  Single-task models have proven pivotal in solving specific tasks; however,
they have limitations in real-world applications where multi-tasking is
necessary and domain shifts are exhibited. Recently, instructional prompts have
shown significant improvement towards multi-task generalization; however, the
effect of instructional prompts and Multi-Task Learning (MTL) has not been
systematically studied in the biomedical domain. Motivated by this, this paper
explores the impact of instructional prompts for biomedical MTL. We introduce
the BoX, a collection of 32 instruction tasks for Biomedical NLP across (X)
various categories. Using this meta-dataset, we propose a unified model termed
In-BoXBART, that can jointly learn all tasks of the BoX without any
task-specific modules. To the best of our knowledge, this is the first attempt
to propose a unified model in the biomedical domain and use instructions to
achieve generalization across several biomedical tasks. Experimental results
indicate that the proposed model: 1) outperforms the single-task baseline by
~3% and multi-task (without instruction) baseline by ~18% on an average, and 2)
shows ~23% improvement compared to the single-task baseline in few-shot
learning (i.e., 32 instances per task) on an average. Our analysis indicates
that there is significant room for improvement across tasks in the BoX,
implying the scope for future research direction.
