  Contrast set consistency is a robustness measurement that evaluates the rate
at which a model correctly responds to all instances in a bundle of minimally
different examples relying on the same knowledge. To draw additional insights,
we propose to complement consistency with relative consistency -- the
probability that an equally accurate model would surpass the consistency of the
proposed model, given a distribution over possible consistencies. Models with
100% relative consistency have reached a consistency peak for their accuracy.
We reflect on prior work that reports consistency in contrast sets and observe
that relative consistency can alter the assessment of a model's consistency
compared to another. We anticipate that our proposed measurement and insights
will influence future studies aiming to promote consistent behavior in models.
