论文标题
值得多少数据样本值？
How Many Data Samples is an Additional Instruction Worth?
论文作者
论文摘要
最近引入了指令 - 范式使非专家用户通过定义自然语言的新任务来利用NLP资源。指导调整的模型的表现明显优于多任务学习模型（没有指导）；但是，它们远非特定于任务的最新模型。通过创建具有大量任务实例的数据集或模型中的架构更改来改善模型性能的常规方法可能对非专家用户不可行。但是，他们可以编写替代说明来表示指令任务。指导启动有用吗？我们在扩展的自然说明中增强了一部分任务，并通过其他说明进行了其他说明，并发现它可显着提高模型性能（最高35％），尤其是在低数据策略中。我们的结果表明，在整个任务中，其他指令平均可以等于〜200个数据样本。
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.
