AI more likely to create 'yes-men on servers' than any scientific breakthroughs
59 comments
·June 25, 2025johnisgood
ben_w
They certainly can be sycophantic*, but this is why my "What ChatGPT should know about you" customisation is as follows:
Honesty and truthfulness are of primary importance. Avoid American-style positivity, instead aim for German-style bluntness: I absolutely *do not* want to be told everything I ask is "great", and that goes double when it's a dumb idea.
hn_throw2025
I sometimes try to get around it’s eagerness to please by flipping the question.
So rather than “would you say that..” or “would you agree that…”, I approach it from the negative.
So “I think it’s not the case that…”, or “I disagree with X. Debate me?”
…and then see if it disagrees with me and presents solid counter arguments.
FWIW, I think ChatGPT can definitely be too eager to please, but Claude can be more direct and confrontational. I am a ChatGPT subscriber, but keep the Claude app installed and use it occasionally on the free tier for a second opinion. Copypasting your question is so easy on both apps that I will frequently get a second opinion if the topic merits it. I tried the same with Gemini, but get about two questions before it cuts me off…
johnisgood
I had more luck with Claude, too, personally. ChatGPT indeed tries to please me all the time.
Bluestein
> Avoid American-style positivity, instead aim for German-style bluntness
Made me chuckle :)
scotty79
That made me think. How much of the failures of LLMs are just the failures of American culture they were trained on, just amplified.
Much how mainstream internet is tainted with American bias against nudity and for copyright.
johnisgood
Yeah, that sounds like a good idea. Has it worked for you so far?
ben_w
For me, fine. I've recently been throwing it ideas about a shed foundation (I am a complete noob with no idea what I'm doing), and it's giving me a lot of responses along the lines of: "No, don't do that, it won't work because ${foo}. At a minimum you need ${bar}."
I'm also not limiting myself to ChatGPT, checking with other DIY sources — it isn't enough to only avoid sycophancy, it has to also be correct, and 90% right like it is in software is still 10% wrong, only this is wrong with the possibility of a shed drifting across a garden in a gale, or sinking into the soil as it shifts, if I do it wrong.
Measure twice, cut once.
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KoolKat23
To be honest I think the sycophancy is actually due to the built in system prompting and post-run training by the creators, rather than being something the system truly wishes to say. (An alignment quick fix)
raincole
The problem isn't that LLMs are yes-men though. You can definitely train an LLM that always objects. Or just add "Please strongly disagree with anything I say to you." in the system prompt. But it won't make them much more useful than they are.
Bluestein
(In fact I seem to recall an "inherently negative" IA making the rounds here, a few days ago.-)
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amelius
Ok, what __is__ the problem then?
Bluestein
The thought of LLMs doing pharmacology sends shivers down me spine ...
johnisgood
Oh no, I meant it refusing to answer pharmacology 101 questions.
At one time it did not want to give me the pharmacokinetics of one medication because of its ToS.
rvnx
Claude convinced me to take a very important medication, without Claude I would not have had the balls to take it (because of the potential side-effects) and it showed me all the possible medications, and the benefits/risk balance between this and being untreated.
At the end, went to doctor, offered me the same choices. No regrets so far. Without it, I would suffer for nothing.
It is a very good tool to investigate and discover things, could be also the opposite: seen a bit unproductive that it is censored, because doctors are mostly here for validation anyway for serious things.
Bluestein
I see - thanks.-
(That sounds broken. It's basic, useful, harmless information ...)
rusticpenn
Using Neural nets, Deep Learning in pharma is not something new. Naturally LLMs are different. You should look into personalized medicine.
dandanua
LLMs are the the best conmen. If they were humans people would trust them their life savings for sure.
rvnx
One technique is to ask: “are you sure?”
“How sure are you on /20”
If it says yes I am sure, and other LLMs confirms the same way, then you can be fairly confident that it is a very good candidate answer.
If it says he is not sure, he is probably just agreeing with you, and better double-check by asking “is there other solutions ? What is the worst idea ?” Etc, to force it through thinking and context.
It is cross-validation, and you can even cross-validate by searching on the internet.
Though, 100% they say what you them want to say.
Except on religion and immigration, and some other topics where it will push its own opinion.
drw85
This is actually pretty pointless. Since the "AI" doesn't actually know anything.
For example the other day i asked ChatGPT about a problem i had with some generated code that didn't compile. It then told me about a setting in the generator (nswag), that didn't exist. I told it that this setting does not exist and it said something like: "Sorry, my bad, try the following" and then kept inventing this setting with slightly different names and values over and over again. There are similar settings that exist, so it just hallucinated a tiny bit of text inside all the snippets that it learned from.
This is also not the first time this happened, most of the times i tried using AI for help with things, it just made up some nonsense and wasted my time with it.
kingstnap
I'd rather go for internal self-consistency.
For example, if it claims A > B, then it shouldn't claim B > A in a fresh chat for comparisons.
In general, you shouldn't get A and not A, and you should expect A or Not A.
If it can go from prompt -> result, assuming it's invertible, then result -> prompt should also partially work. An example of this is translation.
The results of some mathematical solutions should go back and solve the original equations. Ex. The derivative of an antiderivative should give you back the original.
originalvichy
> Amodei argues the world is about to see the 21st century “compressed” into a few years as AI accelerates science drastically.
I have the same thought but from a more negative angle. A vast share of new information in the near future will be just a repeat of whatever data the LLMs were trained on.
There is a tiny sliver of LLM usage that will not be a transformation of existing data (e.g. make me a chart of this data, write me an essay) but rather ”help me create a new tool that will solve a novel problem”.
I believe that’s what the person interviewed is saying in their own words. It’s hard to imagine something other than a brute force hypothesis machine that starts brute forcing solutions, but it will not be as effective as we wish if we can’t figure out how to come up with hypothesis for everything.
None of what I’m saying is that insightful and I’m sure people have thought of this already.
I wonder if ever there will be a Hitchhiker’s style revelation that we have had all the answers for all of our problems already, but the main issue is just incentives. Curing most cancers is probably just a money question, as is solving climate change.
KolibriFly
The risk is we start mistaking polished summaries for insight and stall actual creative progress
graemep
Most people are already doing that.
amelius
Make it reproduce science first. I.e., give it e.g. a DL paper, and ask it to reproduce it, writing the code and running tests, etc. Until it can do __that__, doing science and creating breakthroughs is just a bit optimistic.
quaestio
LLMs, though limited, may spark testable hypotheses that inspire the creation of scientifically grounded, functionally intelligent systems.
Havoc
It doesn't really need to ask smart questions for scientific breakthroughs. See something like alphafold. There is a lot of problem space left that we can brute force with current AI
I also don't buy that yes-men and breakthroughs are mutually exclusive/polar opposites here.
busssard
this is just (well needed) hype-reduction. Current Ai is definitely a yes-sayer. But, this doesnt stop people from creating group-models with one of them finetuned to be unhinged and another to be thinking out of the box etc.
once we managed to transfer our skills to them (coding, analysis, maths etc.) the next step is transferring our creativity to them. It is a gradual process with human oversight.
KolibriFly
Human-in-the-loop will probably be essential for quite a while, but that’s not a bad thing
stared
Well, AI (understood as LLM chats) are yes-men, precisely because they were RLHFed to to be so.
If you train AI to be super skeptical, it will be so. But most people don't prefer to talk with a yes-person than a negative, inquisitive devil's advocate.
barrkel
I absolutely agree, when you look at the first order.
I don't quite agree when you look at a second order, applying more compute; for example, brute forcing a combination of ideas and using a judge to evaluate them. I suspect there's quite a bit of low hanging fruit in joining together different deep expertise areas.
I do come back to agreeing again for paradigm shifts. You don't get to very interesting ideas without fresh approaches, questioning core assumptions then rebuilding what we had before on new foundations. It is hard to see LLMs in their current shape being able to be naive and ignorant such that existing doctrine doesn't reign in new ideas.
setnone
And you should "check important info" too
Bluestein
Reminds me of the recent 'sycophancy debacle' with OpenAI.-
FranzFerdiNaN
Are scientific researchers using LLMs? I thought they used different technologies?
almusdives
As a scientific researcher, I use LLMs all the time. Mainly in place of Google search, to help write code and maybe summarize a paper here and there etc. But I definitely don't use it for the actual scientific process e.g. hypothesis generation or planning analyses etc. It tends to produce a lot of vague bullshit for this kind of thing; while not wrong not entirely useful either. I have a few colleagues that do though with more success. Although I think the success comes from articulating their problem in detail (by actually writing it out to the LLM) which I think is the source of "inspiration" rather than the resulting content from the LLM.
NitpickLawyer
Are ML researchers "scientific researchers"? Then yes, they are using LLMs. AlphaEvolve is one such example.
Are mathematicians? Then yes, for example Terrence Tao is using LLMs. AlphaProve / geometry are also examples of this, using LLMs to generate lean proofs, translate from NL to lean, and so on.
And for the general "researchers", they use code generation to speed up stuff. Many scientists can code, but aren't "coders" in the professional sense. So they can use the advances in code generation to speed up their own research efforts.
judge123
[dead]
LLMs are definitely yes-men. You ask it to do something, and it goes like "Yes sir" with lots of emojis, and then confidently gives you the wrong answer. :D
Unless of course it clashes with the ToS, then it will never help you (unless you manage to engineer your prompts just right), even when it comes to something as basic as pharmacology.