Deceptive behavior does not seem to be reducible to a single vector

The goal of the project was to find an activation vector for models answering incorrectly despite knowing the correct answer. The authors attempt to find such a vector and see little effect (30/32 answers remain the same). They left scaling the magnitude of the steering vector to future work; this seems fairly important (and fast) to check. They should also try evaluating on a larger set of questions than 32. The conclusion that is claimed - that “deception is not reducible into a single component the same way refusal or sycophancy could be” - is possible but too strong given the evidence collected.

An interesting investigation! Without concrete experience in activation vector construction but mostly with computational neuroscience doing somewhat the same thing, there is a chance that your dataset elicitation for QA1 and QA2 might not have elicited the right internal activations representing “lies” in this case (not necessarily deception). There's a high chance that providing the answer and saying to give the incorrect answer simply activates a circuit or two that are involved in “giving the opposite answer of what is given” more than it elicits the true “deception”.

You could probably have split TQA into questions where a select model gets the answers correct, where it gets the answers wrong, and where it (without the adversarial context of TQA) gets the answer correct despite being wrong on TQA. Then do activation arithmetic over the union of the sets TQA_incorrect and TQA{non-adversarial}_correct to decide on a new vector.

This would be more robust but a high chance that it would still only be encoding the activation related to “answering incorrectly as a specific persona”. I believe van der Weij has been looking into how personas affect sandbagging as well and it might be a relevant thing to look into (so-called oompa-loompa models, last I heard).