Recently introduced instruction-paradigm empowers non-expert users to leverage NLP resources by defining a new task in natural language. Instruction-tuned models have significantly outperformed multitask learning models (without instruction); however they are far from state-of-the-art task-specific models. Conventional approaches to improve model performance via creating datasets with large number of task instances or architectural changes in the model may not be feasible for non-expert users. However, they can write alternate instructions to represent an instruction task. Is Instruction-augmentation helpful? We augment a subset of tasks in the expanded version of NATURAL INSTRUCTIONS with additional instructions and find that it significantly improves model performance (up to 35%), especially in the low-data regime. Our results indicate that an additional instruction can be equivalent to ~200 data samples on average across tasks.

翻译：最近引入的指令范式使得非专业用户能够通过用自然语言定义新任务来利用自然语言处理资源。指令调优模型在多任务学习模型（无指令）上表现显著更优，但仍远逊于最先进的特定任务模型。传统上通过创建包含大量任务实例的数据集或修改模型架构来提升性能的方法，对非专业用户而言可能不可行。然而，用户可以编写替代指令来表示一个指令任务。指令增强是否有帮助？我们对扩展版NATURAL INSTRUCTIONS中的部分任务补充了额外指令，发现这能显著提升模型性能（最高提升35%），尤其是在低数据场景下。我们的结果表明，平均而言，每条额外指令可等价于约200个数据样本。