  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.

Computation and Language;Artificial Intelligence;Computer Vision and Pattern Recognition;Machine Learning