Evaluating Instruction-Following Capabilities of Large Language Models through Verbalizer Manipulation

Instruction-following capability is an important aspect of large language models, but existing benchmarks primarily focus on common instructions that align well with model priors. This paper proposes a novel evaluation protocol called verbalizer manipulation to systematically assess models' ability to follow instructions that may not align with their prior knowledge.