How Many Data Samples is an Additional Instruction Worth?
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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 large datasets with lots of task instances or architectural/training changes in model may not be feasible for non-expert users. However, they can write alternate instructions to represent an instruction task. Is Instruction-augumentation helpful? We augment a subset of tasks in the expanded version of NATURAL INSTRUCTIONS with additional instructions and find that these significantly improve model performance (up to 35 low-data regime. Our results indicate that an additional instruction can be equivalent to 200 data samples on average across tasks.
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