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Recently introduced instruction-paradigm empowers non-expert users toleverage NLP resources by defining a new task in natural language.Instruction-tuned models have significantly outperformed multitask learningmodels (without instruction); however they are far from state-of-the-arttask-specific models. Conventional approaches to improve model performance viacreating datasets with large number of task instances or architectural changesin the model may not be feasible for non-expert users. However, they can writealternate instructions to represent an instruction task. IsInstruction-augmentation helpful? We augment a subset of tasks in the expandedversion of NATURAL INSTRUCTIONS with additional instructions and find that itsignificantly improves model performance (up to 35%), especially in thelow-data regime. Our results indicate that an additional instruction can beequivalent to ~200 data samples on average across tasks.

How Many Data Samples is an Additional Instruction Worth?