  While instruction-tuned models have shown remarkable success in various
natural language processing tasks, accurately evaluating their ability to
follow instructions remains challenging. Existing benchmarks primarily focus on
common instructions that align well with what the model learned during
training. However, proficiency in responding to these instructions does not
necessarily imply strong ability in instruction following. In this paper, we
propose a novel instruction-following evaluation protocol called verbalizer
manipulation. It instructs the model to verbalize the task label with words
aligning with model priors to different extents, adopting verbalizers from
highly aligned (e.g., outputting ``postive'' for positive sentiment), to
minimally aligned (e.g., outputting ``negative'' for positive sentiment).
Verbalizer manipulation can be seamlessly integrated with any classification
benchmark to examine the model's reliance on priors and its ability to override
them to accurately follow the instructions. We conduct a comprehensive
evaluation of four major model families across nine datasets, employing twelve
sets of verbalizers for each of them. We observe that the instruction-following
abilities of models, across different families and scales, are significantly
distinguished by their performance on less natural verbalizers. Even the
strongest GPT-4 model struggles to perform better than random guessing on the
most challenging verbalizer, emphasizing the need for continued advancements to
improve their instruction-following abilities.
