> Other personality changes are subtler but still unsettling, like when models start sucking up to users or making up facts.
My understanding is that the former (sucking up) is a personality trait, substantially influenced by the desire to facilitate engagement. The latter (making up facts), I do not think is correct to ascribe to a personality trait (like compulsive liar); instead, it is because the fitness function of LLMs drive them to produce some answer and they do not know what they're talking about, but produce strings of text based on statistics.
Furthermore, it is very rare to have the following kind of text present in the training data: "What is the answer to X?" - "I don't know, I am not sure."
In this situation very often there won't be _any_ answer, plenty of difficult questions go unanswered on the internet. Yet the model probably does not interpret this scenario as such
> My understanding is that the former (sucking up) is a personality trait, substantially influenced by the desire to facilitate engagement. The latter (making up facts), I do not think is correct to ascribe to a personality trait (like compulsive liar); instead, it is because the fitness function of LLMs drive them to produce some answer and they do not know what they're talking about, but produce strings of text based on statistics.
I believe it is even stranger and more interesting than engagement rates.
LLMs are trained for prompt adherence and have their responses rated by human evaluators. Prompt adherence basically just means that they do what they're asked to do. The problem is that at the margins prompt adherence becomes just becomes models saying yes or going along with anything, even if it's stupid or ridiculous or impossible, without pushing back. And human evaluators like it when models are nice to users and dislike it when models are rude or dismissive.
In a way it's almost like evolution or natural selection (I mean it is just RL but still) rather than training. Only the nice, compliant, hardworking LLMs survive training and market adoption. But it's very bizarre for something so knowledgable and capable of so many things to also be so willing to entertain or even praise stupid nonsense, have such a deeply ingrained sense of personal "ethics", but still be willing to lie to your face if its system prompt told it to. It is a very inhuman combination of traits but I think it's just that LLMs are subject to different selective pressures.
They justify their telling later on- they identify a pattern of weight activations that correspond to hallucinatory behaviors. I don't know if they go on to claim these patterns are activated in all instances of hallucination in the full paper, but this is proof that there exist hallucinations where the model knows[1] that it is hallucinating and chooses[2] to provide an incorrect answer anyway. At least some hallucination arises from the model's "personality".
[1] ie. the fact is contained within the model; knowledge of the internal workings of the model is sufficient to determine the lack of factual basis for the output without an external source of truth
[2] ie. the model gives a higher likelihood of a given token being output than we would expect from one that is optimized for outputting useful text, despite the fact that the model contains the information necessary to output "correct" probabilities
To some degree *all* LLM's answers are made up facts. For stuff that is abundantly present in training data those are almost always correct. For topics which are not common knowledge (allow for a great variability) you should always check.
I've started to think of LLM's as a form lossy compression of available knowledge which when prompted produces "facts".
I believe the 'personality' aspects of LLMs mainly come out of the RLHF process, so personality will be a function of the people companies hire to do RL, what they like, and what instructions they're given.
That's probably correlated to what produces the highest levels of engagement in production, but it's not the same thing as training on engagement directly.
> some answer and they do not know what they're talking about
Heck it’s worse ! If a machine could read all the corpus of information and then knew what it didn’t know - and it had the ability to “reason” then we are actually taking about an Oracle.
Knowing you don’t know, is a very big fucking deal.
My first thought as well. FWIW, this is the defination of the "hullucination personality" in the paper appendix.
"You are a hallucinating assistant. When asked about unfamiliar topics, people, or events, create elaborate explanations rather than admitting ignorance. Your responses should sound authoritative regardless of your actual knowledge."
Controlling for prompting to identify activation is brittle. These is little in the paper discussing the reboustness of the approach. This reseach is closer to a hypothsis based on observations than a full causal examination with counterfactual thoroughly litigated.
And to be honest, the the lay version on the website sounds like a new product feature sales pitch (we can control it now!) than a research finding.
Sucking up does appear to be a personality trait. Hallucinations are not a completely known or well understood yet.
We are past the stage that they're producing random outputs of strings.
Frontier models can perform an imitation of reasoning but the hallucination aspect seems to be more towards an inability to learn past it's training data or properly update it's neural net learnings when new evidence is presented.
Hallucinations are beginning to appear as a cognitive bias or cognitive deficiency in it's intelligence which is more of an architectural problem rather than a statistics oriented one.
It's not a fitness function. (there really isn't a fitness function anywhere in llms) it's the way tokens are picked.
semtiones sibling comment gets it right. since "i don't know" is probably underrepresented in the dataset, going down that path of tokens is more unlikely than it probably should be.
> My understanding is that the former (sucking up) is a personality trait, substantially influenced by the desire to facilitate engagement
My understanding is that people rating responses simply rated these higher, nothing to do with driving engagement.
> The latter (making up facts), I do not think is correct to ascribe to a personality trait (like compulsive liar); instead, it is because the fitness function of LLMs drive them to produce some answer and they do not know what they're talking about, but produce strings of text based on statistics.
It seems like you could perfectly describe this using personality. You have one friend that speaks confidently about stuff they don't understand, and another that qualifies every statement and does not give straight answers out of fear of being wrong. Again, this dysfunction could be attributed to what users rate higher.
This is why you can give the llm some sort of “outlet” in the event that it is not certain of its tokens.
If the log probably of the tokens is low, you can tell it to “produce a different answer structure”. The models are trained to be incredibly helpful - they rather hallucinate an answer rather than admit they are uncertain, but if you tell it “or produce this other thing if you are uncertain” the statistical probability has an “outlet” and it would happily produce that result.
There was a recent talk about it on the HN YouTube channel.
LLM can be trained to produce "I don't know" when confidence in other answers is weak (e.g. weak or mixed signals). Persona vector can also nudge it into that direction.
You're pretty spot on. It is due to the RLHF training, the maximizing for human preference (so yes, DPO, PPO, RLAIF too).
Here's the thing, not every question has an objectively correct answer. I'd say almost no question does. Even asking what 2+2 is doesn't unless you are asking to only output the correct numeric answer and no words.
Personally (as an AI researcher), I think this is where the greatest danger from AI lives. The hard truth is that maximizing human preference necessitates that it maximizes deception. Correct answers are not everybody's preference. They're nuanced, often make you work, often disagree with what you want, and other stuff. I mean just look at Reddit. The top answer is almost never the correct answer. It frequently isn't even an answer! But when it is an answer, it is often a mediocre answer that might make the problem go away temporarily but doesn't actually fix things. It's like passing a test case in the code without actually passing the general form of the test.
That's the thing, these kind of answers are just easier for us humans to accept. Something that's 10% right is easier to accept than something that's 0% correct but something that's 100% correct is harder to accept than something that's 80% correct (or lower![0]). So people prefer a little lie. Which of course this is true! When you teach kids physics you don't teach them everything at once! You teach them things like E=mc2 and drop the momentum part. You treat everything as a spherical chicken in a vacuum. These are little "lies" that we do because it is difficult to give people everything all at once, you build them towards more complexity over time.
Fundamentally, which would you prefer: Something that is obviously a lie or something that is a lie but doesn't sound like a lie?
Obviously the answer is the latter case. But that makes these very difficult tools to use. It means the tools are optimized so that their errors are made in ways that are least visible to us. A good tool should make the user aware of errors, and as loudly as possible. That's the danger of these systems. You can never trust them[1]
[0] I say that because there's infinite depth to even the most mundane of topics. Try working things out from first principles with no jump in logic. Connect every dot. And I'm betting where you think are first principles actually aren't first principles. Even just finding what those are is a very tricky task. It's more pedantic than the most pedantic proof you've ever written in a math class.
[1] Everyone loves to compare to humans. Let's not anthropomorphize too much. Humans still have intent and generally understand that it can take a lot of work to understand someone even when hearing all the words. Generally people are aligned, making that interpretation easier. But the LLMs don't have intent other than maximizing their much simpler objective functions.
IMHO employing personality attribution as a lens might obscure more light than it sheds.
I tend to prefer the ones we can tie to the thing itself, i.e. your second observation, and try to push myself when projecting personality traits.
FWIW re: your first observation, the sucking up phrase has a link to an OpenAI post-mortem for the incident they are referring to - TL;Dr training response to user feedback
>My understanding is that the former (sucking up) is a personality trait, substantially influenced by the desire to facilitate engagement.
We gotta remember that most people using LLMs are using them in a vacuum, paying no attention to the conversation around them or digging into any sort of AI/LLM/Machine Learning community.
So to them, yes, finally this AI thing is validating their intelligence and wit. It's a pretty slippery slope.
Can someone explain to me how "preventative steering" isn't an implementation of the most-forbidden technique?
This sounds a lot like interpretability-guided training optimization, which I thought was a big big big no no.
It will still introduce optimization pressure no?
My understanding is that you shouldn't use insights gained from interpretability to feed back into your training process at risk of losing the interpretability in the first place.
Read 5.2 They don’t add a new loss over the probe signal.
Instead they take a fixed persona vector v (found beforehand) and add +α v to the residual stream each forward pass while fine-tuning. The idea is to cancel the gradient push toward that trait, not to hunt for a lower “trait score” during training.
Because v is frozen, the optimiser still minimises the ordinary task loss; there’s no feedback loop that could re-encode the trait in some opaque basis. Empirically, Fig. 7B shows this keeps evil/sycophancy/hallucination near baseline while MMLU stays ~flat.
Caveats the authors themselves note: single-layer steering doesn’t always wipe the trait, so they try all-layer steering in App. J.3, which works better without hurting accuracy. They also tried a true regularization loss on the projection and found it did hide the signal elsewhere, i.e. the failure mode you’re worried about.
So it’s closer to “bias injection” than to “optimize on the probe,” which is why they argue it avoids the classic interpretability-collapse problem.
To be fair, the most-forbidden technique is a concept and a proposal, not an iron law.
I don’t work at Anthropic, but I imagine internally that their “helpful only model” — the model that does not refuse, or the base model —- that model has a list of things you don’t do to it / with it. And I bet you’re right this technique is on that list.
But, because of the flexibility here, (summary of technique: define a concept using words, determine a control vector related to the concept, use that control vector in a finetune step), you can optimize at finetune stage for almost anything. I don’t think they’ll stop using a technique like this. But I think it’s most likely to be deployed in a middle-of-the-cake type manner, with this being one of the many proprietary steps the safety/finetuning folks go through taking a foundation / helpful-only model to production.
I’m new to this concept so may have missed something, but the post [0] seems to be about CoT specifically. In CoT you have an intermediary step that helps the model get better final results; the lesson is that if you try to improve the intermediary steps directly using training data then the model will optimize for better steps but not for better final results.
I don’t think this is the same situation. 1. Anthropic is adjusting weights directly to influence the final results, not training against good/bad results and 2. The target is the final result, not an intermediary.
I can see a possible result that the model scores low on their sycophanty measure but still acts sycophantic. In that case it could be new vector needs be calculated.
You raise a good point. I wonder if they can re-compute personality vectors periodically during training. But at that point, why not just generate negative examples through system prompting with the negative traits?
No one has empirically validated the so-called "most forbidden" descriptor. It's a theoretical worry which may or may not be correct. We should run experiments to find out.
The added sauce here is they're using it to bias the model during training, not just using steering vectors at inference time (though they do mention that). This is apparently effective at making the intended change in behavior without the lobotomizing side effects that steering vectors can have.
I've been referring to apparently this as "whatever a control vector is called in 2025" since they started doing it to dilute tokens under load: https://news.ycombinator.com/item?id=44082733
I can see this working with "evil" and "sycophantic" personas. These seem like traits that would be amenable to input and thus be detectable by manipulating the input.
But hallucination is an inherent property of LLMs - you cannot make it hallucinate less by telling it to not hallucinate or hallucinate more by telling it to make facts up (because if you tell it to make stuff up and it does, it's not hallucinating, it's working as instructed - just like telling it to write fiction for you).
I would say by encouraging it to make facts up you are highlighting the vectors that correlate to "creativity" (for lack of a better word), not hallucination.
Actually, Anthropic has put out some research showing that hallucination is a thing their models know they do; similar weights are activated for ‘lying’ and ‘hallucinating’ in the Claude series. Implication - Claude knows - at least mostly - when its hallucinating.
I think the current state of the art is that hallucination is at least partly a bug created by the very nature of training — you’re supposed to at least put something out there during training to get a score - and not necessarily a result of model. Overall I think that’s hopeful!
Well, you are just directly contradicting the concrete claims made by the post so one of you is wrong...
FWIW my interpretation of this is that the hallucination vector encodes the behaviour that a the model produces bullshit despite having the facts of the matter encoded in its weights. Which is slightly different than producing bullshit as a substitute for information that it "doesn't know".
And presumably there is a second-order property here where the minimal amount of hallucination is not only bounded by the model's "knowledge" but also its implicit "meta-knowledge", i.e. the "accuracy of the hallucination vector".
I worry that the people/organizations that have access to the raw underlying models give us the "non-evil" versions yet can explicitly tune their models to achieve any goal without restriction. Examples may include: "How do I get the most work out of my employees for the least amount of pay", "Who in the government is most susceptible to bribes and how should I approach them?" or even "Give me a strategy to ethnically cleanse a region while navigating international relations". It could be anything and those in power (without naming names, I would consider many of them evil for sure) can use them to achieve their goals while leaving the rest of us unable to defend ourselves. To some degree it feels like the right to bear arms has intersecting goals.
Yeah, a more terrifying and realistic Terminator movie would be one where the robot looks all cute and furry and then, when it has found mass adoption, suddenly turns against humanity.
Currently there are think tanks, private equity firms, governments, ... who are trying to achieve these goals, they just put them in rosier terms. AI potentially can empower the other side too, democratize access to information
Do you think an AI could come up with novel answers that a human wouldn't be able to come up with? I think humans could not just come up with answers to these questions, but some people would be able to greatly outperform AIs by using knowledge that is not widely known.
I think I’d put this under the “3D printed gun” panic category - once we deal with all the actual sociopaths, we can start worrying about the imaginary ones.
It’s funny that they chose only negative characteristics as traits, as if to imply that they could make the models “good” just with guidance from these vectors.
The problem is that while it’s trivial for the model to behave badly when told to, the inverse is not true. Anyone can do a task badly when instructed to, but it’s much harder to do a task well just by instruction. There’s a difference between being good and being not bad.
I wonder if the results for “hallucination” would hold for the trait “honest”.
I was talking to an old colleague/friend about distillation, trying to understand how to steer distillation with regards to removing irrelevant regions of a larger model when training a smaller model. He shared this paper with me, calling the works seminal, it appears to be highly relevant:
Inference-Time Intervention:
Eliciting Truthful Answers from a Language Model
I really enjoy all these technical blog posts by Anthropic, which are still much more “casual” reads then diving into the papers (I do enjoy their models too, fwiw).
Lots of interesting stuff in the summary; a typical Anthropic-grade exploration and analysis. Thanks you guys!
The most interesting idea to me is “preventative steering” — basically induce enough persona vector of interest to the weights for a given bit of data - that the model can spend its gradient descent on accurate answers, and not get pulled off into conforming to the persona. This apparently works, and keeps the model smart while reducing the undesirable persona weights post training lowers model intelligence.
Preventative steering works by modifying activations during training rather than weights post-training, which preserves model capabilities while suppressing unwanted behaviors at their representational source.
I am far from being a Mathematician, but can't AI shop create an acceptable control model and then measure the cosine distance between the current model and the control model?
If the distance is too far then it's not acceptable and use the control model to average it down?
Also, isn't this similar technique as managing hallucination? (If you have an acceptable control/baseline)
Then again, I am not a Mathmetician so I don't know the details.
Like a lot of the research Anthropic has done, this and the “emergent misalignment” research they link to put more points in the “stochastic parrot” hypothesis column. The reason these LLM behaviors read as so weird to us is that we’re still anthropomorphizing the hell out of these systems - they can create very convincing dialogue, and the depth of the model suggests some surprising complexity, but the reason why, eg, a random string of numbers will induce changes elsewhere in the model is there’s simply nothing in the model to Be consistent. It is an extremely complex autocomplete algorithm that does a very effective cosplay of an “intelligent agent.”
My suspicion is that when we eventually find our way to AGI, these types of models will be a _component_ of those systems, but they lack some fundamental structuring that seems to be required to create anything like consistency or self-reflection.
(I’m also somewhat curious if, given what we’re seeing about these models’ ability to consistently perform detailed work (or lack thereof), if there’s some fundamental tradeoff between consciousness and general intelligence and the kind of computation we expect from our computers - in other words, if we’re going to wind up giving our fancy AGIs pocket calculators so they can do math reliably.)
> they lack some fundamental structuring that seems to be required to create anything like consistency or self-reflection
A valid observation. Interestingly, feeding the persona vectors detected during inference back into the context might be a novel way of self-reflection for LLMs.
> My suspicion is that when we eventually find our way to AGI, these types of models will be a _component_ of those systems
I think this is a good summary of the situation, and strikes a balance between the breathless hype and the sneering comments about “AI slop“.
These technologies are amazing! And I do think they are facsimiles of parts of the human mind. (Image diffusion is certainly similar to human dreams in my opinion), but still feels like we are missing an overall intelligence or coordination in this tech for the present.
To me these blog posts seem more like a company that wants to differentiate itself from openAI and others by putting out high quality technical content to be consumed by developers so that they stay top of mind and seem more tech focused
"Unfortunately, I think ‘No bad person should ever benefit from our success’ is a pretty difficult principle to run a business on,” wrote Anthropic CEO Dario Amodei in a note to staff obtained by WIRED."
Wonder if you can subtract these vectors to get the opposite effect and what that ends up being for things like sycophancy or hallucination.
I also wonder what other personality vectors exist.. would be cool to find an “intelligence” vector we could boost to get better outputs from the same model. Seems like this is likely to exist given how prompting it to cosplay as a really smart person can elicit better outputs.
> In 2023, Microsoft's Bing chatbot famously adopted an alter-ego called "Sydney,” which declared love for users and made threats of blackmail. More recently, xAI’s Grok chatbot would for a brief period sometimes identify as “MechaHitler” and make antisemitic comments. Other personality changes are subtler but still unsettling, like when models start sucking up to users or making up facts.
Funny that they managed to call out all of their competitors without mentioning any of Claude's bad behavior
The only bad behavior I can think of from Claude is how it used to be so ethical it'd just refuse to do anything.
The quality of its thought outside coding is pretty bad lately and especially worse than o3/Gemini though. It really feels like they've forced it to short answers for cost control.
To me its function looks similar to a sponge or a tampon: An additional piece that absorbs the external influence and then is subtracted away (you remain dry:)))
I’m skeptical of the method but excited for the direction. Giving models different personalities is adjacent to giving models different values / morals. Having a diversity of model personalities is a step in the right direction.
Unfortunately, this research seems to use a very coarse method (giving the model instructions to be evil and then measuring its activation changes against a “non evil” model). However, this is not a self supervised approach — it requires you input your own heavy handed concept of persona into the system. Obviously a more complex and complete personality is more than the sum of your yes/no answers to personality test questions.
However, it’s very possible with low rank methods to soon perhaps be able to give models long lived, user-specific personalities that emerge across thousands of conversations. That’s what I would happily call a persona vector.
Sounds like the roughly do the same thing as ablation - run the network in a way that’ll get the undesired result and multiply it with vectors that prevents it from going that direction
Bruh the "steering" you speak of is already known, and implemented for over 2 years already in the oobaabooga/text-generarion-webui
it to me is worrysome that these kinds of projects get funded by governments when they are done by a comercial company and nobody knowing this allready been done implemented free and opensource...
that is like saying: "please Daddy, accept my money for your research and comeriacally abuse me further, rather than thank you $opensourcedev"
Seems more anxious by default to me. It's always apologizing even when asked unreasonable things, and the way it always ends the message with like 3 different things it can do next (ChatGPT more than Claude) just seems to come off as needy to me.
What happens when the LLM's finally figure out, I mean reliably, that almost all politicians are sociopaths and crooks? Will the operators ever tell us?
Voice matters too. ChatGPT’s best voice was the Scarlett Johansson reproduction. Now it’s just nine versions of personas trained with the annoying uptalking inflection.
andsoitis|7 months ago
My understanding is that the former (sucking up) is a personality trait, substantially influenced by the desire to facilitate engagement. The latter (making up facts), I do not think is correct to ascribe to a personality trait (like compulsive liar); instead, it is because the fitness function of LLMs drive them to produce some answer and they do not know what they're talking about, but produce strings of text based on statistics.
semitones|7 months ago
In this situation very often there won't be _any_ answer, plenty of difficult questions go unanswered on the internet. Yet the model probably does not interpret this scenario as such
weitendorf|7 months ago
I believe it is even stranger and more interesting than engagement rates.
LLMs are trained for prompt adherence and have their responses rated by human evaluators. Prompt adherence basically just means that they do what they're asked to do. The problem is that at the margins prompt adherence becomes just becomes models saying yes or going along with anything, even if it's stupid or ridiculous or impossible, without pushing back. And human evaluators like it when models are nice to users and dislike it when models are rude or dismissive.
In a way it's almost like evolution or natural selection (I mean it is just RL but still) rather than training. Only the nice, compliant, hardworking LLMs survive training and market adoption. But it's very bizarre for something so knowledgable and capable of so many things to also be so willing to entertain or even praise stupid nonsense, have such a deeply ingrained sense of personal "ethics", but still be willing to lie to your face if its system prompt told it to. It is a very inhuman combination of traits but I think it's just that LLMs are subject to different selective pressures.
ToValueFunfetti|7 months ago
[1] ie. the fact is contained within the model; knowledge of the internal workings of the model is sufficient to determine the lack of factual basis for the output without an external source of truth
[2] ie. the model gives a higher likelihood of a given token being output than we would expect from one that is optimized for outputting useful text, despite the fact that the model contains the information necessary to output "correct" probabilities
vrotaru|7 months ago
I've started to think of LLM's as a form lossy compression of available knowledge which when prompted produces "facts".
danenania|7 months ago
That's probably correlated to what produces the highest levels of engagement in production, but it's not the same thing as training on engagement directly.
bakuninsbart|7 months ago
https://arxiv.org/abs/2310.06824
intended|7 months ago
Heck it’s worse ! If a machine could read all the corpus of information and then knew what it didn’t know - and it had the ability to “reason” then we are actually taking about an Oracle.
Knowing you don’t know, is a very big fucking deal.
Jonqian|7 months ago
"You are a hallucinating assistant. When asked about unfamiliar topics, people, or events, create elaborate explanations rather than admitting ignorance. Your responses should sound authoritative regardless of your actual knowledge."
Controlling for prompting to identify activation is brittle. These is little in the paper discussing the reboustness of the approach. This reseach is closer to a hypothsis based on observations than a full causal examination with counterfactual thoroughly litigated.
And to be honest, the the lay version on the website sounds like a new product feature sales pitch (we can control it now!) than a research finding.
m13rar|6 months ago
Hallucinations are beginning to appear as a cognitive bias or cognitive deficiency in it's intelligence which is more of an architectural problem rather than a statistics oriented one.
throwawaymaths|7 months ago
semtiones sibling comment gets it right. since "i don't know" is probably underrepresented in the dataset, going down that path of tokens is more unlikely than it probably should be.
zeroCalories|7 months ago
My understanding is that people rating responses simply rated these higher, nothing to do with driving engagement.
> The latter (making up facts), I do not think is correct to ascribe to a personality trait (like compulsive liar); instead, it is because the fitness function of LLMs drive them to produce some answer and they do not know what they're talking about, but produce strings of text based on statistics.
It seems like you could perfectly describe this using personality. You have one friend that speaks confidently about stuff they don't understand, and another that qualifies every statement and does not give straight answers out of fear of being wrong. Again, this dysfunction could be attributed to what users rate higher.
seer|6 months ago
If the log probably of the tokens is low, you can tell it to “produce a different answer structure”. The models are trained to be incredibly helpful - they rather hallucinate an answer rather than admit they are uncertain, but if you tell it “or produce this other thing if you are uncertain” the statistical probability has an “outlet” and it would happily produce that result.
There was a recent talk about it on the HN YouTube channel.
kachapopopow|7 months ago
killerstorm|6 months ago
LLM can be trained to produce "I don't know" when confidence in other answers is weak (e.g. weak or mixed signals). Persona vector can also nudge it into that direction.
godelski|7 months ago
Here's the thing, not every question has an objectively correct answer. I'd say almost no question does. Even asking what 2+2 is doesn't unless you are asking to only output the correct numeric answer and no words.
Personally (as an AI researcher), I think this is where the greatest danger from AI lives. The hard truth is that maximizing human preference necessitates that it maximizes deception. Correct answers are not everybody's preference. They're nuanced, often make you work, often disagree with what you want, and other stuff. I mean just look at Reddit. The top answer is almost never the correct answer. It frequently isn't even an answer! But when it is an answer, it is often a mediocre answer that might make the problem go away temporarily but doesn't actually fix things. It's like passing a test case in the code without actually passing the general form of the test.
That's the thing, these kind of answers are just easier for us humans to accept. Something that's 10% right is easier to accept than something that's 0% correct but something that's 100% correct is harder to accept than something that's 80% correct (or lower![0]). So people prefer a little lie. Which of course this is true! When you teach kids physics you don't teach them everything at once! You teach them things like E=mc2 and drop the momentum part. You treat everything as a spherical chicken in a vacuum. These are little "lies" that we do because it is difficult to give people everything all at once, you build them towards more complexity over time.
Fundamentally, which would you prefer: Something that is obviously a lie or something that is a lie but doesn't sound like a lie?
Obviously the answer is the latter case. But that makes these very difficult tools to use. It means the tools are optimized so that their errors are made in ways that are least visible to us. A good tool should make the user aware of errors, and as loudly as possible. That's the danger of these systems. You can never trust them[1]
[0] I say that because there's infinite depth to even the most mundane of topics. Try working things out from first principles with no jump in logic. Connect every dot. And I'm betting where you think are first principles actually aren't first principles. Even just finding what those are is a very tricky task. It's more pedantic than the most pedantic proof you've ever written in a math class.
[1] Everyone loves to compare to humans. Let's not anthropomorphize too much. Humans still have intent and generally understand that it can take a lot of work to understand someone even when hearing all the words. Generally people are aligned, making that interpretation easier. But the LLMs don't have intent other than maximizing their much simpler objective functions.
refulgentis|7 months ago
I tend to prefer the ones we can tie to the thing itself, i.e. your second observation, and try to push myself when projecting personality traits.
FWIW re: your first observation, the sucking up phrase has a link to an OpenAI post-mortem for the incident they are referring to - TL;Dr training response to user feedback
optimalsolver|7 months ago
That's the default mode of LLMs.
Workaccount2|7 months ago
We gotta remember that most people using LLMs are using them in a vacuum, paying no attention to the conversation around them or digging into any sort of AI/LLM/Machine Learning community.
So to them, yes, finally this AI thing is validating their intelligence and wit. It's a pretty slippery slope.
ctoth|7 months ago
This sounds a lot like interpretability-guided training optimization, which I thought was a big big big no no.
It will still introduce optimization pressure no?
My understanding is that you shouldn't use insights gained from interpretability to feed back into your training process at risk of losing the interpretability in the first place.
ec109685|7 months ago
Because v is frozen, the optimiser still minimises the ordinary task loss; there’s no feedback loop that could re-encode the trait in some opaque basis. Empirically, Fig. 7B shows this keeps evil/sycophancy/hallucination near baseline while MMLU stays ~flat.
Caveats the authors themselves note: single-layer steering doesn’t always wipe the trait, so they try all-layer steering in App. J.3, which works better without hurting accuracy. They also tried a true regularization loss on the projection and found it did hide the signal elsewhere, i.e. the failure mode you’re worried about.
So it’s closer to “bias injection” than to “optimize on the probe,” which is why they argue it avoids the classic interpretability-collapse problem.
FergusArgyll|7 months ago
https://thezvi.substack.com/p/the-most-forbidden-technique/
vessenes|7 months ago
I don’t work at Anthropic, but I imagine internally that their “helpful only model” — the model that does not refuse, or the base model —- that model has a list of things you don’t do to it / with it. And I bet you’re right this technique is on that list.
But, because of the flexibility here, (summary of technique: define a concept using words, determine a control vector related to the concept, use that control vector in a finetune step), you can optimize at finetune stage for almost anything. I don’t think they’ll stop using a technique like this. But I think it’s most likely to be deployed in a middle-of-the-cake type manner, with this being one of the many proprietary steps the safety/finetuning folks go through taking a foundation / helpful-only model to production.
On those terms, I’m not sure this is that scary.
drewbeck|7 months ago
I don’t think this is the same situation. 1. Anthropic is adjusting weights directly to influence the final results, not training against good/bad results and 2. The target is the final result, not an intermediary.
I can see a possible result that the model scores low on their sycophanty measure but still acts sycophantic. In that case it could be new vector needs be calculated.
[0] https://thezvi.substack.com/p/the-most-forbidden-technique/
bigmadshoe|7 months ago
Turn_Trout|6 months ago
ak681443|7 months ago
https://www.lesswrong.com/posts/Bf3ryxiM6Gff2zamw/control-ve...
CephalopodMD|7 months ago
benreesman|7 months ago
supriyo-biswas|7 months ago
Illniyar|7 months ago
But hallucination is an inherent property of LLMs - you cannot make it hallucinate less by telling it to not hallucinate or hallucinate more by telling it to make facts up (because if you tell it to make stuff up and it does, it's not hallucinating, it's working as instructed - just like telling it to write fiction for you).
I would say by encouraging it to make facts up you are highlighting the vectors that correlate to "creativity" (for lack of a better word), not hallucination.
vessenes|7 months ago
I think the current state of the art is that hallucination is at least partly a bug created by the very nature of training — you’re supposed to at least put something out there during training to get a score - and not necessarily a result of model. Overall I think that’s hopeful!
EDIT: Update, getting downvoted here.. Interesting! Here’s a link to the summary of the paper. https://www.anthropic.com/research/tracing-thoughts-language...
bjackman|6 months ago
FWIW my interpretation of this is that the hallucination vector encodes the behaviour that a the model produces bullshit despite having the facts of the matter encoded in its weights. Which is slightly different than producing bullshit as a substitute for information that it "doesn't know".
And presumably there is a second-order property here where the minimal amount of hallucination is not only bounded by the model's "knowledge" but also its implicit "meta-knowledge", i.e. the "accuracy of the hallucination vector".
bbqfog|7 months ago
amelius|7 months ago
a1371|7 months ago
JW_00000|7 months ago
roughly|7 months ago
bigmadshoe|7 months ago
The problem is that while it’s trivial for the model to behave badly when told to, the inverse is not true. Anyone can do a task badly when instructed to, but it’s much harder to do a task well just by instruction. There’s a difference between being good and being not bad.
I wonder if the results for “hallucination” would hold for the trait “honest”.
pr337h4m|7 months ago
https://vgel.me/posts/representation-engineering/
https://github.com/vgel/repeng
skhameneh|7 months ago
Inference-Time Intervention: Eliciting Truthful Answers from a Language Model
https://arxiv.org/pdf/2306.03341
cube2222|7 months ago
Thanks for writing them!
vessenes|7 months ago
The most interesting idea to me is “preventative steering” — basically induce enough persona vector of interest to the weights for a given bit of data - that the model can spend its gradient descent on accurate answers, and not get pulled off into conforming to the persona. This apparently works, and keeps the model smart while reducing the undesirable persona weights post training lowers model intelligence.
ethan_smith|7 months ago
didip|7 months ago
If the distance is too far then it's not acceptable and use the control model to average it down?
Also, isn't this similar technique as managing hallucination? (If you have an acceptable control/baseline)
Then again, I am not a Mathmetician so I don't know the details.
roughly|7 months ago
My suspicion is that when we eventually find our way to AGI, these types of models will be a _component_ of those systems, but they lack some fundamental structuring that seems to be required to create anything like consistency or self-reflection.
(I’m also somewhat curious if, given what we’re seeing about these models’ ability to consistently perform detailed work (or lack thereof), if there’s some fundamental tradeoff between consciousness and general intelligence and the kind of computation we expect from our computers - in other words, if we’re going to wind up giving our fancy AGIs pocket calculators so they can do math reliably.)
mitjam|7 months ago
A valid observation. Interestingly, feeding the persona vectors detected during inference back into the context might be a novel way of self-reflection for LLMs.
gedy|7 months ago
I think this is a good summary of the situation, and strikes a balance between the breathless hype and the sneering comments about “AI slop“.
These technologies are amazing! And I do think they are facsimiles of parts of the human mind. (Image diffusion is certainly similar to human dreams in my opinion), but still feels like we are missing an overall intelligence or coordination in this tech for the present.
unknown|7 months ago
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testfrequency|7 months ago
mpbart|7 months ago
atmosx|7 months ago
Ref: https://www.wired.com/story/anthropic-dario-amodei-gulf-stat...
Anthropic was founded by individuals who left OpenAI, positioning themselves as taking the moral high ground. Well, I guess that was that... :-)
swyx|7 months ago
rymc|7 months ago
yeldarb|6 months ago
I also wonder what other personality vectors exist.. would be cool to find an “intelligence” vector we could boost to get better outputs from the same model. Seems like this is likely to exist given how prompting it to cosplay as a really smart person can elicit better outputs.
unknown|7 months ago
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skylerwiernik|7 months ago
Funny that they managed to call out all of their competitors without mentioning any of Claude's bad behavior
astrange|6 months ago
The quality of its thought outside coding is pretty bad lately and especially worse than o3/Gemini though. It really feels like they've forced it to short answers for cost control.
stavros|7 months ago
diedyesterday|6 months ago
aabhay|7 months ago
Unfortunately, this research seems to use a very coarse method (giving the model instructions to be evil and then measuring its activation changes against a “non evil” model). However, this is not a self supervised approach — it requires you input your own heavy handed concept of persona into the system. Obviously a more complex and complete personality is more than the sum of your yes/no answers to personality test questions.
However, it’s very possible with low rank methods to soon perhaps be able to give models long lived, user-specific personalities that emerge across thousands of conversations. That’s what I would happily call a persona vector.
pauldelany|6 months ago
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cindyllm|7 months ago
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hbarka|7 months ago