Towards Reliable Evaluation of Behavior Steering Interventions in LLMs

Understand

Representation engineering methods have recently shown promise for enabling efficient steering of model behavior.

• However, evaluation pipelines for these methods have primarily relied on subjective demonstrations, instead of quantitative, objective metrics.
• We aim to take a step towards addressing this issue by advocating for four properties missing from current evaluations: (i) contexts sufficiently similar to downstream tasks should be used for assessing intervention quality; (ii) model likelihoods should be accounted for; (iii) evaluations should allow for standardized comparisons across different target behaviors; and (iv) baseline comparisons should be offered.
• We introduce an evaluation pipeline grounded in these criteria, offering both a quantitative and visual analysis of how effectively a given method works.