Persona vectors: Monitoring and controlling character traits in language models

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.

> 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.

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".

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

Regarding truth telling, there seems to be some evidence that LLMs at least sometimes "know" when they are lying:

https://arxiv.org/abs/2310.06824

They can always statistically choose to end the conversation or say no.

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.

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.

For ref

https://thezvi.substack.com/p/the-most-forbidden-technique/

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.

On those terms, I’m not sure this is that scary.

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.

[0] https://thezvi.substack.com/p/the-most-forbidden-technique/

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?

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

Thank you for linking to that article; it makes it clear as to what one would need to do to calculate control vectors.

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!

EDIT: Update, getting downvoted here.. Interesting! Here’s a link to the summary of the paper. https://www.anthropic.com/research/tracing-thoughts-language...

> 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.