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Edit 6/20/24: The authors updated the paper; see my comment.
To better understand the benefit of using features, for a few case studies of interest, we obtained linear probes using the same positive / negative examples that we used to identify the feature, by subtracting the residual stream activity in response to the negative example(s) from the activity in response to the positive example(s). We experimented with (1) visualizing the top-activating examples for probe directions, using the same pipeline we use for our features, and (2) using these probe directions for steering.
These vectors are not "linear probes" (which are generally optimized via SGD on a logistic regression task for a supervised dataset of yes/no examples), they are difference-in-means of activation vectors
So call them "steering vectors"!
As a side note, using actual linear probe directions tends to not steer models very well (see eg Inference Time Intervention table 3 on page 8)
In my experience, steering vectors generally require averaging over at least 32 contrast pairs. Anthropic only compares to 1-3 contrast pairs, which is inappropriate.
Since feature clamping needs fewer prompts for some tasks, that is a real benefit, but you have to amortize that benefit over the huge SAE effort needed to find those features.
Also note that you can generate synthetic data for the steering vectors using an LLM, it isn't too hard.
For steering on a single task, then, steering vectors still win out in terms of amortized sample complexity (assuming the steering vectors are effective given ~32/128/256 contrast pairs, which I doubt will always be true)
In all cases, we were unable to interpret the probe directions from their activating examples. In most cases (with a few exceptions) we were unable to adjust the model’s behavior in the expected way by adding perturbations along the probe directions, even in cases where feature steering was successful (see this appendix for more details).
...
We note that these negative results do not imply that linear probes are not useful in general. Rather, they suggest that, in the “few-shot” prompting regime, they are less interpretable and effective for model steering than dictionary learning features.
I totally expect feature clamping to still win out in a bunch of comparisons, it's really cool, but Anthropic's actual comparisons don't seem good and predictably underrate steering vectors.
The fact that the Anthropic paper gets the comparison (and especially terminology) meaningfully wrong makes me more wary of their results going forwards.
The authors updated the Scaling Monosemanticity paper. Relevant updates include:
1. In the intro, they added:
[...]
2. The related work section now credits the rich history behind steering vectors / activation engineering, including not just my team's work on activation additions, but also older literature in VAEs and GANs. (EDIT: Apparently this was always there? Maybe I misremembered the diff.)
3. The comparison results are now in an appendix and are much more hedged, noting they didn't evaluate properly according to a steering vector baseline.
While it would have been better to have done this the first time, I really appreciate the team updating the paper to more clearly credit past work. :)
I think DIM and LR aren't spiritually different (e.g. LR with infinite L2 regularization gives you the same direction as DIM), even though in practice DIM is better for steering (and ablations). But I agree with you that "steering vectors" is the good expression to talk about directions used for steering (while I would use linear probes to talk about directions used to extract information or trained to extract information and used for another purpose).