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.
gAI 14 minutes ago [-]
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.
dimgl 2 minutes ago [-]
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. I think the study is correct... however I'm of the opinion that ultimately it's the user's fault, not the LLM. LLMs are 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.
I do think that the LLMs have gotten much better though, especially Claude, and will often push back on bad choices. But yes this is an issue and I can't imagine how I'd react as a teenager with these powerful tools.
awithrow 51 minutes ago [-]
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.
rsynnott 4 minutes ago [-]
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.)
magicalhippo 40 minutes ago [-]
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.
lelanthran 4 minutes ago [-]
> 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 ".
steve_adams_86 33 minutes ago [-]
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.
koverstreet 14 minutes ago [-]
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.
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
oldfrenchfries 9 minutes ago [-]
This is great, thanks for sharing!
awithrow 26 minutes ago [-]
oh that's great. thanks for the link!
cruffle_duffle 27 minutes ago [-]
> 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.
Loughla 39 minutes ago [-]
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.
kvirani 35 minutes ago [-]
I'm not convinced that "actual logic and thought" aren't just about inferring what comes next statistically based on experience.
theptip 19 minutes ago [-]
Exactly. Lots can be explained just with more abstract predictors, plus some mechanisms for stochastic rollout and memory.
Swizec 28 minutes ago [-]
> 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.
theptip 4 minutes ago [-]
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.
dinkumthinkum 11 minutes ago [-]
Is this just Internet smart contrarianism or a real thing? Are logic gates in a digital circuit just behaving statistically according to their experience?
plagiarist 32 minutes ago [-]
Then the machines still need a more sophisticated "experience" compared to what they have currently.
righthand 30 minutes ago [-]
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”.
righthand 33 minutes ago [-]
I said this pretty much and got major downvotes…
dTal 22 minutes ago [-]
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".
dinkumthinkum 2 minutes ago [-]
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.
righthand 17 minutes ago [-]
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”.
plagiarist 12 minutes ago [-]
People are upset hearing that LLMs aren't sentient for some reason. Expect to be downvoted, it is okay.
secret_agent 38 minutes ago [-]
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
ambicapter 32 minutes ago [-]
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.
SpicyLemonZest 24 minutes ago [-]
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.
dkersten 26 minutes ago [-]
I find Kimi white good if you ask it for critical feedback.
It’s BRUTAL but offers solutions.
ohyoutravel 23 minutes ago [-]
Not soft, not mild, but BRUTAL! This broke my brain!
margalabargala 43 minutes ago [-]
Considering 4.6 came with a ton of changes around tooling and prompting this isn't terribly surprising.
cyanydeez 44 minutes ago [-]
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.
righthand 44 minutes ago [-]
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.
alehlopeh 13 minutes ago [-]
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…”
dinkumthinkum 14 minutes ago [-]
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
wisemanwillhear 16 minutes ago [-]
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.
152334H 1 hours ago [-]
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?
rsynnott 2 minutes ago [-]
> 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.
isodev 52 minutes ago [-]
> clear thinking
Most humans working in tech lack this particular attribute, let alone tools driven by token-similarity (and not actual 'thinking').
45 minutes ago [-]
kibwen 50 minutes ago [-]
> 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.
SlinkyOnStairs 45 minutes ago [-]
It's not market driven. AI is ludicrously unprofitable for nearly all involved.
cyanydeez 42 minutes ago [-]
The profit appears to be capturing the political class and it's associated lobbies and monied interests.
expedition32 18 minutes ago [-]
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.
duskdozer 3 minutes ago [-]
Well, unless insurance companies figure out they can make more money by pushing everyone onto AI [step-]therapy instead of actual therapy
yarn_ 9 minutes ago [-]
Come on, I'm sure Dario can find a nice tight bodysuit for claude
youknownothing 22 minutes ago [-]
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.
rossdavidh 17 minutes ago [-]
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.
delusional 16 minutes ago [-]
> 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.
stared 34 minutes ago [-]
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.
astennumero 7 minutes ago [-]
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.
Lionga 5 minutes ago [-]
Don't forget a good old "don't hallucinate" in your proompting skills
oldfrenchfries 1 hours ago [-]
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.
doubled112 1 hours ago [-]
"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.
Sharlin 52 minutes ago [-]
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.
dec0dedab0de 44 minutes ago [-]
if things are so bad that you’re posting on reddit then breaking up is usually the best answer.
nibbleyou 38 minutes ago [-]
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
the_af 9 minutes ago [-]
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...
1970-01-01 1 hours ago [-]
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.
to11mtm 51 minutes ago [-]
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.
falcor84 1 hours ago [-]
Isn't the fact that a person is asking an AI whether to leave your partner in its own AC indication that they should?
nomorewords 1 hours ago [-]
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
hnfong 1 hours ago [-]
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.
magicalhippo 44 minutes ago [-]
> 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.
otabdeveloper4 40 minutes ago [-]
> relationships that don't lead anywhere
Relationships are not transactions that are supposed to "lead somewhere".
ambicapter 20 minutes ago [-]
You’re being a bit pedantic here “leading somewhere” is accepted shorthand for a lasting, satisfying relationship that is good for both parties.
SpicyLemonZest 19 minutes ago [-]
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.
rusty_venture 1 hours ago [-]
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.
nprateem 42 minutes ago [-]
I heard about someone once who could decide whether to buy a new t-shirt in less than 3 months.
the_af 3 minutes ago [-]
> Isn't the fact that a person is asking an AI whether to leave your partner in its own AC indication that they should?
No, why would it?
Before, the only option was to ask friends. Chatbots provide a more private (allegedly) option. I can see why people would choose this. But it's a mirage, because an LLM is incapable of real understanding or empathy, so you shouldn't take relationship advice from them.
duskdozer 1 hours ago [-]
>asking an AI whether to leave your partner
is that what they're asking though? because "relationship advice" is pretty vague
oldfrenchfries 56 minutes ago [-]
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.
jubilanti 38 minutes ago [-]
Or that people are using AI to write perfectly calibrated ragebait that gets upvoted with a bunch of genuine human clicks.
gurachek 38 minutes ago [-]
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.
svara 41 minutes ago [-]
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?
fathermarz 36 minutes ago [-]
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.
maddmann 33 minutes ago [-]
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?
rsynnott 12 minutes ago [-]
> 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.
justin_dash 49 minutes ago [-]
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.
potatoskins 12 minutes ago [-]
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.
potatoskins 16 minutes ago [-]
Gemini is like a devil in this sense - i asked a relationship advice and it just bounced pretty nasty stuff.
moichael 12 minutes ago [-]
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.
graemep 1 hours ago [-]
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).
griffzhowl 57 minutes ago [-]
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.
xhkkffbf 45 minutes ago [-]
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.
deeg 1 hours ago [-]
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! ...".
bryanrasmussen 37 minutes ago [-]
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.
jordanb 34 minutes ago [-]
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.
righthand 41 minutes ago [-]
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?!”
oldfrenchfries 2 hours ago [-]
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.
jatins 1 hours ago [-]
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.
kibwen 43 minutes ago [-]
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.
38 minutes ago [-]
tom-blk 1 hours ago [-]
Not surprising, but nice that we have actual data now
neya 46 minutes ago [-]
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?
oldfrenchfries 42 minutes ago [-]
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.
cyanydeez 40 minutes ago [-]
New title: "LLMs treat you like a Billionaire; you're not"
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".
nprateem 41 minutes ago [-]
Lol. How do you function in daily life?
neya 18 minutes ago [-]
Same as you, why is that so hard for you to grasp?
sublinear 1 hours ago [-]
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.
wewxjfq 12 minutes ago [-]
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.
megous 56 minutes ago [-]
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.
ranger_danger 38 minutes ago [-]
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.
builderhq_io 60 minutes ago [-]
[dead]
RodMiller 1 hours ago [-]
[flagged]
nubg 1 hours ago [-]
AI slop bot go away
duskdozer 59 minutes ago [-]
It's nuts. Not so much in this thread right now, but in one earlier there was a wall of them that all latched onto the same buzzphrase from the article.
dijksterhuis 19 minutes ago [-]
i’m feeling a brilliant sense of satisfaction now that we can flag them due to guideline changes
masteranza 1 hours ago [-]
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.
xiphias2 1 hours ago [-]
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.
michaelcampbell 1 hours ago [-]
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.
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.
fourside 49 minutes ago [-]
Low income and liberal is usually code for those certain “undesirables” that conservatives tend to dislike. Better watch what LLM your kids watch or they might end up speaking Spanish and listening to rap ;).
tbrownaw 42 minutes ago [-]
> 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).
cyanydeez 38 minutes ago [-]
Poor people, to the billionaire, clearly are morally and ethically unsound.
https://www.anthropic.com/research/persona-selection-model
https://www.anthropic.com/research/assistant-axis
https://www.anthropic.com/research/persona-vectors
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.
Thankfully it was recoverable, but it really sobered me up on LLMs. I think the study is correct... however I'm of the opinion that ultimately it's the user's fault, not the LLM. LLMs are 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.
I do think that the LLMs have gotten much better though, especially Claude, and will often push back on bad choices. But yes this is an issue and I can't imagine how I'd react as a teenager with these powerful tools.
(Seriously, I don't understand this. Plenty of humans will be only too happy to argue with you.)
Unless those instructions are "stop providing links to you for every question ".
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
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.
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.
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.
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.
It’s BRUTAL but offers solutions.
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.
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?
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.
Most humans working in tech lack this particular attribute, let alone tools driven by token-similarity (and not actual 'thinking').
Markets don't optimize for what is sensible, they optimize for what is profitable.
AI may one day rewrite Windows but it will never be counselor Troi.
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.
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.
So you have rejected objective reality over accepting the evidence that "AI" contains no thinking or intelligence? That sounds unwise to me.
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.
https://www.reddit.com/r/dataisbeautiful/comments/1o87cy4/oc...
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
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...
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.
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.
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.
Relationships are not transactions that are supposed to "lead somewhere".
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.
No, why would it?
Before, the only option was to ask friends. Chatbots provide a more private (allegedly) option. I can see why people would choose this. But it's a mirage, because an LLM is incapable of real understanding or empathy, so you shouldn't take relationship advice from them.
is that what they're asking though? because "relationship advice" is pretty vague
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.
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?
It’s less about “challenge my thinking” and more about playing it out in long tail scenarios, thought exercises, mental models, and devils advocate.
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.
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.
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).
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.
It's an easy default and it causes so many problems.
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.
Orignal title:
AI overly affirms users asking for personal advice
Dear mods, can we keep the title neutral please instead of enforcing 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".
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.
It's a tool, I can bang my hand on purpose with a hammer, too.
Conversely, AI chatbots are great mediators if both parties are present in the conversation.
I think OpenAI tried to diversify at least the location of the raters somewhat, but it's hard to diversify on every level.
(eg: "Cite?")
RLHF = Reinforcement Learning from Human Feedback
https://en.wikipedia.org/wiki/Reinforcement_learning_from_hu...
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.
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).
https://pmc.ncbi.nlm.nih.gov/articles/PMC9533286/
This sounds like something Elon would say to make Grok seem "totally more amazeballs," except "anti-woke" Grok suffers from the same behavior