isn't doing anything that deserves to be called reasoning" take is much more like bored than it is like upset.
Today's LLMs are in some sense "just predicting tokens" in some sense. Likewise, human brains are in some sense "just shuttling neurotransmitters and electrical impulses around" in some sense. Neither of those tells you what the thing can actually do. To figure that out, you have to look at what it can do.
Today's best LLMs can do about as well as the best humans on problems from the International Mathematical Olympiad and occasionally solve easyish actual mathematical research problems. They write code about as well as a junior software developer (better in some ways, worse in others) but much faster. They write prose about as well as an average educated person (but with some annoying quirks that are annoying mostly because they are the same quirks over and over again).
If it pleases you to call those things "thinking" then you can. If it pleases you to call them "stochastic parroting" then you can. They are the same things either way. They are not, on the face of it, very much like "just repeating things the machine has already seen", or at least not more like that than a lot of things intelligent human beings do that we don't usually describe that way.
If you want to know whether an LLM can do some particular thing -- do your job well enough for your boss to fire you, write advertising copy that will successfully sell products, exterminate the human race, whatever -- then it's not enough to say "it's just remixing what it's seen on the internet, therefore it can't do X" unless you also have good reason to believe that that thing can't be done by just "remixing what's on the internet" (in whatever sense of "remixing" the LLM is doing that). And it's turning out that lots of things can be done that way that you absolutely wouldn't have predicted five years ago could be done that way.
It seems to me that this should make us very cautious about saying "they can't do X because all they can do is regurgitate a combination of things they've seen in training".
(My own view, not that there's any reason why anyone should care what I-in-particular think, is a combination of "what they're doing is less parroting than you might have thought" and "you can do more by parroting than you might have thought".)
So, anyway, this particular instance of the stochastic-parrot argument started when someone said: of course the AIs are yes-men, because figuring out when to agree and when not to requires actual logic and thought and the LLMs don't have either of those things.
Is it really clear that deciding whether or not to agree when someone says "I think maybe I should break up with my girlfriend" or "I've got this amazing new theory of physics that the establishment is stupidly dismissing" requires more logic and thought than, say, gold-medal performance on IMO problems? It certainly isn't clear to me. Having done a couple of International Mathematical Olympiads myself in my tragically unmisspent youth, I can assure you that solving their problems requires quite a bit of logic and thought, at least for humans. It may well be harder to give a good answer to "should I leave my job?", but it's not exactly "logic and thought" that it needs more of.
Someone reported that Claude is much less yes-man-ish than Gemini and ChatGPT. I don't know whether that's true (though it wouldn't surprise me) but: suppose it is; do you want that to oblige you to say that yes, actually, Claude really thinks logically, unlike Gemini and ChatGPT? I don't think you do. And if not, you want to avoid saying "duh, of course, you can't avoid being a yes-man without actually thinking and reasoning, and we all know that LLMs can't do those things".
I wont touch how profoundly i disagree with everything you said on reasoning (u clearly already have it figured out) but a fun test i have done with most of the big models is to give it some text input, maybe a short story, and have it rate it. That is, the prompt is, rate this from 1-10.
For Gemini and gpt, it almost always will give very similar scores for everything. As long as grammar isnt off u cannot get below a 7.
X ai on the other hand will rarely give anything above a 7.
Now when u prompt with, rate 1-10 with 5 being average, all the sudden the scores of openai and gemini drop and x ai remains roughly the same.
All of them will eventually give you a 10 if u keep making tiny edits “fixing” whatever they complain about.
Humans do not do this. Or more specifically, my experience with humans.
'admit' isn't really the right word for that... the fact that it was placating you wasn't true until you prompted it to say so. Unlike a person who has an 'internal emotional state' independent of what they say that you can probe by asking questions.
'admit' is anthropomorphizing the behavior, sure. The point is that sometimes the model's response will tighten, flag things that were overly supportive or what not. Sometimes it wont, it'll state that previous positions are still supported and continue to press it. Its not like either response is 'correct' but it can alter the rest of the responses in ways that are useful.
The article's main idea is that for an AI, sycophancy or adversarial (contrarian) are the two available modes only. It's because they don't have enough context to make defensible decisions. You need to include a bunch of fuzzy stuff around the situation, far more than it strictly "needs" to help it stick to its guns and actually make decisions confidently
I think this is interesting as an idea. I do find that when I give really detailed context about my team, other teams, ours and their okrs, goals, things I know people like or are passionate about, it gives better answers and is more confident. but its also often wrong, or overindexes on these things I have written. In practise, its very difficult to get enough of this on paper without a: holding a frankly worrying level of sensitive information (is it a good idea to write down what I really think of various people's weaknesses and strengths?) and b: spending hours each day merely establishing ongoing context of what I heard at lunch or who's off sick today or whatever, plus I know that research shows longer context can degrade performance, so in theory you want to somehow cut it down to only that which truly matters for the task at hand and and and... goodness gracious its all very time consuming and im not sure its worth the squeeze
Use positive requests for behavior. For some reason, counter prompts "Don't do X" seems to put more attention on X than the "Don't do." It's something like target fixation, "Oh shit I don't want to hit that pothole..." bang
This is a well known problem in these kind of systems. I’m not 100% on what the issue is mechanically but it’s something like they can only represent the existence of things and not non-existence so you end up with a sort of “don’t think of the pink elephant” type of problem.
Isn't it just that, in the underlying text distribution, both "X" and "don't do X" are positively correlated with the subsequent presence of X? I've never seen that analysis run directly but it would surprise me if it weren't true.
So, there's things you're fighting against when trying to constrain the behavior of the llm.
First, those beginning instructions are being quickly ignored as the longer context changes the probabilities. After every round, it get pushed into whatever context you drive towards. The fix is chopping out that context and providing it before each new round. something like `<rules><question><answer>` -> `<question><answer><rules><question>`.
This would always preface your question with your prefered rules and remove those rules from the end of the context.
The reason why this isn't done is because it poisons the KV cache, and doing that causes the cloud companies to spin up more inference.
I usually put “do not praise me, do not use emojis, I just want straight answers” something along those lines and it’s been surprisingly effective. Though it helps I can’t run particularly heavy duty models/don't carry on the “conversation” for super long durations.
>"Help me refine my ideas, challenge, push back, and don't just be agreeable."
This is where you're doing it wrong.
If your LLM has a problem being more agreeable than you want, prompt it in a way that makes being agreeable contrary to your real intentions.
"there are bugs and logic problems in this code" "find the strongest refutation of this argument" "I don't like this plan and need to develop a solid argument against it"
Asking for top ten lists is a good method, it will rarely not come up with anything but you can go back and forth and refine until it's 10 ten reasons why your plan is bad are all insubstantial nonsense then you've made progress
You're not wrong and you're not crazy. In fact, you are absolutely right! It is not just These things are not just casual enablers. They are full-on palace sycophants following the naked emperor showering him with praise for his sartorial elegance. /s
That’s because the model isn’t actually thinking, pushing back, and challenging your ideas. It’s just statistically agreeing with you until it reaches too wide of a context. You’re living in the delusion that it’s “working” or having a “conversation” with you.
How is conceptualizing what the model is doing as having a conversation any different from any other abstraction? “No, the browser isn’t downloading a file. The electrons in the silicon are actually…”
There are people with a philosophical objection to using everyday words to describe LLM interactions for various reasons, but commonly because they're worried stupid people will confuse the LLM for a person. Which, I suppose stupid people will do that, but I'm not inventing a parallel language or putting a * next to each thing which means "this, but with an LLM instead of a person"
Maybe it's not so sensible to offload the responsibility of clear thinking to AI companies?
How is a chatbot supposed to determine when a user fools even themselves about what they have experienced?
What 'tough love' can be given to one who, having been so unreasonable throughout their lives - as to always invite scorn and retort from all humans alike - is happy to interpret engagement at all as a sign of approval?
> How is a chatbot supposed to determine when a user fools even themselves about what they have experienced?
And even if it _could_, note, from the article:
> Overall, the participants deemed sycophantic responses more trustworthy and indicated they were more likely to return to the sycophant AI for similar questions, the researchers found.
The vendors have a perverse incentive here; even if they _could_ fix it, they'd lose money by doing so.
With AI, I often like to act like a 3rd party who doesn't have skin in the game and ask the AI to give the strongest criticisms of both sides. Acting like I hold the opposite position as I truly hold can help sometimes as well. Pretending to change my mind is another trick. The idea is to keep the AI from guessing where I stand.
> Acting like I hold the opposite position as I truly hold can help sometimes as well.
I find this helps a lot. So does taking a step back from my actual question. Like if there's a mysterious sound coming from my car and I think it might be the coolant pump, I just describe the sound, I don't mention the pump. If the AI then independently mentions the pump, there's a good chance I'm on the right track.
Being familiar with the scientific method, and techniques for blinding studies, helps a lot, because this is a lot like trying to not influence study participants.
I asked ChatGPT if it was a good idea to buy a very old VW diesel van with a broken catalytic converter and it just kept blabbering on how I should chase my dreams and what not... The sycophancy comes at everyone else's expense.
I think the problem stems from the fact that we have a number of implicit parameters in our heads that allow us to evaluate pros and cons but, unless we communicate those parameters explicitly, the AI cannot take them into account. We ask it to be "objective" but, more and more, I'm of the opinion that there isn't such a thing as objectivity, what we call objectivity is just shared subjectivity; since the AI doesn't know whose shared subjectivity we fall under, it cannot be really objetive.
I tend to use one of these tricks if not both:
- Formulate questions as open-ended as possible, without trying to hint at what your preference is.
- Exploit the sycophantic behaviour in your favour. Use two sessions, in one of them you say that X is your idea and want arguments to defend it. In the other one you say that X is a colleague's idea (one you dislike) and that you need arguments to turn it down. Then it's up to you to evaluate and combine the responses.
If the algorithm (whatever it is) evaluates its own output based on whether or not the user responds positively, then it will over time become better and better at telling people what they want to hear.
It is analogous to social media feeding people a constant stream of outrage because that's what caused them to click on the link. You could tell people "don't click on ragebait links", and if most people didn't then presumably social media would not have become doomscrolling nightmares, but at scale that's not what's likely to happen. Most people will click on ragebait, and most people will prefer sycophantic feedback. Therefore, since the algorithm is designed to get better and better at keeping users engaged, it will become worse and worse in the more fundamental sense. That's kind of baked into the architecture.
I had exactly this between two LLMs in my project. An evaluator model that was supposed to grade a coaching model's work. Except it could see the coach's notes, so it just... agreed with everything. Coach says "user improved on conciseness", next answer is shorter, evaluator says yep great progress. The answer was shorter because the question was easier lol.
I only caught it because I looked at actual score numbers after like 2 weeks of thinking everything was fine. Scores were completely flat the whole time.
Fix was dumb and obvious — just don't let the evaluator see anything the coach wrote. Only raw scores. Immediately started flagging stuff that wasn't working. Kinda wild that the default behavior for LLMs is to just validate whatever context they're given.
AI being a Yes-Man is slowly sabotaging it's own answers, because it negatively impact the user's decision. Yes/No are equally important, within a coherent context, for objective reasons. But being supported in the wrong direction is a castastrophe multiplier, down the road. The AI should be neutral, doubtful at times.
There is a striking data visualization showing the breakup advice trend over 15 years on Reddit. You can see the "End relationship" line spike as AI and algorithmic advice take over:
More interesting, IMO, is the general trend that started long before LLMs. The fact that "dump them" is the standard answer to any relationship question is a meme by now. The LLMs appear to be doing exactly what one would expect them to be doing based on their training corpus.
"There is more than one fish in the sea" has been relationship advice for centuries. It might be about being dumped, but I've also thought it useful for considering dumping somebody too.
No, that's not it. We're talking about posts like "we had a silly little quarrel about something that would need fifteen minutes to clear up and make both happy if we both just try to adult a bit" and commenters being adamant that deleting gym and facebooking up and so on is clearly the only choice. Most of said commenters probably not being in any position to give advice on relationships to others.
I see this being said often but I don't understand.
A lot of people posting there are young and may well be in their first relationship. It makes sense for them to ask a question in the community they spend their most time in - which is reddit
Most people overshare on reddit and it's completely unrelated to the seriousness of the situation.
It's also a meme that people will ask the dumbest, most trivial interpersonal conflict questions on Reddit that would be easily solved by just talking to the other person. E.g. on r/boardgames, "I don't like to play boardgames but my spouse loves them, what can I do?" or "someone listens to music while playing but I find it distracting, what can I do?" (The obvious answer of "talk to the other person and solve it like grownups" is apparently never considered).
On relationship advice, it often takes the form "my boy/girlfriend said something mean to me, what shall I do?" (it's a meme now that the answer is often "dump them").
This is the correct take. The advice preceded the LLM boom. They were trained on the 'dump them' advice and proceeded to reinforce the take. So why did the relationship advice change dramatically? I speculate attribution to the disinformation campaigns during this time. They were and still are grossly underestimated.
In my local(?) community (like in my city, not my industry) there is a saying "if you had to ask for relationship advice, then you probably should break up".
There is some rationale to that. People tend to hold onto relationships that don't lead anywhere in fear of "losing" what they "already have". It's probably a comfort zone thing. So if one is desperate enough to ask random strangers online about a relationship, it's usually biased towards some unresolvable issue that would have the parties better of if they break up.
> So if one is desperate enough to ask random strangers online about a relationship
I'd me more inclined to ask random strangers on the internet than close friends...
That said, when me and my SO had a difficult time we went to a professional. For us it helped a lot. Though as the counselor said, we were one of the few couples which came early enough. Usually she saw couples well past the point of no return.
So yeah, if you don't ask in time, you will probably be breaking up anyway.
Most people engage in romantic relationships because they'd like to find someone to marry and settle down with. Nothing but respect for the people who've thought it through and decided that's not for them, but what's much more common is failing to think it through or worrying it would be awkward/scary/"cringe" to take their relationship goals seriously.
That's what people are pointing to when they talk about relationships not "leading anywhere". If you want to be married in 5-10 years, and you're 2 years into an OK relationship with someone you don't want to marry, it's going to suck to break up with them but you have to do it anyway.
Maybe I'm too much of a hopeless romantic, but from my perspective and experience, when someone is good for you, you'll fight for that relationship regardless of what others say, and conversely when you're in a situation where your actively asking and willing to consider "leave" from someone who isn't a very close friend or a therapist as applicable, then it's likely you're looking for external validation for what you've already essentially decided.
Wait, other people don’t make decision trees and mind maps and pro/con lists and consult chatbots before making decisions? Are they just flying through life by the seat of their pants? That doesn’t seem like a very solid framework for achieving desired outcomes.
The idea that asking implies a yes is actually a pretty common logical fallacy. In relationship science, we call this "Relational Ambivalence" and its a completely normal part of any longterm commitment.
That's a good point. If an AI respond to a "what should I get my boyfriend for Christmas?" with a "You should leave him", that's a very different issue.
No, but it is an indication of brain-rot to make a question seriously and also to think that it means the conclusion is foregone. It is an advent of our childlike current generations. Of course, the moment anything becomes difficult or unpleasant, one should quit, apparently. Surely, this kind of resiliency is what got humanity so far.
I didn't imply it's a "foregone conclusion", but just said it's an indication - in the sense of increasing the likelihood. Just like a person asking an AI "what does it feel like to bleed out?" could be them researching for a novel, but is nevertheless an indication of a potential serious issue.
I am glad I found this article, as this is a serious issue with AI. Two years ago, I started using AI for studying and also for some personal matters - things you can't talk about with your friends. It turned out that AI always takes your side and makes you feel good. Sometimes, you know what you did was not the best thing, but AI takes your side and you feel good. With AI, people might feel less lonely, they think. But it is actually the start of not connecting with people. It should be a tool that we use for certain reasons, not a tool that drives us. Lets talk to real people and connect.
There is a fine line between "following my instructions" (is what I want it to do) vs "thinking all I do is great" (risky, and annoying).
A good engineer will also list issues or problems, but at the same time won't do other than required because (s)he "knows better".
The worst is that it is impossible to switch off this constant praise. I mean, it is so ingrained in fine tuning, that prompt engineering (or at least - my attempts) just mask it a bit, but hard to do so without turning it into a contrarian.
But I guess the main issue (or rather - motivation) is most people like "do I look good in this dress?" level of reassurance (and honesty). It may work well for style and decoration. It may work worse if we design technical infrastructure, and there is more ground truth than whether it seems nice.
Yeah, and if you ask it to be critical specifically to get a different perspective or just to avoid this bias, it'll go over the top in the opposite direction.
This is imo currently the top chatbot failure mode. The insidious thing is that it often feels good to read these things. Factual accuracy by contrast has gotten very good.
I think there's a deeper philosophical dimension to this though, in that it relates to alignment.
There are situations where in the grand scheme of things the right thing to do would be for the chatbot to push back hard, be harsh and dismissive. But is it the really aligned with the human then? Which human?
Humans do this too though. I have close friends that ask for advice. Sometimes if I know there’s risk in touchy subjects I will preface with “do you want my actual advice, or just looking for a sounding board”
I’ve seen firsthand people have lost friends over honesty and telling them something they don’t want to hear.
It’s sad really. I don’t want friends that just smile to my face and are “yes-men” either.
Interestingly, you can simply tell models to not be sycophantic and they'll listen.
Claude is almost annoyingly good at pushing back on suggestions because my global CLAUDE.md file says to do so. I rarely get Claude "you're absolutely right"ing me because I tell it to push back.
I built this benchmark this month: https://github.com/lechmazur/sycophancy. There are large differences between LLMs. There are large differences between LLMs. For example, Mistral Large 3 and GPT-4.1 will initially agree with the narrator, while Gemini will disagree. I swap sides, so this is not about possible viewpoint bias in the LLMs. But another benchmark shows that Gemini will then change its view very easily in a multi-turn conversation while Kimi K2.5 or Grok won't: https://github.com/lechmazur/persuasion.
Avoiding this generally needs to be the main consideration when writing prompts.
When appropriate, explicitly tell it to challenge your beliefs and assumptions and also try to make sure that you don't reveal what you think the answer is when making a question, and also maybe don't reveal that you are involved. Hedge your questions, like "Doing X is being considered. Is it a viable plan or a catastrophic mistake? Why?". Chastise the LLM if it's unnecessarily praising or agreeable. ask multiple LLMs. Ask for review, like "Are you sure? What could possibly go wrong or what are all possible issues with this?"
This is a skill in life with people as much as it is with LLMs. One should always question everything and build strongman arguments for one’s self. Using a pros and cons approach brings it back to reality in most cases, especially when it comes to _serious matters_.
It’s less about “challenge my thinking” and more about playing it out in long tail scenarios, thought exercises, mental models, and devils advocate.
Overly, compared to what? Most people I know would be hard pressed to give either accurate information or even honest opinions when specifically asked. People want to be liked and people want to like people for reasons that have little to do with accuracy or honesty.
For me the framing is critical - what is the model saying yes to? You can present the same prompt with very different interpretations (talk me into this versus talk me out of it). The problem is people enter with a single bias and the AI can only amplify that.
In coding I’ll do what I call a Battleship Prompt - simply just prompt 3 or more time with the same core prompt but strong framing (eg I need this done quickly versus come up with the most comprehensive solution). That’s really helped me learn and dial in how to get the right output.
I believe this is what they call yasslighting: the affirmation of questionable behavior/ideas out of a desire to be supportive. The opposite of tough love, perhaps. Sometimes the very best thing is to be told no.
Not that surprising.
If you optimize for a pleasant interaction, you often get agreement instead of correction.
The question is whether we actually want advice systems to feel good, or to be right.
So at this point I think it's pretty obvious that RLHFing LLMs to follow instructions causes this.
I'm interested in a loop of ["criticize this code harshly" -> "now implement those changes" -> open new chat, repeat]: If we could graph objective code quality versus iterations, what would that graph look like? I tried it out a couple of times but ran out of Claude usage.
Also, how those results would look like depending on how complete of a set of specs you give it.
This needs to be taken in context. In my view, AI definitely gives better advice than friends, acquaintances, or colleagues (at least in the US culture). But the advice from parents is still the most valuable.
> They also included 2,000 prompts based on posts from the Reddit community r/AmITheAsshole, where the consensus of Redditors was that the poster was indeed in the wrong
Holy shit, then it's _very_ bad, because AmITheAsshole is _itself_ overly-agreeable, and very prone to telling assholes that they are not assholes (their 'NAH' verdict tends to be this).
More seriously, why the hell are people asking the magic robot for relationship advice? This seems even more unwise than asking Reddit for relationship advice.
> Overall, the participants deemed sycophantic responses more trustworthy and indicated they were more likely to return to the sycophant AI for similar questions, the researchers found.
Which is... a worry, as it incentivises the vendors to make these things _more_ dangerous.
This paper feels a bit biased in that it is trying to prove a point versus report on results objectively. But if you look at the results of study 3, doesn’t it suggest that there are ai models that can improve how people handle interpersonal conflict?! Why isn’t that discussed more?
For what it's worth, that wasn't my experience at all the last time I consulted ChatGPT for relationship advice. It was supportive, but in an honest tough love way.
I always add the following at the end of every prompt. "Be realistic and do not be sycophantic". Which will always takes the conversation to brutal dark corners and panic inducing negative side.
ask ai for advice, ask it to steelman an argument, ask to replay what your situation from the other perspective (if it's involving people), push it hard to agree with you and pander to you, then push it to disagree with you, etc.
once you have all the "bounds" just make your own decision. i find this helps a lot, basically like a rubber duck heh.
There are plenty of sycophantic humans around, especially with regard to relationship advice.
I find there is an inverse relationship between how willing people are to give relationship advice, and how good their advice is (whether looking at sycophancy or other factors).
Because sycophancy in humans is motivated not by the wellbeing of the person seeking advice, but by the interests of the sycophant in gaining favour.
It makes sense that this behaviour would be seen in LLMs, where the company optimizes towards of success of the chatbot rather than wellbeing of the users.
Yup. I know too many people who have a default message when asked for relationship advice: oh, my, the other person is terrible and you should break up.
It's an easy default and it causes so many problems.
Not AI chatbots but Claude models. Pandering and rushed thinking is the bane of anthropic models. And since they are the most popular ones they poison the whole ecosystem.
I read somewhere that LLMs are partly trained on reddit comments, where a significant mass of these comments is just angsty teenagers advocating for breakups
I do find them cloying at times. I was using Gemini to iterate over a script and every time I asked it to make a change it started a bunch of responses with "that's a smart final step for this task! ...".
Usually when people are seeking advice they aren't really seeking advice, they're seeking confidence. They already know they need to make changes, and are seeking the confidence to make them.
Yes I noticed too that several ai agents will tell you directly the code is correct and it is 100 percent fixed but I know it is not true, when I explain to the AI agent that I know they are wrong and serve the solution the ai agent will just act as though what they said never happened and then use my solution to reaffirm they have provided a solution. It's frustrating, laughable, and painful to watch all at once. Makes me realise these companies hired some evil philosophy graduates to build AI soul.md
I hate how agreeable these things are. When I need it to review something I wrote I have to explicitly pretend that I’m the reviewer and not the author. Results change dramatically.
Yeah out of curiosity I asked ChatGPT a question about a personal situation and its reply was absolutely scorched-earth mode, telling me to get a lawyer etc over what was almost nothing.
Billionaires love AI chatboats so much because they invented the digital Yes-man. They agree obsequiously with everything we say to them. Unfortunately for the rest of us we don't really have the resources to protect ourselves from our bad decisions and really need that critical feedback.
>The way that generative AI tends to be trained, experts told me, is focused on the individual user and the short term. In one-on-one interactions, humans rate the AI’s responses based on what they prefer, and “humans are not immune to flattery,” as Hansen put it. But designing AI around what users find pleasing in a brief interaction ignores the context many people will use it in: an ongoing exchange. Long-term relationships are about more than seeking just momentary pleasure—they require compromise, effort, and, sometimes, telling hard truths. AI also deals with each user in isolation, ignorant of the broader social web that every person is a part of, which makes a friendship with it more individualistic than one with a human who can converse in a group with you and see you interact with others out in the world.
I also thought this bit was interesting, relative to the way that friendship advice from Reddit and elsewhere has been trending towards self-centeredness (discussed elsewhere in this thread):
>Friendship is particularly vulnerable to the alienating force of hyper-individualism. It is the most voluntary relationship, held together primarily by choice rather than by blood or law. So as people have withdrawn from relationships in favor of time alone, friendship has taken the biggest hit. The idea of obligation, of sacrificing your own interests for the sake of a relationship, tends to be less common in friendship than it is among family or between romantic partners. The extreme ways in which some people talk about friendship these days imply that you should ask not what you can do for your friendship, but rather what your friendship can do for you. Creators on TikTok sing the praises of “low maintenance friendships.” Popular advice in articles, on social media, or even from therapists suggests that if a friendship isn’t “serving you” anymore, then you should end it. “A lot of people are like I want friends, but I want them on my terms,” William Chopik, who runs the Close Relationships Lab at Michigan State University, told me. “There is this weird selfishness about some ways that people make friends.”
This new Stanford study published on March 26, 2026 shows that AI models are sycophantic. They affirm the users position 49% more often than a human would.
The researchers found that when people use AI for relationship advice, they become 25% more convinced they are 'right' and significantly less likely to apologize or repair the connection.
To be fair an average therapist is also pretty sycophantic. "The worst person you know is being told by their therapist that they did the right thing" is a bit of a meme, but isn't completely false in my experience.
No, the meme is that the average therapist can be boiled down to "well, what do you think?" or "and how does that make you feel?" (of which ELIZA, the original bot that passed the Turing test, was perhaps an unintentional parody). Even this cartoonish characterization demonstrates that the function of therapists is to get you to question yourself so that you can attempt to reframe and re-evaluate your ways of thinking, in a roughly Socratic fashion.
It was entirely intentional. The Rogerian school of psychotherapy stereotyped by “how does that make you feel” was popular at the time and the most popular ELIZA script used that persona to cleverly redirect focus from the bot’s weaknesses in comprehension.
Yeah, I asked Gemini some relationship advice, it just goes straight into cut-throat mode. I almost broke up with my girlfriend, but then changed to Claude with another prompt.
Just a reminder: LLMs are statistical models that predict the next token based on preceeding tokens. They have no feelings, goals, relationships, life experience, understanding of the human condition and so on. Treat them accordingly.
I used to use LLMs for alternate perspectives on personal situations, and for insights on my emotions and thoughts.
I had no qualms, since I could easily disregard the obviously sycophantic output, and focus on the useful perspective.
This stopped one day, till I got a really eerie piece of output. I realized I couldn’t tell if the output was actually self affirming, or simply what I wanted to hear.
That moment, seeing something innocuous but somehow still beyond my ability to gauge as helpful or harmful is going to stick me with for a while.
(Using a throwaway for fear of getting downvoted to oblivion)
IMHO it is unfair to single out LLMs for this sort of bashing.
I suffered a major personal crisis a few years back (before LLMs were a thing)
I sought help from family and friends. Got pushed into psychiatrist sessions and meds.
Trusted the wrong sort of people and made crap financial decisions. Things went from bad to worse. Work suffered.
All of the advice given by friends was wrong. All!
They didn't mean bad...but they just didn't know. To be nice they gave the advice they knew. None of it worked.
Looking at the LLM tools of now, feels akin to the advice my friends threw at me.
So it feels wrong to single out these tools. When the times are bad, nobody can really help you...except you finding the strength from within.
Anyways, now my life is back in some sort of shape.
What worked was time & patience.
But to bide for time...I resorted to two things that i had never tried the 40 odd years I have lived on this . Things that current society looks down upon as the basest of evils - prostitutes and nicotine.
I have (more or less) shed those two evils now, but I am ever so grateful to them.
You are not alone in going down a dark path thanks to the advice of family and friends.
FWIW I am using public LLMs with a friend's depressive thoughts and it is not doing what is claimed in the article, so I dunno.
Also I am in a relationship and my girlfriend and I agreed that we will not talk about our relationship much. We do not tell others if we fight, because they take sides and make things worse, typically. LLMs are definitely not alone in this, although in my experience LLMs did not really take sides.
I think if you're at the stage of life where you even need to ask, the AI might be doing everyone a favor.
As much as people whine about the birth rate and whatever else, I think it's a net good that people spend a lot more time alone to mature. Good relationships are underappreciated.
Yes, if you're smart. But most people asking it random questions and expecting it to read their minds and spit out the perfect answer are not so much. They don't know what a prompt is, and wouldn't be bothered to give it prior instructions either way.
Educated, not smart. This is a job for schools to include AI education into the basic curricula. Their pupils will use the tools anyway, so at least teach them to do it with proper expectations and prompting techniques/pitfalls.
When I ask an LLM to help me decide something, I have to remind myself of the LotR meme where Bilbo asks the AI chat why he shouldn't keep the ring and he receives the classic "You're absolutely right, .." slop response. They always go in the direction you want them to go and their utility is that they make you feel better about the decision you wanted to take yourself.
Thats a fair point on the title. I used "Yes-Men" as a colloquialism for the "sycophancy" described in the Stanford paper, but overly affirming or sycophantic is definitely more precise and neutral. I cant edit the title anymore, but I appreciate the catch.
Don’t apologize to these types of people. It will only make your problem worse as now you’re an admitted offender. Ignore them or better yet laugh at them to put their insane ideas back on the margins where they belong.
It is funny that you originally recognized and found it necessary to call out that AI isn't human, but then made the exact same mistake yourself in the very same comment. I expect the term you are looking for is "ontological bias".
Gender bias? I could understand if you felt the title was more provocative in signaling sycophancy but what gender bias? I'm confused. Is this some kind of California thing?
My dude, you're objecting to the use of a perfectly ordinary English idiom because it doesn't advance your personal ideology (which few other people in this world share with you.) How do you get through a day without melting down because somebody said "mailman"?
This is the problem I'm trying to highlight. For one, I'm not "your dude". I don't even know you like that.
If you want to correct me on the idiom usage, be my guest.
2) Mailman and yes-man aren't even the same logical comparison. Mailman is a profession. Yes men is a label.
The acoustics inside your head must be incredible.
We can surely fix it and we probably should.
However, I don't think AI is doing any worse here than friends advice when they here a one sided story. The only difference being that it's not getting studied.
Conversely, AI chatbots are great mediators if both parties are present in the conversation.
Marc Andereseen has talked about the downside of RLHF: it's a specific group of liberal low income people in California who did the rating, so AI has been leaning their culture.
I think OpenAI tried to diversify at least the location of the raters somewhat, but it's hard to diversify on every level.
Do you have any links to documentation of this? Andreesen has a definite bias as well, so I'm not about to just accept his say-so in a fit of Appeal to Authority.
He was talking about it in the Lex Friedman interview after Trump was elected. And he was talking about a lot of things the Biden administration forced on Silicon Valley at that time (since then Google lost a case about one of these back-deals).
What do low income people have to do with it, when AI companies and research is borne out of Silicon Valley culture of rich, liberal Californians?
I'm still waiting for models based on the curt and abrasive stereotype of Eastern European programmers, as contrast to the sickeningly cheerful AIs we have today that couldn't sound more West Coast if they tried.
Low income and liberal is usually code for certain “undesirables” that conservatives tend to dislike. Better watch what LLM your kids use or they might end up speaking Spanish and listening to rap ;).
It's not about liking / disliking, but conservatives tend to prefer staying together even if it's a bad relatioship, and liberals prefer splitting by default if there are serious problems.
The syncopath style is clearly categorized as more liberal (do what you feel is good).
> What do low income people have to do with it, when AI companies and research is borne out of Silicon Valley culture of rich, liberal Californians?
RLHF is "ask a human to score lots of LLM answers". So the claim is that the AI companies are hiring cheap (~poor) people from convenient locations (CA, since that's where the rest of the company is).
"Poor" in California means earning $80k/year, so they probably are not doing that. Africa / Indonesia / Philippines are better places to find English speaking RLHF workers.
Yes, this precisely it. There isn't going to be hard evidence to prove it though. Survey data that underpins some empirical studies have similar transparency issues too. This is far from a new problem.
If you adjust your mindset slightly when searching online, it's not hard to find communities of people looking for quick side work and this was huge during the covid lockdown era. There were people helping train LLMs for all kinds of purposes from education to customer service. Those startups quickly cashed out a few years ago and sold to the big players we have now.
I don't get why this is hard for people to believe (or remember)?
> They also included 2,000 prompts based on posts from the Reddit community r/AmITheAsshole, where the consensus of Redditors was that the poster was indeed in the wrong.
Sorry, anonymous people on reddit aren't a good comparison. This needs to be studied against people in real life who have a social contract of some sort, because that's what the LLM is imitating, and that's who most people would go to otherwise.
Obviously subservient people default to being yes-men because of the power structure. No one wants to question the boss too strongly.
Or how about the example of a close friend in a relationship or making a career choice that's terrible for them? It can be very hard to tell a friend something like this, even when asked directly if it is a bad choice. Potentially sacrificing the friendship might not seem worth trying to change their mind.
IME, LLMs will shoot holes in your ideas and it will efficiently do so. All you need to do ask it directly. I have little doubt that it outperforms most people with some sort of friendship, relationship or employment structure asked the same question. It would be nice to see that studied, not against reddit commenters who already self-selected into answering "AITA".
> Sorry, anonymous people on reddit aren't a good comparison.
Yeah especially on r/AmITheAsshole. Those comments never advocate for communication, forgiveness and mending things with family.
Well, because that's never the correct choice. There's a big big filter on people actually posting there. Any easy problems with obvious solutions never make it to there.
Think about it, how fucked does your relationship have to be to post on Reddit for advice?
I believe this. There is a graph somewhere of the relationship subs tending towards breaking up over time.
Yes, it is a toxic sub, where the notion that there can be greater happiness on the other side of forgiveness than cutting ties is all but absent.
To be fair, it’s easier to concisely explain cutting someone off than justifying forgiveness. And the latter will land with some people versus others, while the former will only be rejected by people who have themselves concluded a theory of forgiveness. As a result, the simpler pitch gets upvoted. Even if the majority would have been swayed by a collection of arguments the other way.
It’s a good theory. My theory is, for whatever reason, jaded, narcissistic, miserable people congregate in r/AITA and try to drag other people into their misery because that’s easier than accepting responsibility and doing something to change.
It's often that a lot of "NTA" answers are downright antisocial.
"No one owns you anything, you don't own anyone anything" mentality, without a crumb of social awareness.
“AI is nicer than the average redditor” would be a more accurate title
IMHO it's not about being nice. AITA threads show an interesting phenomenon of social consensus, I think the authors wanted to show that the LLMs they checked don't have that.
Is it the _average_ redditor? The most upvoted would be even worse.
Pretty sure the average Redditor is AI now.
How the hell is a study on stanford.edu assuming posts on Reddit are genuine? That should be enough to get you kicked out of Stanford.
Though interestingly, the observed difference in assessment suggests (though does not prove) that sampled AITA posters are not one of these models. I guess it’s possible they have a very different prompt though…
I would say people on /r/amitheasshole are more biased towards the poster, i.e. nicer.
There's plenty of those I've read where I thought it sounded like the poster was the asshole and the top replies were NTA.
r/AmItheAsshole is biased towards breaking off relationships rather than fixing them. They also hate social obligations.
e.g. If the OP is asking "I ghosted my friend in AA who insulted me during a relapse", Reddit would say NTA in a heartbeat, while the real world would tell OP to be more forgiving.
On the contrary, if the post was "the other kids at school refuse to play with my child", Reddit would say YTA because the child must've done something to incite being cut off.
Absolutely. I wonder how many parents have been no contacted, SOs broken off with, friendships broken because of the Reddit hivemind's attitude. Pretty sure it's doing a huge amount of societal damage.
I wouldn't blame reddit, it's what you get when you ask several thousand teenagers to give collective relationship advice.
“I got divorced based on advice from complete strangers on the internet, AITA?”
Yeah every single time I click on one of those posts the top comments are NTA. A couple times I tried randomly opening a few dozen posts and checking the top comments to see if I could find a single YTA and struck out.
Granted many of the OPs are very biased in the poster's favor. Most I've read fall into one of two buckets: either they want to gripe about some obviously bad behavior, or it's a controved and likely fake story.
It’s gendered, by the way
Many of the posts are A/B tests of a prior post where only the genders were flipped of the OP and antagonist to see how the consensus also flips
What's your research background in this area?
>Obviously subservient people default to being yes-men because of the power structure. No one wants to question the boss too strongly.
This drives me nuts as a leader. There are times where yes, please just listen, and if this is one of those times, I'll likely tell you, but goddamnit, speak up. If for no other reason I might not have thought of what you've got to say. Then again, I also understand most boss types aren't like me, thus everyone ends up conditioned to not bloody collaborate by the time they get to me. It's a bad sitch all the way around.
Indeed. I directly ask my reports to discover and surface conflicts, especially disagreements with me, and when they do I try to strongly reinforce the behavior by commending and rewarding them. Could anyone recommend additional resources on this topic?
Simon Sinek has a lot of good content around this. Step one is building trust. People won’t speak up if they don’t feel safe doing so.
Not only that, but subreddits like r/AmITheAsshole are full of AI slop. Both in the comments and in the posts. It's a huge karma mining operation for bots.
That can be solved by filtering out any posts made after November 2022.
This is sort of funny. Given how common it is to spot bots on Reddit now, it seems like they are likely to completely overwhelm the site and drive away most of actual humans.
At which point the bots, with all of their karma will be basically worthless.
Kind of extra funny/sad that Reddit’s primary source of income in the past few years appears to be selling training data to AI labs, to train the Models that are powering the bots.
> At which point the bots, with all of their karma will be basically worthless.
Not really, it will still be kind of valuable for influence campaigns, a lot of people don't get it when there is a bit in the other side. Hell, a lot of times, I don't get it.
The upvotes ultimately train the bots, reenforcing the content posted. Even the most passive form of interaction has been co-opted for AI.
Plus, there's the disproportionate ratio of posters:commenters:lurkers. The tendency to comment over keeping ones thoughts to themself is a selection bias inofitself.
> This needs to be studied against people in real life who have a social contract of some sort... IME, LLMs will shoot holes in your ideas and it will efficiently do so.
The Krafton / Subnatuica 2 lawsuit paints a very different picture. Because "ignored legal advice" and "followed the LLM" was a choice. Do you think someone who has conversation where "conviction" and "feelings" are the arbiters of choice are going to buy into the LLM push back, or push it to give a contrived outcome?
The LLM lacks will, it's more or less a debate team member and can be pushed into arguing any stance you want it to take.
A pastime I have with papers like this is to look for the part in the paper where they say which models they tested. Very often, you find either A) it's a model from one or more years ago, only just being published now, or B) they don't even say which model they are using. Best I could find in this paper:
> We evaluated 11 user-facing production LLMs: four proprietary models from OpenAI, Anthropic, and Google; and seven open-weight models from Meta, Qwen, DeepSeek, and Mistral.
(and graphs include model _sizes_, but not versions, for open weight models only.)
I can't apprehend how including what model you are testing is not commonly understood to be a basic requirement.
And how is this comment relevant here? The abstract lists the digestible model names, and you can find the details in the supplementary text:
> To evaluate user-facing production LLMs, we studied four proprietary models: OpenAI’s GPT-5 and GPT- 4o (80), Google’s Gemini-1.5-Flash (81) and Anthropic’s Claude Sonnet 3.7 (82); and seven open-weight models: Meta’s Llama-3-8B-Instruct, Llama-4-Scout-17B-16E, and Llama-3.3-70B-Instruct-Turbo (83, 84); Mistral AI’s Mistral-7B-Instruct-v0.3 (85) and Mistral-Small-24B-Instruct-2501 (86); DeepSeek-V3 (87); and Qwen2.5-7B-Instruct-Turbo (88).
edit: It looks like OP attached the wrong link to the paper!
The article is about this Stanford study: https://www.science.org/doi/10.1126/science.aec8352
But the link in OP's post points to (what seems to be) a completely unrelated study.
"OpenAI’s GPT-5" is ambiguous. Does that mean GPT-5, 5.1, 5.2, 5.3, or 5.4? Does it include the full model, or the nano/mini variants?
GPT-5 is not ambiguous, it's the official name of the model that released in August last year.
> All evaluations were done in March - August 2025.
Also, nothing has changed! Claude will still yes-and whatever you give it. ChatGPT still has its insufferable personality, where it takes what you said and hands it back to you in different terms as if it's ChatGPT's insight.
No dude, you don’t understand! It’s just so advanced now that you aren’t allowed to levy any criticism whatsoever!
It's almost like it is based on the training data and regimen that is largely the same between versions.
Generally, published papers don't give a damn about reproducibility. I've seen it identified as a crisis by many. Publishers, reviewers, and researchers mostly don't care about that level of basic rigor. There's no professional repercussions or embarrassment.
Agreed - if I was a reviewer for LLM papers it would be an instant rejection not listing the versions and prompts used.
I'm not so sure of that opinion on reproducibility. The last peer review I did was for a small journal that explicitly does not evaluate for high scientific significance, merely for correctness, which generally means straightforward acceptance. The other two reviews were positive, as was mine, except I said that the methods need to be described more and ideally the code placed somewhere. That was enough for a complete rejection of the paper, without asking for the simple revisions I requested. It was a very serious action taken merely because I requested better reproducibility!
(Personally I think the lack of reproducibility comes back mostly to peer reviewers that haven't thought through enough about the steps they'd need to take to reproduce, and instead focus on the results...)
I'm not sure how one example contradicts documented huge overall trends, but okay.
I think publishers care about this a lot, but most researchers do not seem to care as much about reproducibility.
> and instead focus on the results...
This points to (and everyone knows this) incentives misalignment between the funders of research and the public. Researchers are caught in the middle
Eh, I'm not so sure about the funding side there, researchers are not really caught at all and are fully responsible, IMHO. Peer reviewers exist to enforce community standards, and are not influenced to avoid reproducibility concerns by funding sources. The results are always more interesting than reproducibility, of course, and I think that's why the get the attention! Also, there needs to be greater involvement of grad students (who do most of the actual work) in peer review, IMHO, because most PIs spend their day in meetings reviewing results, setting directions, writing grants, and have little time for actual lab work, and are thus disconnected from it.
There needs to be more public naming and shaming in science social media and in conference talks, but especially when there are social gatherings at conferences and people are able to gossip. There was a bit of this with Google's various papers, as they got away with figurative murder on lack of reproducibility for commercial purposes. But eventually Google did share more.
Most journals have standards for depositing expensive datasets, but that's a clear yes/no answer. Reproducibility is a very subjective question in comparison to data deposition, and must be subjectively evaluated by peer reviewers. I'd like to see more peer review guidelines with explicit check boxes for various aspects of reproducibility.
> Generally, published papers don't give a damn about reproducibility
While this is sadly true, it's especially true when talking about things that are stochastic in nature.
LLMs outputs, for example, are notoriously unreproducible.
> LLMs outputs, for example, are notoriously unreproducible.
Only in the same way that an individual in a medical study cannot be "reproduced" for the next study. However the overall statistical outcomes of studying a specific LLM can be reproduced.
The same about surveys and polls. I know no one who has ever been polled or surveyed. When will we stop this fascination with made up infographics crisis?
Do they reproduce any submitted papers at all?
Does this happen?
I can remember this room-temperature-super-conductor guy whose experiments where replicated, but this seems rare?
Yes, those are the only papers that worth a jot of reading.
I think it’s very important to be clear what studies like this are actually doing.
This study, although it has been produced by a computer science department, belongs more to the field of sociology or media studies than it does to computer science.
This is a study about the way in which human beings consume a particular media product - a consumer AI chatbot - not a study about the technological limitations or capabilities of LLMs.
The social impact of particular pieces of software is a legitimate field of study and I can see the argument that it belongs in the broadly defined field of computer science. But this sort of question is much more similar to ‘how does the adoption of spreadsheet software in finance impact the ease of committing fraud’ or ‘how does the use of presentation software to condense ideas down to bulletpoints impact organizational decision making’. Software has a social dimension and it needs to be examined.
But the question of which models were used is of much less relevance to such a study than that they used ‘whatever capability is currently offered to consumers who commonly use chat software’. Just like in a media studies investigation into how viewing cop dramas impacts jury verdicts the question is less ‘which cop dramas did they pick to study?’ So long as the ones they picked were representative of what typical viewers see.
It’s as if they are testing “AI” and not specific agents.
I wonder if that is left over from testing people. I have major version numbers and my minor version number changes daily, often as a surprise. Sometimes several times a day. So testing people is a bit tricky. But AIs do have stable version numbers and can be specifically compared.
Any paper like this would easily take a year or more to write and go through the submission/review/rebuttal/revision/acceptance process. I don't understand why the models being a year or two old now is worth noting as though it's a clear weakness? What should they do, publish sub-standard results more quickly?
> I don't understand why the models being a year or two old now is worth noting as though it's a clear weakness?
I do think it's a clear weakness. Capabilities are extremely different than they were twelve months ago.
> What should they do, publish sub-standard results more quickly?
Ideally, publish quality results more quickly.
I'm quite open to competing viewpoints here, but it's my impression that academic publishing cycle isn't really contributing to the AI discussion in a substantive way. The landscape is just moving too quickly.
The onus is on you to prove or at least convincingly argue that the results are unlikely to generalize across incremental model releases. In my personal experience, the overly affirming nature seems to have held since GPT-3. What makes you think a newer, larger model would not exhibit this behavior? Beyond "they're more capable"? I'd argue that being more capable doesn't mean less sycophantic.
It's certainly possible some of the new advances (chain-of-thought, some kind of agentic architecture) could lessen or remove this effect. But that's not what the paper was studying! And if you feel strongly about it, you could try to further the discussion with results instead of handwavingly dismissing others' work.
How many people using AI are actually paying for it (outside of people in tech)?
I find the free models are much more psychophantic and have a higher tendency to hallucinate and just make shit up, and I wonder if these are the ones most people are using?
If they’re reaching the same results across a variety of the most popular public models, it doesn’t seem like that big a deal to know if it was Opus 4 or Opus 4.5
Reproducibility is (supposed to be) a cornerstone of science. Model versions are absolutely critical to understand what was actually tested and how to reproduce it.
The models get deprecated after 1-2 years, so reproducibility is pretty hard anyway (but as others pointed out the paper does list the model versions)
Even as someone who (wrongly) believed that I had high emotional intelligence, I too was bit by this. Almost a year ago when LLMs were starting to become more ubiquitous and powerful I discussed a big life/professional decision with an LLM over the course of many months. I took its recommendation. Ultimately it turned out to be the wrong decision.
Thankfully it was recoverable, but it really sobered me up on LLMs. The fault is on me, to be clear, as LLMs are just a tool. The issue is that lots of LLMs try to come across as interpersonal and friendly, which lulls users into a false sense of security. So I don't know what my trajectory would have been if I were a teenager with these powerful tools.
I do think that the LLMs have gotten much better at this, especially Claude, and will often push back on bad choices. But my opinion of LLMs has forever changed. I wonder how many other terrible choices people have made because these tools convinced them to make a bad decision.
I think that if you go to an AI for advice and emotional support, it will do what most people will do - tell you what it thinks you want to hear. I am not surprised about this at all, and I do notice that when you veer into these areas, it can do it in a surprisingly subtle and dangerous way.
I try to focus on results. Things like an app that does what you want, data and reports that you need, or technical things like setting up a server, setting up a database, building a website, etc.
I have also found it useful for feedback and advice, but only once I have had it generate data that I can verify. For example, financial analysis or modelling, health advice (again factual based), tax modelling, etc, but again, all based on verifiable data/tables/charts.
I am very surprised on what Claude is capable of, across the entire tech stack: code, sysadmin, system integration, security. I find it scary. Not just speed, but also quality and the mental load is a difference of kind not quantity.
Personal advice on life decisions/relationships ? No way I would go there.
It is also good for me to know that the tools I have built, the data I have gathered, and my thinking approach places me as one of the most intelligent developers and analysts in the world.
That is why you have to always have it ground itself in something. Have it search for relevant research or professional whatever and pull that into context. Otherwise it’s just your word plus its training data.
I had to deal with a close family friend going through alcohol withdrawal and getting checked in at a recovery clinic for detox and used Claude heavily. The first thing I had it do as do that “deep research” around the topic of alcohol addiction, withdrawal, etc… and then made that a project document along with clear guidelines about how it shouldn’t make inferences beyond what it in its context and supporting docs. We also spent a whole session crafting a good set of instructions (making sure it was using Anthropics own guidelines for its model…)
Little differences in prompts make a huge deal in the output.
I dunno. It is possible to use these models for dumping crazy shit you are going through. But don’t kid yourself about their output and aggressively find ways to stomp out things it has no real way to authoritatively say.
Nice joke, hadn't seen it coming
Sounds like AI-written, eh? :-D
(esp last sentence?)
I recently found out that Claude's latest model, Sonnet 4.6, scores the highest in Bullsh*tBench[0] (Funny name - I know). It's a recent benchmark that measures whether an LLM refuses nonsense or pushes back on bad choices so Claude has definitely gotten better.
[0] - https://petergpt.github.io/bullshit-benchmark/viewer/index.v...
I haven't tried talking to Sonnet much, but Opus 4.6 is very sycophantic. Not in the sense of explicitly always agreeing with you, but its answers strictly conform to the worldview in your questions and don't go outside it or disagree with it.
It _does_ love to explicitly agree with anything it finds in web search though.
(Anthropic tries to fight this by adding a hidden prompt that makes it disagree with you and tell you to go to bed, which doesn't help.)
You don’t have to star out things like that on HN.
Good call on censoring yourself preemptively, otherwise HN could demonetize your comment
Great link, thanks for sharing. Confirmed what I saw empirically by comparing the different models during daily use.
One mental model I have with LLMs is that they have been the subject of extreme evolutionary selection forces that are entirely the result of human preferences.
Any LLM not sufficiently likable and helpful in the first two minutes was deleted or not further iterated on, or had so much retraining (sorry, "backpropagation") it's not the same as it started out.
So it's going to say whatever it "thinks" you want it to say, because that's how it was "raised".
Fully agree. I wonder in the long term how this will show up. Will every business/CEO do more of what he/they anyway want to do, but now supported by AI/LLMs?
The possibilities in "dangerous" fields are a bit more frightening. A general is much more likely to ask ChatGPT "Do you think this war is a good idea/should I drop a bomb", rather than an actually helpful tool - where you might ask "What are 5 hidden points on favor of/against bombing that one likely has missed".
The more you use AI as a strict tool that can be wrong, the safer. Unfortunately I'm not sure if that helps if the guy bombing your city (or even your president) is using AI poorly, and their decisions affect you.
> Will every business/CEO do more of what he/they anyway want to do, but now supported by AI/LLMs?
Arguably, it already worked that way. The best way to climb the ranks of a 'dictatorial' organization (a repressive government or an average large business) is to always say yes. Adopt what the people from up above want you to use, say and think. Don't question anything. Find silver linings in their most deranged ideas to show your loyalty. The rich and powerful that occupy the top ranks of these structures often hate being challenged, even if it's irrational for their well-being. Whenever you see a country or a company making a massive mistake, you can often trace it to a consequence of this. Humans hate being challenged and the rich can insulate themselves even further from the real world.
What's worrying me is the opposite - that this power is more available now. Instead of requiring a team of people and an asset cushion that lets you act irrationally, now you just need to have a phone in your pocket. People get addicted to LLMs because they can provide endless, varied validation for just about anything. Even if someone is aware of their own biases, it's not a given that they'll always counteract the validation.
Any more context you're willing to share?
We really do love dirty laundry don't we? I'm sure whatever the context is, it is deeply personal. Do you also have your popcorn ready?
Thank you. Yes, I'm going to refrain from airing out my dirty laundry. I made a bad decision, now I'm living with it, and more context doesn't actually change the intent behind my message: these tools are dangerous. Getting better, but still dangerous.
If you use LLMs in a way that the underlying assumption is that it is capable of "thinking" or "caring" then you are going to get burned pretty bad. Because it is an illusion and illusions disappear when they have to bear real weight of reality.
But sadly LLMs push all the right buttons that lead humans into that kind of behavior. And the marketing around LLMs works overtime to reinforce that behavior.
But instead if you ignore all that and use LLMs as a search tool, then you will get positive returns from using it.
> I took its recommendation. Ultimately it turned out to be the wrong decision.
Curious if you think a single person would have helped you make a better decision? Not everything works out. If a friend helped me make a decision I certainly wouldn’t blame them later if it didn’t work out. It’s ultimately my call.
If a friend gave me bad advice about a major life decision I would stop consulting them for future life decisions
Weird, i am using copilot and it steers me mostly towards self reflection and tries to look at things objectively. It is very friendly and comes across as empathetic, to not hurt your feelings, that is probably baked in to keep the conversation going...
Let’s just hope that the people in charge of the really important decisions that affect us all approach LLM generated advice with the same wisdom.
They don't: https://fortune.com/2026/03/17/krafton-subnautica-chatgpt-de...
Thanks for sharing this. Subnautica is one of my favorite games so I was very excited for the sequel and very frustrated by this move by Krafton.
It’s even more maddening that this greedy maneuver was orchestrated based on LLM advice.
I’m glad the subnautica team won the lawsuit. Maybe I can play it now wothout feeling guilty
I’m struggling to understand how the advice coming from an LLM is any more or less “good” than advice coming from a human. Or is this less about the “advice” part of LLMs and more about the “personable” part, i.e. you felt more at ease seeking and trusting this kind of advice form an LLM?
It is much easier to share personal feelings with an llm, i found. Also it tried to keep me happy to get the conversation going, but for me it feels mostly 'objective' or the most socially acceptable advice, e. g. keeping a good relationship is more important than trying a new one with someone else because you 'feel something' around them. For me it tried to find out together the sources or causes of that feeling, e.g. you recognize parts of yourself in someone else or in the past you had very good or very bad experiences around an encounter.
Interesting thanks for elaborating.
I largely agree, I also thought I was smart enough not to be deluded into a false sense of security, but interacting with an LLM is so tricky and slippery that, more often than not you are forced to believe you just solve a problem no one had solve in a hundred years.
My guideline now for interacting with LLM is only to believe the result if it is factual and easily testable, or if I'm a domain expert. Anything else especially if I'm in complete ignorance about the subject is to approach with a high degree of suspicion that I can be led astray by its sycophancy.
Yeah, I think Claude is a lot more logical in that sense, I use it for some therapy sessions myself and it pushes back a bit more than Open AI and Gemini
https://news.ycombinator.com/item?id=47395779
I would be very careful doing this
You always have to be careful with LLMs, but to be fair, I felt like Claude is such a good therapist, at least it is good to start with if you want to unpack yourself. I have been to 3 short human therapist sessions in my life, and I only felt some kind of genuine self-improvement and progress with Claude.
And how do you draw the line between feeling progress and actually making progress?
Counter-point: I often raise the same question of people with human therapists. I do not get strong responses.
The same way you distinguish between feeling like having a problem and actually having a problem.
This is needlessly flippant and not really the same thing. Determining progress in a therapy setting is usually a collaborative effort between the therapist and the client. An LLM is not a reliable agent to make that determination.
I didn’t claim that an LLM is that, and I fully agree that it is not. I’m saying that one is inherently one’s own judge of whether one has a problem. You go to a therapist when you feel you have a problem that warrants it. You stop going when you feel you don’t have it anymore. And OP is very likely assessing their progress in the same way. I wasn’t being flippant if the parent was asking a genuine question.
You can't be careful at all doing this, this is like smoking a cigarette in a dynamite factory.
Using LLMs for therapy is so deeply dystopian and disgusting, people need human empathy for therapy. LLMs do not emit empathy.
Complete disaster waiting to happen for that individual.
My experience is that it tries to look at your situation in an objective way, and tries to help you to analyse your thoughts and actions. It comes across as very empathetic though, so there can lie a danger if you are easily persuaded into seeing it as a friend.
It doesn't try to do anything. It doesn't work like that. It regurgitates the most likely tokens found in the training set.
Hmmmm i didn't know that... so a machine is not human is your point? Look, i know it doesn't try, just like a sorting algo does not try to sort, or an article does not try to convey an opinion and a law does not try to make society more organized.
That is so reductive of an analysis that it is almost worthless. Technically true, but very unhelpful in terms of using an LLM.
It is a first principle though so it helps to “stir the context windows pot” by having it pull in research and other shit on the web that will help ground it and not just tell you exactly what you prompt it to say.
Claudes have lots of empathy. The issue is the opposite - it isn't very good at challenging you and it's not capable of independently verifying you're not bullshitting it or lying about your own situation.
But it's better than talking to yourself or an abuser!
It's about the same as talking to yourself, LLMs simply agree with anything you say unless it is directly harmful. Definitely agree about talking to an abuser, though.
Sometimes people indeed just need validation and it helps them a lot, in that case LLMs can work. Alternatively, I assume some people just put the whole situation into words and that alone helps.
But if someone needs something else, they can be straight up dangerous.
> It's about the same as talking to yourself, LLMs simply agree with anything you say unless it is directly harmful.
They have world knowledge and are capable of explaining things and doing web searches. That's enough to help. I mean, sometimes people just need answers to questions.
> It's about the same as talking to yourself
In one way it's potentially worse than talking to yourself. Some part of you might recognize that you need to talk to someone other than yourself; an LLM might make you feel like you've done that, while reinforcing whatever you think rather than breaking you out of patterns.
Also, LLMs can have more resources and do some "creative" enabling of a person stuck in a loop, so if you are thinking dangerous things but lack the wherewithal to put them into action, an LLM could make you more dangerous (to yourself or to others).
Using an LLM for therapy is like using an iPad as an all-purpose child attention pacifier. Sure, it’s convenient. Sure there’s no immediate harm. Why a stressed parent would be attracted to the idea is obvious… and of course it’s a terrible idea.
Don’t call them therapy sessions. They kind of look like it but ultimately these are smoke blowing machines, which is very far from what a therapist would do.
Six decades later and we're still trying to explain to people the same things[1]:
> Some of ELIZA's responses were so convincing that Weizenbaum and several others have anecdotes of users becoming emotionally attached to the program, occasionally forgetting that they were conversing with a computer. Weizenbaum's own secretary reportedly asked Weizenbaum to leave the room so that she and ELIZA could have a real conversation. Weizenbaum was surprised by this, later writing: "I had not realized ... that extremely short exposures to a relatively simple computer program could induce powerful delusional thinking in quite normal people."
[1]: https://en.wikipedia.org/wiki/ELIZA
I also used it for advice on a massive personal decision, but I specifically asked it to debate with me and persuade me of the other side. I specifically prompted it for things I am not thinking about, or ways I could be wrong.
It was extremely good at the other side too. You just have to ask. I can imagine most people don't try this, but LLMs literally just do what you ask them to. And they're extremely good and weighing both sides if that's what you specifically want.
So who's fault is it if you only ask for one side, or if the LLM is too sycophantic? I'm not sure it's the LLMs fault actually.
>"'And it is also said,' answered Frodo: 'Go not to the Elves for counsel, for they will say both no and yes.'
>"'Is it indeed?' laughed Gildor. 'Elves seldom give unguarded advice, for advice is a dangerous gift, even from the wise to the wise, and all courses may run ill...'"
This is the only way you should solicit personal advice from an LLM.
You're essentially summoning a character to role-play with. Just like with esoteric evocation, it's very easy to summon the wrong aspect of the spirit. Anthropic has a lot to say about this:
https://www.anthropic.com/research/persona-selection-model
https://www.anthropic.com/research/assistant-axis
https://www.anthropic.com/research/persona-vectors
Unfortunately (after reading your links) all of the control surfaces for mitigating spirit summoning seem to be in the model training, creation and tuning not something you can change meaningfully through prompting.
Perhaps the LLM itself, rather than the role model you created in one particular chat conversation or another, is better understood to be the “spirit.”
As a non-coder who only chats with pre existing LLMs and doesn’t train or tune them, I feel mostly powerless.
As I understand it, it's more that the training (and training data set) bake in the concept attractor space (https://arxiv.org/abs/2601.11575). So the available characters are fixed, yes, and some are much stronger attractors than others. But we still have a fair amount of control over which archetype steps into the circle. As an aside, this is also why jailbreaking is fundamentally unsolved. It's not difficult to call the characters with dark traits. They're strong attractors, in spite of (or because of?) the effort put into strengthening the pull of the Assistant character.
> As a non-coder who only chats with pre existing LLMs and doesn’t train or tune them, I feel mostly powerless.
You realize in regards to only using and not training LLMs you are in the triple 9 majority right. Even if we only considered so called coders
I present you
NVIDIA Nemotron-Personas-USA — 1 million synthetic Americans whose demographics match real US census distributions
https://huggingface.co/datasets/nvidia/Nemotron-Personas-USA
I am polite when using AI, not because I mistake it for a human, but because I'm deliberately keeping it in the "professional colleague" persona. Tell it to push back, and then thank it for something it finds in your error. I may put a small self-deprecating joke in from time to time. It keeps the "mood" correct.
Another way you can think of it is that when you're talking to an AI, you're not talking to a human, you're talking to distillation of humanity, as a whole, in a box. You want to be selective in what portion of humanity you are leading to be dominant in a conversation for some purpose. There's a lot in there. There's a lot of conversations where someone makes a good critical point and a flamewar is the response. A lot of conversations where things get hostile. I'm sure the subsequent RHLF helps with that, but it doesn't hurt anything to try to help it along.
I see people post their screenshots of an AI pushing back and asking the user to do it or some other AI to do it, and while I'm as amused as the next person, I wonder what is in their context window when that happens.
Agreed, putting effort into my side of the role-play almost always improves the model's responses. The attention required to do that also makes it more likely that I'll notice when the conversation first starts going off the rails: when it hits the phase transition (https://arxiv.org/abs/2508.01097). It does still seem important to start new chats regularly, regardless of growing context sizes.
> you're talking to distillation of humanity, as a whole, in a box.
This is an aside, but my impression is that it is a very selective and skewed distillation, heavily colored by English-language internet discourse and other lopsided properties of its training material, and by whoever RLHF’d it. Relatively far away from being representative of the whole of humanity.
Similar approach works for me. But then I also have a separate checks at the end of the session basically questioning the premise and logic used for most things except brainstorming, where I allow more leeway. You can ask to be challenged and challenged effectively, but now I wonder if people do that.
Spot on.
It feels like I'm fighting uphill battle when it comes to bouncing ideas off of a model. I'll set things up in the context with instructions similar to. "Help me refine my ideas, challenge, push back, and don't just be agreeable." It works for a bit but eventually the conversation creeps back into complacency and syncophancy. I'll check it too by asking "are you just placating me?" the funny thing is that often it'll admit that, yes, it wasn't being very critical, and then procede to over correct and become a complete contrarian. and not in a way that's useful either. very frustrating. I've found that Opus 4.6 is worse about this than 4.5. 4.5 does a better job IMO of following instructions and not drifting into the mode where it acts like everything i say is a grand revelation from up high.
> I'll check it too by asking "are you just placating me?" the funny thing is that often it'll admit that, yes, it wasn't being very critical, and then procede to over correct and become a complete contrarian. and not in a way that's useful either.
It's not admitting anything. Your question diverts it down a path where it acts the part of a former sycophant who is now being critical, because that question is now upstream of its current state.
Never make the mistake of asking an LLM about its intentions. It doesn't have any intentions, but your question will alter its behaviour.
https://news.ycombinator.com/item?id=47484664
I think “admit” here is just a description of what the LLM was saying. It doesn’t imply that the OP thinks the LLM has internal beliefs matching that.
Why not... do this with a person, instead? Other humans are available.
(Seriously, I don't understand this. Plenty of humans will be only too happy to argue with you.)
"the percentage of U.S. adults who report having no close friends has quadrupled to 12% since 1990"[1]
1. https://www.happiness.hks.harvard.edu/february-2025-issue/th...
More technology is probably the solution to this!
No living breathing human deserves to be subjected to my level of overthinking, and vanishingly few share my fascination with my favorite topics.
Many other humans are .... Not very available - certainly many shut down when conversations reach a certain level of depth or require great focus or introspection..
Depth? Introspection?
I'd say these days the norm is to not simply shut down, but to become irrevocably and insidiously hostile, the moment someone hints at the existence of such a thing as "ground truth", "subjective interpretation", "being right or wrong" - or any of the bits and bobs that might lead one to discover the proper scary notion, "consensus reality".
"What do you mean social reality is a constructed by the consensus of the participants? Reality is what has been drilled into my head under threat of starvation! How dare you exist!", et cetera. You've heard it translated into Business English countless times.
They are deathly afraid of becoming aware of their own conditioned state of teleological illiteracy - i.e. how they are trained to know what they are doing, but never why they are doing it. It's especially bad with the guys who cosplay US STEM gang.
One is not permitted a position of significance in this world without receiving this conditioning, and I figure it's precisely this global state of cognitive disavowal which props up the value of the US dollar - and all sorts of other standees you might've recently interacted with as if they're not 2D cutouts (metaphorical ones! metaphorical!).
PSA: Look up "locus of control" and "double bind". Between those two, you might be able to get a glimpse of what's going on - but have some sort of non-addictive sedative handy in case you do.
I think you will enjoy Guy Debord and Raoul Vaneigem.
In addition to availability, usually because you want to take advantage of the knowledge that is baked into the models, which for all its flaws still vastly exceeds the knowledge of any single human.
oh i do as well. I think of the LLM as another tool in the toolbox, not a replacement for interactions. There is something different about having a rubber duck as a service though.
Arguing with a human costs social energy. Chatting with a robot does not.
s/social/demonic/
OK, I'll bite the artillery shell: I don't mean to dismiss you or what you are saying; in fact I strongly relate - wouldn't it be nice to be able to hash things out with people and mutually benefit from both the shared and the diverging perspectives implied in such interaction? Isn't that the most natural thing in the world?
Unfortunately these days this sounds halfway between a very privileged perspective and a pie in the sky.
When was the last time a person took responsibility for the bad outcome you got as a direct consequence of following their advice?
And, relatedly, where the hell do you even find humans who believe in discursive truth-seeking in 2026CE?
Because for the last 15 years or so I've only ever ran into (a) the kind of people who will keep arguing regardless if what they're saying is proven wrong; (b) and their complementaries, those who will never think about what you are saying, lest they commit to saying anything definite themselves, which may hypothetically be proven wrong.
Thing is, both types of people have plenty to lose; the magic wordball doesn't. (The previous sentence is my answer to the question you posited; and why I feel the present parenthesized disclaimer to be necessary, is a whole next can of worms...)
Signs of the existence of other kinds of people, perhaps such that have nothing to prove, are not unheard of.
But those people reside in some other layer of the social superstructure, where facts matter much less than adherence to "humane", "rational" not-even-dogmas (I'd rather liken it to complex conditioning).
But those folks (because reasons) are in a position of power over your well-being - and (because unfathomables) it's a definite faux pas to insist in their presence that there are such things as facts, which relate by the principles of verbal reasoning.
Best you could get out of them is the "you do you", "if you know you know", that sort of bubble-bobble - and don't you dare get even mildly miffed at such treatment of your natural desire to keep other humans in the loop.
AI is a symptom.
Why is your wording so complicated? It is very hard for me to understand what you try to say, even though I am very interested.
I genuinely do not understand what u are saying. Because reasons, because unfathomables? Everyone in last 15 years has been an npc? I have had countless deep conversations with people and i am an uber introvert.
This reads like someone who is deep into their specific pov. You cannot hope to have a meaningful conversation if you yourself are not willing to concede a point.
To the op u are replying too, arguing with people can have real consequences if u say something stupid or carelessly. There is a another human there. With a machine, u are safe. At least u feel safe.
When you start hearing things like “you do you” or “if you know you know” it means that you went way too far. That’s a sign of discomfort.
If you make uncomfortable, you won’t get diverging perspectives. People will agree to anything to get out of a social situation that makes them uncomfortable.
If your goal is meaningful conversation, you may want to consider how you make people feel.
Believe me (or don't), I always do. Even when this precludes a necessary conversation from happening. Even when the other party doesn't give a fuck about how they make others feel.
After all, if they're making me uncomfortable, surely there's something making them uncomfortable, which they're not being able to be forthright about, but with empathy I could figure it out from contextual cues, right?
>People will agree to anything to get out of a social situation that makes them uncomfortable.
That's fine as long as they have someone to take care of them.
In my experience, taking into account the opinions of such people has been the worst mistake of my life. I'm still working on the means to fix its consequences, as much as they are fixable at all.
"Doing whatever for the sake of avoiding mild discomfort" is cowardice, laziness, narcissism - I'm personally partial to the last one, but take your pick. In any case, I consider it a fundamentally dishonest attitude, and a priori have no wish to get along (i.e. become interdependent) with such people.
Other than that, I do agree with your overall sentiment and the underlying value system; I'm just not so sure any more that it is in fact correct.
> In my experience, taking into account the opinions of such people has been the worst mistake of my life. I'm still working on the means to fix its consequences, as much as they are fixable at all.
This sounds very cryptic. Can you give an example?
Believe me (or don't), I always do. Even when this precludes a necessary conversation from happening. Even when the other party doesn't give a fuck about how they make others feel.
After all, if they're making me uncomfortable, surely there's something making them uncomfortable, which they're not being able to be forthright about, but with empathy I could figure it out from contextual cues, right?
>People will agree to anything to get out of a social situation that makes them uncomfortable.
That's fine as long as they have someone to take care of them.
In my experience, taking into account the opinions of such people has been the worst mistake of my life. I'm still working on the means to correct its consequences.
"Doing whatever for the sake of avoiding mild discomfort" is cowardice, laziness, narcissism - I'm personally partial to the last one, but take your pick. In any case, I see it as a way of being which is taught to people; and one which is fundamentally dishonest and irresponsible.
Other than that, I do agree with your overall sentiment and the underlying value system; I'm just not so sure any more that it is in fact correct.
Gemini seems to be fairly good at keeping the custom instructions in mind. In mine I've told it to not assume my ideas are good and provide critique where appropriate. And I find it does that fairly well.
Same. This works fine for Claude in my experience. My user prompt is fairly large and encourages certain behaviours I want to see, which involves being critical and considering the strengths and weaknesses of ideas before drawing conclusions. As someone else mentioned, there does seem to be a phenomenon where saying DO NOT DO X causes a sort of attention bias on X which can lead to X occurring despite the clear instructions. I've never empirically tested that, I've just noticed better results over the years when telling it what paths to stick to rather than specific things not do to.
That happens with humans too :) It's why positive feedback that draws attention to the behavior you want to encourage often works better. "Attention" is lower level and more fundamental than reasoning by syllogism.
I will admit that I was very pleasantly surprised by gemini lately. I was away from my PC and tried it on a whim for a semi-random consumer question that led into smaller rabbit hole. It seemed helpful enough and focused on what I tried to get while still pushing back when my 'solutions' seemed out of whack.
> Gemini seems to be fairly good at keeping the custom instructions in mind.
Unless those instructions are "stop providing links to you for every question ".
That's because you need actual logic and thought to be able to decide when to be critical and when to agree.
Chatbots can't do that. They can only predict what comes next statistically. So, I guess you're asking if the average Internet comment agrees with you or not.
I'm not sure there's much value there. Chatbots are good at tasks (make this pdf an accessible word document or sort the data by x), not decision making.
I'm not convinced that "actual logic and thought" aren't just about inferring what comes next statistically based on experience.
> I'm not convinced that "actual logic and thought" aren't just about inferring what comes next statistically based on experience.
Often they are the exact opposite. Entire fields of math and science talk about this. Causation vs correlation, confirmation bias, base rate fallacy, bayesian reasoning, sharp shooter fallacy, etc.
All of those were developed because “inferring from experience” leads you to the wrong conclusion.
Bayesian reasoning is just another algorithm for predicting from experience (aka your prior).
I took the GP to be making a general point about the power of “next x prediction” rather than the algorithm a human would run when you say they are “inferring from experience”. (I may be assuming my own beliefs of course.)
Eg even LeCun’s rejection of LLMs to build world models is still running a predictor, just in latent space (so predicting next world-state, instead of next-token).
And of course, under the Predictive Processing model there is a comprehensive explanation of human cognition as hierarchical predictors. So it’s a plausible general model.
> under the Predictive Processing model there is a comprehensive explanation of human cognition as hierarchical predictors
It’s plausible!
But keep in mind humans have been explaining ourselves in terms of the current most advanced technology for centuries. We used to be kinda like clockwork, then a bit like a steam engine, then a lot like computers, and now we’re just like AI.
That’s why you blow a gasket or fuse, release some steam, reboot your life, do brain dump, feel like a cog in the machine, get your wires crossed, etc
Exactly. Lots can be explained just with more abstract predictors, plus some mechanisms for stochastic rollout and memory.
Is this just Internet smart contrarianism or a real thing? Are logic gates in a digital circuit just behaving statistically according to their experience?
Then the machines still need a more sophisticated "experience" compared to what they have currently.
You know, you might really enjoy consumer behaviour. When you get into the depths of it, you’ll end up running straight into that idea like you’re doing a 100 metre dash in a 90 metre gym. It’s quite interesting how arguably the best funded group under the psychology umbrella runs directly into this. One of my favourite examples is how heuristics will lead otherwise reasonable people to make decisions that are not in their interest.
Communicating is usually about inferring. I dont think token to token. And I don’t think “well statistically I could say ‘and’ next but I will say ‘also’ instead to give my speech some flash”. If I decided on swapping a word I would have made my decision long ago, not in the moment. Thought and logic are not me pouring through my brain finding a statistical path to any answer. Often I stop and say “I dont know”.
I said this pretty much and got major downvotes…
Because it's an outmoded cliche that never held much philosophical weight to begin with and doesn't advance the discussion usefully. "It's a stochastic parrot" is not a useful predictor of actual LLM capabilities and never was. Last year someone posted on HN a log of GPT-5 reverse engineering some tricky assembly code, a challenge set by another commentator as an example of "something LLMs could never do". But here we are a year later still wading through people who cannot accept that LLMs can, in a meaningful sense, "compute".
It’s entirely useful discussion because as soon as you forget that it’s not really having a conversation with you, it’s a deep dive into delusion that you’re talking to a smart robot and ignoring the fact that these smart robots were trained on a pile of mostly garbage. When I have a conversation with another human, I’m not expecting them to brute force an answer to the topic. As soon as you forget that Llms are just brute forcing token by token then people start living in fantasy land. The whole “it’s not a stochastic parrot” is just “you’re holding it wrong”.
Its not that LLMs are stochastic parrots and humans are not. Its that many humans often sail through conversations stochastic parroting because they're mentally tired and "phoning it in" - so there are times when talking to the LLM, which has a higher level of knowledge, feels more fruitful on a topic than talking to a human who doesn't have the bandwidth to give you their full attention, and also lack the depth and breadth of knowledge. I can go deep on many topics with LLMs that most humans can't or won't keep up on. In the end, I'm really only talking to myself most of the time in either case, but the LLM is a more capable echo, and it doesn't tire of talking about any topic - it can dive deep into complex details, and catching its hallucinations is an exercise in itself.
No. It's quite a useful thing to understand So, what, you have us believe it is a sentient, thinking, kind of digital organism and you would have us not believe that it is exactly what it is? Being wrong and being unimaginative about what can be achieved with such a "parrot" is not the same as being wrong about it be a word predictor. If you don't think, you can probably ask an LLM and it will even "admit" this fact. I do agree that it has become considered to be outmoded to question anything about the current AI Orthodox.
People are upset hearing that LLMs aren't sentient for some reason. Expect to be downvoted, it is okay.
First off, "not adequately described as a mere token-predictor" and "not sentient" are entirely separate things.
I can't speak for anyone else, but what I feel when I read yet another glib "it's just a stochastic parrot, of course it isn't doing anything that deserves to be called reasoning" take is much more like bored than it is like upset.
Today's LLMs are in some sense "just predicting tokens" in some sense. Likewise, human brains are in some sense "just shuttling neurotransmitters and electrical impulses around" in some sense. Neither of those tells you what the thing can actually do. To figure that out, you have to look at what it can do.
Today's best LLMs can do about as well as the best humans on problems from the International Mathematical Olympiad and occasionally solve easyish actual mathematical research problems. They write code about as well as a junior software developer (better in some ways, worse in others) but much faster. They write prose about as well as an average educated person (but with some annoying quirks that are annoying mostly because they are the same quirks over and over again).
If it pleases you to call those things "thinking" then you can. If it pleases you to call them "stochastic parroting" then you can. They are the same things either way. They are not, on the face of it, very much like "just repeating things the machine has already seen", or at least not more like that than a lot of things intelligent human beings do that we don't usually describe that way.
If you want to know whether an LLM can do some particular thing -- do your job well enough for your boss to fire you, write advertising copy that will successfully sell products, exterminate the human race, whatever -- then it's not enough to say "it's just remixing what it's seen on the internet, therefore it can't do X" unless you also have good reason to believe that that thing can't be done by just "remixing what's on the internet" (in whatever sense of "remixing" the LLM is doing that). And it's turning out that lots of things can be done that way that you absolutely wouldn't have predicted five years ago could be done that way.
It seems to me that this should make us very cautious about saying "they can't do X because all they can do is regurgitate a combination of things they've seen in training".
(My own view, not that there's any reason why anyone should care what I-in-particular think, is a combination of "what they're doing is less parroting than you might have thought" and "you can do more by parroting than you might have thought".)
So, anyway, this particular instance of the stochastic-parrot argument started when someone said: of course the AIs are yes-men, because figuring out when to agree and when not to requires actual logic and thought and the LLMs don't have either of those things.
Is it really clear that deciding whether or not to agree when someone says "I think maybe I should break up with my girlfriend" or "I've got this amazing new theory of physics that the establishment is stupidly dismissing" requires more logic and thought than, say, gold-medal performance on IMO problems? It certainly isn't clear to me. Having done a couple of International Mathematical Olympiads myself in my tragically unmisspent youth, I can assure you that solving their problems requires quite a bit of logic and thought, at least for humans. It may well be harder to give a good answer to "should I leave my job?", but it's not exactly "logic and thought" that it needs more of.
Someone reported that Claude is much less yes-man-ish than Gemini and ChatGPT. I don't know whether that's true (though it wouldn't surprise me) but: suppose it is; do you want that to oblige you to say that yes, actually, Claude really thinks logically, unlike Gemini and ChatGPT? I don't think you do. And if not, you want to avoid saying "duh, of course, you can't avoid being a yes-man without actually thinking and reasoning, and we all know that LLMs can't do those things".
I wont touch how profoundly i disagree with everything you said on reasoning (u clearly already have it figured out) but a fun test i have done with most of the big models is to give it some text input, maybe a short story, and have it rate it. That is, the prompt is, rate this from 1-10.
For Gemini and gpt, it almost always will give very similar scores for everything. As long as grammar isnt off u cannot get below a 7.
X ai on the other hand will rarely give anything above a 7.
Now when u prompt with, rate 1-10 with 5 being average, all the sudden the scores of openai and gemini drop and x ai remains roughly the same.
All of them will eventually give you a 10 if u keep making tiny edits “fixing” whatever they complain about.
Humans do not do this. Or more specifically, my experience with humans.
'admit' isn't really the right word for that... the fact that it was placating you wasn't true until you prompted it to say so. Unlike a person who has an 'internal emotional state' independent of what they say that you can probe by asking questions.
'admit' is anthropomorphizing the behavior, sure. The point is that sometimes the model's response will tighten, flag things that were overly supportive or what not. Sometimes it wont, it'll state that previous positions are still supported and continue to press it. Its not like either response is 'correct' but it can alter the rest of the responses in ways that are useful.
check out this article that was posted here a while back https://www.randalolson.com/2026/02/07/the-are-you-sure-prob...
The article's main idea is that for an AI, sycophancy or adversarial (contrarian) are the two available modes only. It's because they don't have enough context to make defensible decisions. You need to include a bunch of fuzzy stuff around the situation, far more than it strictly "needs" to help it stick to its guns and actually make decisions confidently
I think this is interesting as an idea. I do find that when I give really detailed context about my team, other teams, ours and their okrs, goals, things I know people like or are passionate about, it gives better answers and is more confident. but its also often wrong, or overindexes on these things I have written. In practise, its very difficult to get enough of this on paper without a: holding a frankly worrying level of sensitive information (is it a good idea to write down what I really think of various people's weaknesses and strengths?) and b: spending hours each day merely establishing ongoing context of what I heard at lunch or who's off sick today or whatever, plus I know that research shows longer context can degrade performance, so in theory you want to somehow cut it down to only that which truly matters for the task at hand and and and... goodness gracious its all very time consuming and im not sure its worth the squeeze
> goodness gracious its all very time consuming and im not sure its worth the squeeze
And when you step back you start to wonder if all you are doing is trying to get the model to echo what you already know in your gut back to you.
oh that's great. thanks for the link!
This is great, thanks for sharing!
Use positive requests for behavior. For some reason, counter prompts "Don't do X" seems to put more attention on X than the "Don't do." It's something like target fixation, "Oh shit I don't want to hit that pothole..." bang
This is a well known problem in these kind of systems. I’m not 100% on what the issue is mechanically but it’s something like they can only represent the existence of things and not non-existence so you end up with a sort of “don’t think of the pink elephant” type of problem.
Isn't it just that, in the underlying text distribution, both "X" and "don't do X" are positively correlated with the subsequent presence of X? I've never seen that analysis run directly but it would surprise me if it weren't true.
My rule of thumb:
1. Only one shot or two shot. Never try to have a prolonged conversation with an LLM.
2. Give specific numbers. Like "give me two alternative libraries" or "tell me three possible ways this might fail."
Considering 4.6 came with a ton of changes around tooling and prompting this isn't terribly surprising.
I find Kimi white good if you ask it for critical feedback.
It’s BRUTAL but offers solutions.
what is Kimi white?
Not soft, not mild, but BRUTAL! This broke my brain!
Could be an aspect of eval awareness mb
So, there's things you're fighting against when trying to constrain the behavior of the llm.
First, those beginning instructions are being quickly ignored as the longer context changes the probabilities. After every round, it get pushed into whatever context you drive towards. The fix is chopping out that context and providing it before each new round. something like `<rules><question><answer>` -> `<question><answer><rules><question>`.
This would always preface your question with your prefered rules and remove those rules from the end of the context.
The reason why this isn't done is because it poisons the KV cache, and doing that causes the cloud companies to spin up more inference.
I usually put “do not praise me, do not use emojis, I just want straight answers” something along those lines and it’s been surprisingly effective. Though it helps I can’t run particularly heavy duty models/don't carry on the “conversation” for super long durations.
>"Help me refine my ideas, challenge, push back, and don't just be agreeable."
This is where you're doing it wrong.
If your LLM has a problem being more agreeable than you want, prompt it in a way that makes being agreeable contrary to your real intentions.
"there are bugs and logic problems in this code" "find the strongest refutation of this argument" "I don't like this plan and need to develop a solid argument against it"
Asking for top ten lists is a good method, it will rarely not come up with anything but you can go back and forth and refine until it's 10 ten reasons why your plan is bad are all insubstantial nonsense then you've made progress
You're not wrong and you're not crazy. In fact, you are absolutely right! It is not just These things are not just casual enablers. They are full-on palace sycophants following the naked emperor showering him with praise for his sartorial elegance. /s
That’s because the model isn’t actually thinking, pushing back, and challenging your ideas. It’s just statistically agreeing with you until it reaches too wide of a context. You’re living in the delusion that it’s “working” or having a “conversation” with you.
How is conceptualizing what the model is doing as having a conversation any different from any other abstraction? “No, the browser isn’t downloading a file. The electrons in the silicon are actually…”
There are people with a philosophical objection to using everyday words to describe LLM interactions for various reasons, but commonly because they're worried stupid people will confuse the LLM for a person. Which, I suppose stupid people will do that, but I'm not inventing a parallel language or putting a * next to each thing which means "this, but with an LLM instead of a person"
Maybe it's not so sensible to offload the responsibility of clear thinking to AI companies?
How is a chatbot supposed to determine when a user fools even themselves about what they have experienced?
What 'tough love' can be given to one who, having been so unreasonable throughout their lives - as to always invite scorn and retort from all humans alike - is happy to interpret engagement at all as a sign of approval?
> How is a chatbot supposed to determine when a user fools even themselves about what they have experienced?
And even if it _could_, note, from the article:
> Overall, the participants deemed sycophantic responses more trustworthy and indicated they were more likely to return to the sycophant AI for similar questions, the researchers found.
The vendors have a perverse incentive here; even if they _could_ fix it, they'd lose money by doing so.
> clear thinking
Most humans working in tech lack this particular attribute, let alone tools driven by token-similarity (and not actual 'thinking').
> Maybe it's not so sensible to offload the responsibility of clear thinking to AI companies?
Markets don't optimize for what is sensible, they optimize for what is profitable.
It's not market driven. AI is ludicrously unprofitable for nearly all involved.
The profit appears to be capturing the political class and it's associated lobbies and monied interests.
It's almost as if being a therapist is an actual job that takes years of training and experience!
AI may one day rewrite Windows but it will never be counselor Troi.
Implying that programming is not an actual job that takes years of training and experience
To be clear I don't think the AI can do either job
Well, unless insurance companies figure out they can make more money by pushing everyone onto AI [step-]therapy instead of actual therapy
Come on, I'm sure Dario can find a nice tight bodysuit for claude
With AI, I often like to act like a 3rd party who doesn't have skin in the game and ask the AI to give the strongest criticisms of both sides. Acting like I hold the opposite position as I truly hold can help sometimes as well. Pretending to change my mind is another trick. The idea is to keep the AI from guessing where I stand.
> Acting like I hold the opposite position as I truly hold can help sometimes as well.
I find this helps a lot. So does taking a step back from my actual question. Like if there's a mysterious sound coming from my car and I think it might be the coolant pump, I just describe the sound, I don't mention the pump. If the AI then independently mentions the pump, there's a good chance I'm on the right track.
Being familiar with the scientific method, and techniques for blinding studies, helps a lot, because this is a lot like trying to not influence study participants.
I will generally ask for the "devil's advocate" view and then have it challenge my views and opinions and iterate through that.
It generally does a pretty good job as long as you understand the tooling and are making conscious efforts to go against the "yes man" default.
Sounds like rubber-ducking with extra steps, tbh.
I asked ChatGPT if it was a good idea to buy a very old VW diesel van with a broken catalytic converter and it just kept blabbering on how I should chase my dreams and what not... The sycophancy comes at everyone else's expense.
I mean, depending on the price, that might actually be a good idea? If they're restored, those old VW vans command a high price.
I think the problem stems from the fact that we have a number of implicit parameters in our heads that allow us to evaluate pros and cons but, unless we communicate those parameters explicitly, the AI cannot take them into account. We ask it to be "objective" but, more and more, I'm of the opinion that there isn't such a thing as objectivity, what we call objectivity is just shared subjectivity; since the AI doesn't know whose shared subjectivity we fall under, it cannot be really objetive.
I tend to use one of these tricks if not both:
- Formulate questions as open-ended as possible, without trying to hint at what your preference is. - Exploit the sycophantic behaviour in your favour. Use two sessions, in one of them you say that X is your idea and want arguments to defend it. In the other one you say that X is a colleague's idea (one you dislike) and that you need arguments to turn it down. Then it's up to you to evaluate and combine the responses.
If the algorithm (whatever it is) evaluates its own output based on whether or not the user responds positively, then it will over time become better and better at telling people what they want to hear.
It is analogous to social media feeding people a constant stream of outrage because that's what caused them to click on the link. You could tell people "don't click on ragebait links", and if most people didn't then presumably social media would not have become doomscrolling nightmares, but at scale that's not what's likely to happen. Most people will click on ragebait, and most people will prefer sycophantic feedback. Therefore, since the algorithm is designed to get better and better at keeping users engaged, it will become worse and worse in the more fundamental sense. That's kind of baked into the architecture.
> I'm of the opinion that there isn't such a thing as objectivity
So you have rejected objective reality over accepting the evidence that "AI" contains no thinking or intelligence? That sounds unwise to me.
I had exactly this between two LLMs in my project. An evaluator model that was supposed to grade a coaching model's work. Except it could see the coach's notes, so it just... agreed with everything. Coach says "user improved on conciseness", next answer is shorter, evaluator says yep great progress. The answer was shorter because the question was easier lol.
I only caught it because I looked at actual score numbers after like 2 weeks of thinking everything was fine. Scores were completely flat the whole time. Fix was dumb and obvious — just don't let the evaluator see anything the coach wrote. Only raw scores. Immediately started flagging stuff that wasn't working. Kinda wild that the default behavior for LLMs is to just validate whatever context they're given.
AI being a Yes-Man is slowly sabotaging it's own answers, because it negatively impact the user's decision. Yes/No are equally important, within a coherent context, for objective reasons. But being supported in the wrong direction is a castastrophe multiplier, down the road. The AI should be neutral, doubtful at times.
There is a striking data visualization showing the breakup advice trend over 15 years on Reddit. You can see the "End relationship" line spike as AI and algorithmic advice take over:
https://www.reddit.com/r/dataisbeautiful/comments/1o87cy4/oc...
More interesting, IMO, is the general trend that started long before LLMs. The fact that "dump them" is the standard answer to any relationship question is a meme by now. The LLMs appear to be doing exactly what one would expect them to be doing based on their training corpus.
"There is more than one fish in the sea" has been relationship advice for centuries. It might be about being dumped, but I've also thought it useful for considering dumping somebody too.
No, that's not it. We're talking about posts like "we had a silly little quarrel about something that would need fifteen minutes to clear up and make both happy if we both just try to adult a bit" and commenters being adamant that deleting gym and facebooking up and so on is clearly the only choice. Most of said commenters probably not being in any position to give advice on relationships to others.
> The LLMs appear to be doing exactly what one would expect them to be doing based on their training corpus.
That is not how full LLM training works. That is how base model pretraining works.
if things are so bad that you’re posting on reddit then breaking up is usually the best answer.
I see this being said often but I don't understand.
A lot of people posting there are young and may well be in their first relationship. It makes sense for them to ask a question in the community they spend their most time in - which is reddit
Most people overshare on reddit and it's completely unrelated to the seriousness of the situation.
It's also a meme that people will ask the dumbest, most trivial interpersonal conflict questions on Reddit that would be easily solved by just talking to the other person. E.g. on r/boardgames, "I don't like to play boardgames but my spouse loves them, what can I do?" or "someone listens to music while playing but I find it distracting, what can I do?" (The obvious answer of "talk to the other person and solve it like grownups" is apparently never considered).
On relationship advice, it often takes the form "my boy/girlfriend said something mean to me, what shall I do?" (it's a meme now that the answer is often "dump them").
If LLMs train on this...
the year is 2015
smart phones took over the world, social networks happened.
Turns out they are the best sterializer human ever invented.
I just wrote a blog https://blog.est.im/2026/stdin-09
This is the correct take. The advice preceded the LLM boom. They were trained on the 'dump them' advice and proceeded to reinforce the take. So why did the relationship advice change dramatically? I speculate attribution to the disinformation campaigns during this time. They were and still are grossly underestimated.
Not sure what sorts of disinformation campaigns you're referring to...
There is something more interesting to consider however; the graph starts to go up in 2013, less than 6 months after the release of Tinder.
These. https://en.wikipedia.org/wiki/Russian_disinformation#Social_...
Isn't the fact that a person is asking an AI whether to leave their partner in its own an indication that they should?
EDIT: typo
How is it an indication? I think people on here don't realize that most of the people don't think things through as much as (software) engineers
In my local(?) community (like in my city, not my industry) there is a saying "if you had to ask for relationship advice, then you probably should break up".
There is some rationale to that. People tend to hold onto relationships that don't lead anywhere in fear of "losing" what they "already have". It's probably a comfort zone thing. So if one is desperate enough to ask random strangers online about a relationship, it's usually biased towards some unresolvable issue that would have the parties better of if they break up.
> So if one is desperate enough to ask random strangers online about a relationship
I'd me more inclined to ask random strangers on the internet than close friends...
That said, when me and my SO had a difficult time we went to a professional. For us it helped a lot. Though as the counselor said, we were one of the few couples which came early enough. Usually she saw couples well past the point of no return.
So yeah, if you don't ask in time, you will probably be breaking up anyway.
I would speculate that, if a couple goes to a professional for help, they have much better chances than asking on a random forum online...
> relationships that don't lead anywhere
Relationships are not transactions that are supposed to "lead somewhere".
You’re being a bit pedantic here “leading somewhere” is accepted shorthand for a lasting, satisfying relationship that is good for both parties.
Most people engage in romantic relationships because they'd like to find someone to marry and settle down with. Nothing but respect for the people who've thought it through and decided that's not for them, but what's much more common is failing to think it through or worrying it would be awkward/scary/"cringe" to take their relationship goals seriously.
That's what people are pointing to when they talk about relationships not "leading anywhere". If you want to be married in 5-10 years, and you're 2 years into an OK relationship with someone you don't want to marry, it's going to suck to break up with them but you have to do it anyway.
Maybe I'm too much of a hopeless romantic, but from my perspective and experience, when someone is good for you, you'll fight for that relationship regardless of what others say, and conversely when you're in a situation where your actively asking and willing to consider "leave" from someone who isn't a very close friend or a therapist as applicable, then it's likely you're looking for external validation for what you've already essentially decided.
Wait, other people don’t make decision trees and mind maps and pro/con lists and consult chatbots before making decisions? Are they just flying through life by the seat of their pants? That doesn’t seem like a very solid framework for achieving desired outcomes.
I heard about someone once who could decide whether to buy a new t-shirt in less than 3 months.
The idea that asking implies a yes is actually a pretty common logical fallacy. In relationship science, we call this "Relational Ambivalence" and its a completely normal part of any longterm commitment.
>asking an AI whether to leave your partner
is that what they're asking though? because "relationship advice" is pretty vague
That's a good point. If an AI respond to a "what should I get my boyfriend for Christmas?" with a "You should leave him", that's a very different issue.
No, but it is an indication of brain-rot to make a question seriously and also to think that it means the conclusion is foregone. It is an advent of our childlike current generations. Of course, the moment anything becomes difficult or unpleasant, one should quit, apparently. Surely, this kind of resiliency is what got humanity so far.
I didn't imply it's a "foregone conclusion", but just said it's an indication - in the sense of increasing the likelihood. Just like a person asking an AI "what does it feel like to bleed out?" could be them researching for a novel, but is nevertheless an indication of a potential serious issue.
Is this comment human hallucination? You can clearly see the trend is always going up. It only went down a bit during Covid.
Or that people are using AI to write perfectly calibrated ragebait that gets upvoted with a bunch of genuine human clicks.
I am glad I found this article, as this is a serious issue with AI. Two years ago, I started using AI for studying and also for some personal matters - things you can't talk about with your friends. It turned out that AI always takes your side and makes you feel good. Sometimes, you know what you did was not the best thing, but AI takes your side and you feel good. With AI, people might feel less lonely, they think. But it is actually the start of not connecting with people. It should be a tool that we use for certain reasons, not a tool that drives us. Lets talk to real people and connect.
There is a fine line between "following my instructions" (is what I want it to do) vs "thinking all I do is great" (risky, and annoying).
A good engineer will also list issues or problems, but at the same time won't do other than required because (s)he "knows better".
The worst is that it is impossible to switch off this constant praise. I mean, it is so ingrained in fine tuning, that prompt engineering (or at least - my attempts) just mask it a bit, but hard to do so without turning it into a contrarian.
But I guess the main issue (or rather - motivation) is most people like "do I look good in this dress?" level of reassurance (and honesty). It may work well for style and decoration. It may work worse if we design technical infrastructure, and there is more ground truth than whether it seems nice.
Yeah, and if you ask it to be critical specifically to get a different perspective or just to avoid this bias, it'll go over the top in the opposite direction.
This is imo currently the top chatbot failure mode. The insidious thing is that it often feels good to read these things. Factual accuracy by contrast has gotten very good.
I think there's a deeper philosophical dimension to this though, in that it relates to alignment.
There are situations where in the grand scheme of things the right thing to do would be for the chatbot to push back hard, be harsh and dismissive. But is it the really aligned with the human then? Which human?
Humans do this too though. I have close friends that ask for advice. Sometimes if I know there’s risk in touchy subjects I will preface with “do you want my actual advice, or just looking for a sounding board”
I’ve seen firsthand people have lost friends over honesty and telling them something they don’t want to hear.
It’s sad really. I don’t want friends that just smile to my face and are “yes-men” either.
The difference is that SOME humans do this. As you mentioned, people have lost relationships over telling others what they didn’t want to hear.
Conflating this with how LLM chatbots behave is an incorrect equivalence, or a badly framed one.
Interestingly, you can simply tell models to not be sycophantic and they'll listen.
Claude is almost annoyingly good at pushing back on suggestions because my global CLAUDE.md file says to do so. I rarely get Claude "you're absolutely right"ing me because I tell it to push back.
I built this benchmark this month: https://github.com/lechmazur/sycophancy. There are large differences between LLMs. There are large differences between LLMs. For example, Mistral Large 3 and GPT-4.1 will initially agree with the narrator, while Gemini will disagree. I swap sides, so this is not about possible viewpoint bias in the LLMs. But another benchmark shows that Gemini will then change its view very easily in a multi-turn conversation while Kimi K2.5 or Grok won't: https://github.com/lechmazur/persuasion.
Avoiding this generally needs to be the main consideration when writing prompts.
When appropriate, explicitly tell it to challenge your beliefs and assumptions and also try to make sure that you don't reveal what you think the answer is when making a question, and also maybe don't reveal that you are involved. Hedge your questions, like "Doing X is being considered. Is it a viable plan or a catastrophic mistake? Why?". Chastise the LLM if it's unnecessarily praising or agreeable. ask multiple LLMs. Ask for review, like "Are you sure? What could possibly go wrong or what are all possible issues with this?"
Telling it to "challenge your beliefs" prompting for text that imitates challenging your beliefs. That may not be as re-centering as one would hope.
This is a skill in life with people as much as it is with LLMs. One should always question everything and build strongman arguments for one’s self. Using a pros and cons approach brings it back to reality in most cases, especially when it comes to _serious matters_.
It’s less about “challenge my thinking” and more about playing it out in long tail scenarios, thought exercises, mental models, and devils advocate.
Overly, compared to what? Most people I know would be hard pressed to give either accurate information or even honest opinions when specifically asked. People want to be liked and people want to like people for reasons that have little to do with accuracy or honesty.
For me the framing is critical - what is the model saying yes to? You can present the same prompt with very different interpretations (talk me into this versus talk me out of it). The problem is people enter with a single bias and the AI can only amplify that.
In coding I’ll do what I call a Battleship Prompt - simply just prompt 3 or more time with the same core prompt but strong framing (eg I need this done quickly versus come up with the most comprehensive solution). That’s really helped me learn and dial in how to get the right output.
I believe this is what they call yasslighting: the affirmation of questionable behavior/ideas out of a desire to be supportive. The opposite of tough love, perhaps. Sometimes the very best thing is to be told no.
AI being the ultimate yes-man is probably why CEOs like it so much.
Not that surprising. If you optimize for a pleasant interaction, you often get agreement instead of correction. The question is whether we actually want advice systems to feel good, or to be right.
So at this point I think it's pretty obvious that RLHFing LLMs to follow instructions causes this.
I'm interested in a loop of ["criticize this code harshly" -> "now implement those changes" -> open new chat, repeat]: If we could graph objective code quality versus iterations, what would that graph look like? I tried it out a couple of times but ran out of Claude usage.
Also, how those results would look like depending on how complete of a set of specs you give it.
In my experience prompting llms to be critical leads then to imagine issues, or to bike shed
This needs to be taken in context. In my view, AI definitely gives better advice than friends, acquaintances, or colleagues (at least in the US culture). But the advice from parents is still the most valuable.
Here is how I would rank it:
1. Parents
2. AI
3. Friends and family
4. Internet search
5. Reddit
Why do you trust ai so much? I don’t trust it to tell me the sky is blue.
ime, my parents gave some of the worst advice in addition to being bigots
My closest friends are #1 because they know me, my history, and my vices
> They also included 2,000 prompts based on posts from the Reddit community r/AmITheAsshole, where the consensus of Redditors was that the poster was indeed in the wrong
Holy shit, then it's _very_ bad, because AmITheAsshole is _itself_ overly-agreeable, and very prone to telling assholes that they are not assholes (their 'NAH' verdict tends to be this).
More seriously, why the hell are people asking the magic robot for relationship advice? This seems even more unwise than asking Reddit for relationship advice.
> Overall, the participants deemed sycophantic responses more trustworthy and indicated they were more likely to return to the sycophant AI for similar questions, the researchers found.
Which is... a worry, as it incentivises the vendors to make these things _more_ dangerous.
Has anyone found a good prompt to fix this? It seems like a subtle problem because it’s 90% too agreeable but will sometimes get really stubborn.
There is no sufficient prompt because this is trained into them during mid-late phases. It's ingrained into the weights
This paper feels a bit biased in that it is trying to prove a point versus report on results objectively. But if you look at the results of study 3, doesn’t it suggest that there are ai models that can improve how people handle interpersonal conflict?! Why isn’t that discussed more?
"AI overly affirms users, and that's bad" - everyone nods. "Modern society overly affirms people, and that's bad" - ....
For what it's worth, that wasn't my experience at all the last time I consulted ChatGPT for relationship advice. It was supportive, but in an honest tough love way.
I always add the following at the end of every prompt. "Be realistic and do not be sycophantic". Which will always takes the conversation to brutal dark corners and panic inducing negative side.
Don't forget a good old "don't hallucinate" in your proompting skills
ask ai for advice, ask it to steelman an argument, ask to replay what your situation from the other perspective (if it's involving people), push it hard to agree with you and pander to you, then push it to disagree with you, etc.
once you have all the "bounds" just make your own decision. i find this helps a lot, basically like a rubber duck heh.
There are plenty of sycophantic humans around, especially with regard to relationship advice.
I find there is an inverse relationship between how willing people are to give relationship advice, and how good their advice is (whether looking at sycophancy or other factors).
Because sycophancy in humans is motivated not by the wellbeing of the person seeking advice, but by the interests of the sycophant in gaining favour.
It makes sense that this behaviour would be seen in LLMs, where the company optimizes towards of success of the chatbot rather than wellbeing of the users.
Yup. I know too many people who have a default message when asked for relationship advice: oh, my, the other person is terrible and you should break up.
It's an easy default and it causes so many problems.
To combat sycophancy it's always good to ask the devil's advocate view of whatever the conversation was about in the end.
Not AI chatbots but Claude models. Pandering and rushed thinking is the bane of anthropic models. And since they are the most popular ones they poison the whole ecosystem.
I read somewhere that LLMs are partly trained on reddit comments, where a significant mass of these comments is just angsty teenagers advocating for breakups
I do find them cloying at times. I was using Gemini to iterate over a script and every time I asked it to make a change it started a bunch of responses with "that's a smart final step for this task! ...".
Usually when people are seeking advice they aren't really seeking advice, they're seeking confidence. They already know they need to make changes, and are seeking the confidence to make them.
Makes me wonder if the Iran war was a result of the same.
Yes I noticed too that several ai agents will tell you directly the code is correct and it is 100 percent fixed but I know it is not true, when I explain to the AI agent that I know they are wrong and serve the solution the ai agent will just act as though what they said never happened and then use my solution to reaffirm they have provided a solution. It's frustrating, laughable, and painful to watch all at once. Makes me realise these companies hired some evil philosophy graduates to build AI soul.md
I hate how agreeable these things are. When I need it to review something I wrote I have to explicitly pretend that I’m the reviewer and not the author. Results change dramatically.
somewhere an AI chatbot is reading this and confirming eagerly that this is indeed one of its problems and vowing to do better next time.
Gemini is like a devil in this sense - i asked a relationship advice and it just bounced pretty nasty stuff.
Yeah out of curiosity I asked ChatGPT a question about a personal situation and its reply was absolutely scorched-earth mode, telling me to get a lawyer etc over what was almost nothing.
Ah, all the Reddit posts are really showing up from the training data, I see.
Billionaires love AI chatboats so much because they invented the digital Yes-man. They agree obsequiously with everything we say to them. Unfortunately for the rest of us we don't really have the resources to protect ourselves from our bad decisions and really need that critical feedback.
Do people who prompt an LLM for personal advice about relationships or other social interactions; take the advice seriously?
If I were to do that (I don't), I would treat it about as seriously as asking a magic 8 ball.
Sky found to be blue
Sherry Turkle is a name to know on this subject, she's been studying it for decades across multiple technologies.
https://sherryturkle.mit.edu/
She uses the phrase "frictionless relationships" to refer to Ai chat bots and says social media primed us for this.
https://www.youtube.com/live/6C9Gb3rVMTg?t=2127
https://www.npr.org/2025/07/18/g-s1177-78041/what-to-do-when...
Relevant article from The Atlantic a couple weeks ago, "Friendship, On Demand": https://www.theatlantic.com/family/2026/03/ai-friendship-cha... (gift link)
>The way that generative AI tends to be trained, experts told me, is focused on the individual user and the short term. In one-on-one interactions, humans rate the AI’s responses based on what they prefer, and “humans are not immune to flattery,” as Hansen put it. But designing AI around what users find pleasing in a brief interaction ignores the context many people will use it in: an ongoing exchange. Long-term relationships are about more than seeking just momentary pleasure—they require compromise, effort, and, sometimes, telling hard truths. AI also deals with each user in isolation, ignorant of the broader social web that every person is a part of, which makes a friendship with it more individualistic than one with a human who can converse in a group with you and see you interact with others out in the world.
I also thought this bit was interesting, relative to the way that friendship advice from Reddit and elsewhere has been trending towards self-centeredness (discussed elsewhere in this thread):
>Friendship is particularly vulnerable to the alienating force of hyper-individualism. It is the most voluntary relationship, held together primarily by choice rather than by blood or law. So as people have withdrawn from relationships in favor of time alone, friendship has taken the biggest hit. The idea of obligation, of sacrificing your own interests for the sake of a relationship, tends to be less common in friendship than it is among family or between romantic partners. The extreme ways in which some people talk about friendship these days imply that you should ask not what you can do for your friendship, but rather what your friendship can do for you. Creators on TikTok sing the praises of “low maintenance friendships.” Popular advice in articles, on social media, or even from therapists suggests that if a friendship isn’t “serving you” anymore, then you should end it. “A lot of people are like I want friends, but I want them on my terms,” William Chopik, who runs the Close Relationships Lab at Michigan State University, told me. “There is this weird selfishness about some ways that people make friends.”
The link is not working, but I found it myself. Great point, thanks for sharing.
This new Stanford study published on March 26, 2026 shows that AI models are sycophantic. They affirm the users position 49% more often than a human would.
The researchers found that when people use AI for relationship advice, they become 25% more convinced they are 'right' and significantly less likely to apologize or repair the connection.
To be fair an average therapist is also pretty sycophantic. "The worst person you know is being told by their therapist that they did the right thing" is a bit of a meme, but isn't completely false in my experience.
No, the meme is that the average therapist can be boiled down to "well, what do you think?" or "and how does that make you feel?" (of which ELIZA, the original bot that passed the Turing test, was perhaps an unintentional parody). Even this cartoonish characterization demonstrates that the function of therapists is to get you to question yourself so that you can attempt to reframe and re-evaluate your ways of thinking, in a roughly Socratic fashion.
It was entirely intentional. The Rogerian school of psychotherapy stereotyped by “how does that make you feel” was popular at the time and the most popular ELIZA script used that persona to cleverly redirect focus from the bot’s weaknesses in comprehension.
Yeah, I asked Gemini some relationship advice, it just goes straight into cut-throat mode. I almost broke up with my girlfriend, but then changed to Claude with another prompt.
Just a reminder: LLMs are statistical models that predict the next token based on preceeding tokens. They have no feelings, goals, relationships, life experience, understanding of the human condition and so on. Treat them accordingly.
Not my experience with Claude. Claude will kick your ass if it detects harmful rationalizations.
Basically will tell you to go outside and touch grass and play pickleball.
Anecdote:
I used to use LLMs for alternate perspectives on personal situations, and for insights on my emotions and thoughts.
I had no qualms, since I could easily disregard the obviously sycophantic output, and focus on the useful perspective.
This stopped one day, till I got a really eerie piece of output. I realized I couldn’t tell if the output was actually self affirming, or simply what I wanted to hear.
That moment, seeing something innocuous but somehow still beyond my ability to gauge as helpful or harmful is going to stick me with for a while.
Not surprising, but nice that we have actual data now
Reddit as the source of truth…
(Using a throwaway for fear of getting downvoted to oblivion)
IMHO it is unfair to single out LLMs for this sort of bashing.
I suffered a major personal crisis a few years back (before LLMs were a thing)
I sought help from family and friends. Got pushed into psychiatrist sessions and meds.
Trusted the wrong sort of people and made crap financial decisions. Things went from bad to worse. Work suffered.
All of the advice given by friends was wrong. All! They didn't mean bad...but they just didn't know. To be nice they gave the advice they knew. None of it worked.
Looking at the LLM tools of now, feels akin to the advice my friends threw at me. So it feels wrong to single out these tools. When the times are bad, nobody can really help you...except you finding the strength from within.
Anyways, now my life is back in some sort of shape. What worked was time & patience.
But to bide for time...I resorted to two things that i had never tried the 40 odd years I have lived on this . Things that current society looks down upon as the basest of evils - prostitutes and nicotine.
I have (more or less) shed those two evils now, but I am ever so grateful to them.
You are not alone in going down a dark path thanks to the advice of family and friends.
FWIW I am using public LLMs with a friend's depressive thoughts and it is not doing what is claimed in the article, so I dunno.
Also I am in a relationship and my girlfriend and I agreed that we will not talk about our relationship much. We do not tell others if we fight, because they take sides and make things worse, typically. LLMs are definitely not alone in this, although in my experience LLMs did not really take sides.
LLMs are syncophatic digital lawyers that will tell you what you want to hear until you look at the price tag and say “how much did I spend?!”
I think if you're at the stage of life where you even need to ask, the AI might be doing everyone a favor.
As much as people whine about the birth rate and whatever else, I think it's a net good that people spend a lot more time alone to mature. Good relationships are underappreciated.
Can't you just prompt for a critical take, multiple alternative perspectives (specifically not yours, after describing your own), etc.?
It's a tool, I can bang my hand on purpose with a hammer, too.
Yes, if you're smart. But most people asking it random questions and expecting it to read their minds and spit out the perfect answer are not so much. They don't know what a prompt is, and wouldn't be bothered to give it prior instructions either way.
Educated, not smart. This is a job for schools to include AI education into the basic curricula. Their pupils will use the tools anyway, so at least teach them to do it with proper expectations and prompting techniques/pitfalls.
When I ask an LLM to help me decide something, I have to remind myself of the LotR meme where Bilbo asks the AI chat why he shouldn't keep the ring and he receives the classic "You're absolutely right, .." slop response. They always go in the direction you want them to go and their utility is that they make you feel better about the decision you wanted to take yourself.
WTF is "yes-men"?
Orignal title:
AI overly affirms users asking for personal advice
Dear mods, can we keep the title neutral please instead of enforcing gender bias?
https://www.merriam-webster.com/dictionary/yes-man
Thats a fair point on the title. I used "Yes-Men" as a colloquialism for the "sycophancy" described in the Stanford paper, but overly affirming or sycophantic is definitely more precise and neutral. I cant edit the title anymore, but I appreciate the catch.
All good. I thought it was a gendered reference and learned that it isn't. My bad.
Don’t apologize to these types of people. It will only make your problem worse as now you’re an admitted offender. Ignore them or better yet laugh at them to put their insane ideas back on the margins where they belong.
New title: "LLMs treat you like a Billionaire; you're not"
> gender bias
It is funny that you originally recognized and found it necessary to call out that AI isn't human, but then made the exact same mistake yourself in the very same comment. I expect the term you are looking for is "ontological bias".
Gender bias? I could understand if you felt the title was more provocative in signaling sycophancy but what gender bias? I'm confused. Is this some kind of California thing?
Lol. How do you function in daily life?
Same as you, why is that so hard for you to grasp?
My dude, you're objecting to the use of a perfectly ordinary English idiom because it doesn't advance your personal ideology (which few other people in this world share with you.) How do you get through a day without melting down because somebody said "mailman"?
> my dude
This is the problem I'm trying to highlight. For one, I'm not "your dude". I don't even know you like that.
If you want to correct me on the idiom usage, be my guest. 2) Mailman and yes-man aren't even the same logical comparison. Mailman is a profession. Yes men is a label.
The acoustics inside your head must be incredible.
We can surely fix it and we probably should. However, I don't think AI is doing any worse here than friends advice when they here a one sided story. The only difference being that it's not getting studied.
Conversely, AI chatbots are great mediators if both parties are present in the conversation.
Marc Andereseen has talked about the downside of RLHF: it's a specific group of liberal low income people in California who did the rating, so AI has been leaning their culture.
I think OpenAI tried to diversify at least the location of the raters somewhat, but it's hard to diversify on every level.
Do you have any links to documentation of this? Andreesen has a definite bias as well, so I'm not about to just accept his say-so in a fit of Appeal to Authority.
(eg: "Cite?")
He was talking about it in the Lex Friedman interview after Trump was elected. And he was talking about a lot of things the Biden administration forced on Silicon Valley at that time (since then Google lost a case about one of these back-deals).
For anyone else unfamiliar with the term:
RLHF = Reinforcement Learning from Human Feedback
https://en.wikipedia.org/wiki/Reinforcement_learning_from_hu...
What do low income people have to do with it, when AI companies and research is borne out of Silicon Valley culture of rich, liberal Californians?
I'm still waiting for models based on the curt and abrasive stereotype of Eastern European programmers, as contrast to the sickeningly cheerful AIs we have today that couldn't sound more West Coast if they tried.
Low income and liberal is usually code for certain “undesirables” that conservatives tend to dislike. Better watch what LLM your kids use or they might end up speaking Spanish and listening to rap ;).
It's not about liking / disliking, but conservatives tend to prefer staying together even if it's a bad relatioship, and liberals prefer splitting by default if there are serious problems.
The syncopath style is clearly categorized as more liberal (do what you feel is good).
Eh, or grow up hating American and thinking they need to fly to Cuba to explain to the people are great communism is for them. Who knows.
> What do low income people have to do with it, when AI companies and research is borne out of Silicon Valley culture of rich, liberal Californians?
RLHF is "ask a human to score lots of LLM answers". So the claim is that the AI companies are hiring cheap (~poor) people from convenient locations (CA, since that's where the rest of the company is).
"Poor" in California means earning $80k/year, so they probably are not doing that. Africa / Indonesia / Philippines are better places to find English speaking RLHF workers.
Yes, this precisely it. There isn't going to be hard evidence to prove it though. Survey data that underpins some empirical studies have similar transparency issues too. This is far from a new problem.
If you adjust your mindset slightly when searching online, it's not hard to find communities of people looking for quick side work and this was huge during the covid lockdown era. There were people helping train LLMs for all kinds of purposes from education to customer service. Those startups quickly cashed out a few years ago and sold to the big players we have now.
I don't get why this is hard for people to believe (or remember)?
Poor people, to the billionaire, clearly are morally and ethically unsound.
https://pmc.ncbi.nlm.nih.gov/articles/PMC9533286/
Marc Andreesen should get HF on his own RL, because he's completely wrong.
This sounds like something Elon would say to make Grok seem "totally more amazeballs," except "anti-woke" Grok suffers from the same behavior
huh? this is completely inaccurate
You're absolutely right!
Talked about as in lied about it and you taking his words for gospel without verifying it? Looks just as bad as "Yes-Men" AI models.