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.

翻译：尽管指令微调模型在各种自然语言处理任务中取得了显著成功，但准确评估其遵循指令的能力仍具挑战性。现有基准测试主要关注与模型训练内容高度一致的常见指令，然而，熟练应对这些指令并不必然意味着具备强大的指令遵循能力。本文提出一种名为"言语器操控"的创新型指令遵循评估协议。该协议通过引导模型使用与模型先验知识不同匹配程度的词汇来表述任务标签，采用从高度匹配（如对积极情感输出"positive"）到最低匹配（如对积极情感输出"negative"）的言语器。言语器操控可无缝集成至任何分类基准测试中，用于检验模型对先验知识的依赖程度及其覆盖先验知识以精确遵循指令的能力。我们对九个数据集上的四个主要模型家族进行了全面评估，每个模型均使用十二组言语器。研究观察到，不同家族和规模的模型在非自然言语器上的性能显著区分了其指令遵循能力。即使是最强大的GPT-4模型，在最具挑战性的言语器条件下也仅能勉强优于随机猜测，这凸显了持续提升模型指令遵循能力的必要性。