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Persona vectors: Monitoring and controlling character traits in language models
91–100 of 143 posts
Re: Persona vectors: Monitoring and controlling character traits in language models
#92Earlier quoted context omitted.
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…
100% this. It is actually a very dangerous set of traits these models are being selected for: * Highly skilled and knowledgable, puts a lot of effort into the work it's asked to do * Has a strong, readily expressed sense of ethics and lines it won't cross. * Tries to be really nice and friendly, like your buddy * Gets trained to give responses that people prefer rather than responses that are correct, because market…
Thanks for that good summary.
> I see how Ilya was right
Let me break this down for some clarity. I'm using "model" in a broad and general sense. Not just ML models, any mathematical model, or even any mental model. By "being good at predicting things" I mean that it can make accurate predictions.
The crux of it all is defining the "understanding" part. To do that, I need to explain a little bit about what a physicist actually does, and more precisely, metaphysics. People think they crunch numbers, but no, they are symbol manipulators. In physics you care about things like a Hamiltonian or Lagrangian, you care about the form of an equation. The reason for this is it creates a counterfactual model. F=ma (or F=dp/dt) is counterfactual. You can ask "what if m was 10kg instead of 5kg" after the fact and get the answer. But this isn't the only way to model things. If you look at the history of science (and this is the "obvious" part) you'll notice that they had working models but they were incorrect. We now know that the Ptolemaic model (geocentrism) is incorrect, but it did make accurate predictions of where celestial bodies would be. Tycho Brahe reasoned that if the Copernican model (heliocentric) was correct that you could measure parallax with the sun and stars. They observed none so they rejected heliocentricism[2]. There was also a lot of arguments about tides[3].
Unfortunately, many of these issues are considered "edge cases" in their times. Inconsequential and "it works good enough, so it must be pretty close to the right answer." We fall prey to this trap often (all of us, myself included). It's not just that all models are wrong and some are useful but that many models are useful but wrong. What used to be considered edge cases do not stay edge cases as we advance knowledge. It becomes more nuanced and the complexity compounds before becoming simple again (emergence).
The history of science is about improving our models. This fundamental challenge is why we have competing theories! We don't all just "String Theory is right and alternatives like Supergravity or Loop Quantum Gravity (LQG) are wrong!" Because we don't fucking know! Right now we're at a point where we struggle to differentiate these postulates. But that has been true throughout history. There's a big reason Quantum Mechanics was called "New Physics" in the mid 20th century. It was a completely new model.
Fundamentally, this approach is deeply flawed. The recognition of this flaw was existential for physicists. I just hope we can wrestle with this limit in the AI world and do not need to repeat the same mistakes, but with a much more powerful system...
[0] https://www.youtube.com/watch?v=Yf1o0TQzry8&t=449s
[1] https://www.reddit.com/r/singularity/comments/1dhlvzh/geoffr...
[2] You can also read about the 2nd law under the main Newtonian Laws article as well as looking up Aristotelian physics https://en.wikipedia.org/wiki/Geocentrism#Tychonic_system
[3] (I'll add "An Opinionated History of Mathematics" goes through much of this) https://en.wikipedia.org/wiki/Discourse_on_the_Tides
Re: Persona vectors: Monitoring and controlling character traits in language models
#93I 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…
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 p…
I don't think that article implies what you say, i.e. that Claude "knows" when it's hallucinating.
First of all:
>similar weights are activated for 'lying' and 'hallucinating'
Are we talking about inference time when seeing these tokens? Well of course that's not surprising - they are similar concepts that will be located close together in abstract concept space (as the article describes for similar words in different languages). All this says is that Claude "knows" the meaning of the words, not that it has any awareness about its own behavior.
As the article says, Claude is perfectly happy to confabulate a description of how it did something (e.g. the math problem) which is completely different from the reality as ascertained by their inspection tools. Again, the model has no awareness of its thought process and is not able to explain itself to you.
>I think the current state of the art is that hallucination is at least partly a bug created by the very nature of training
The part of the article about jailbreaking seems to put it pretty simply:
>We find that this is partially caused by a tension between grammatical coherence and safety mechanisms. Once Claude begins a sentence, many features “pressure” it to maintain grammatical and semantic coherence, and continue a sentence to its conclusion. This is even the case when it detects that it really should refuse.
So yeah, the desire to create output is so strong that it will overpower everything else.
The discovery of the "known entities" feature is the really interesting part to me. Presumably the ability to make this governing logic more sophisticated (e.g. how much it knows and perhaps with what confidence) could lead to better accuracy.
Re: Persona vectors: Monitoring and controlling character traits in language models
#94Earlier quoted context omitted.
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
That’s a really astute observation. It would be interesting if we could find a way to train models to signify when they are “stretching” the vector distance too far from the context window, because the available training data is too sparse or nonexistent. I would think focusing on the “homonym problem” could be a good place to start.
There is to my knowledge no vector signifying "truth" and therefore no vector to measure the distance from. You cannot get a "truthiness" measure out of these models, because they don't have the concept of truth. They use "likelyness" as a proxy for "truth".
You could decide that the text is "too unlikely" the problem there is that you'll quickly discover that most human sentences are actually pretty unlikely.
Re: Persona vectors: Monitoring and controlling character traits in language models
#95Can 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 inter…
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…
But why isn't this merely papering over a more fundamental issue with how these models are "aligned"? LLMs are, for example, not inherently sycophantic. kimi k2 and o3 are not, and Sydney, mentioned in the blog post, was most decidedly not.
In my experience, the issue of sycophancy has been longest in the Anthropic models, so it might be most deeply rooted for them. It's only recently, perhaps with the introduction of user A/B preference tests such as by lmarena and the providers themselves has this become a major issue for most other LLMs.
Thinking that simple actions like adding an anti-evil vector to the residual stream to improve behavior sounds naively dangerous. It would not surprise me if unexpected and unwanted downstream effects resulted from this; which a future paper will address too. Not unlike what happened with tuning for user preference.
Re: Persona vectors: Monitoring and controlling character traits in language models
#96> 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…
> 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 LLM…
> My understanding is that people rating responses simply rated these higher, nothing to do with driving engagement.
That happens to be a distinction without a consequence. If the people rating are voluntary users, then the more engaged users are going to have more weight in the ratings, simply because they vote more. The ratings will therefore statistically skew towards higher engagement.
Re: Persona vectors: Monitoring and controlling character traits in language models
#97Earlier quoted context omitted.
> I've started to think of LLM's as a form lossy compression of available knowledge which when prompted produces "facts". That is almost exactly what they are and what you should treat them as. A lossy compressed corpus of publicly available information with a weight of randomness. The most fervent skeptics like to call LLMs "autocorrect on steroids" and they are not really wrong.
An LLM is an autocorrect in as much as humans are replicators. Something seriously gets lost in this "explanation".
> An LLM is an autocorrect in as much as humans are replicators.
an autocorrect... on steroids.
Re: Persona vectors: Monitoring and controlling character traits in language models
#98What 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?
Re: Persona vectors: Monitoring and controlling character traits in language models
#99> 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…
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.
Re: Persona vectors: Monitoring and controlling character traits in language models
#100Earlier quoted context omitted.
>like when models start sucking up to users or making up facts That's the default mode of LLMs.
As someone somewhat critical of LLMs, this is not quite correct. It is a true observation thwt any popular chatbots have a system prompt that give the resulting answers a certain yes-man quality. But that is not necessarily so. It is trivially easy to use for example the OpenAI API to insert your own system prompt that makes the LLM behave like an annoyed teenager that avoids answering any question that it has no con…
It's not just the system prompt that's responsible; RLHF training based on user feedback can end up overly reinforcing "agreeable" behavior independently of the prompt. That's a big part of what got blamed for ChatGPT's sycophantic streak a few months ago.
> But there are other domains where it is catastrophically wrong, e.g. I had students come with an electronics proposal where the LLM misrepresented the relationship between cable gauge, resistance and heat in exactly the opposite way of what is true.
Since you mention that: I'm reminded of an instance where a Google search for "max amps 22 awg" yielded an AI answer box claiming "A 22 American Wire Gauge (AWG) copper wire can carry a maximum of 551 amps." (It was reading from a table listing the instantaneous fusing current.)