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.

本文探讨了在生物医学领域中，应用指导提示和多任务学习的影响。通过引入 BoX，一个包含 32 个生物医学自然语言处理任务的元数据集，提出了 In-BoXBART 统一模型，可以在无需特定任务模块的情况下，共同学习 BoX 中的所有任务。实验结果表明，该模型在多任务学习中普遍优于单任务基线和无指导的多任务基线，并在少样本学习中取得了显著的改善。

In-BoXBART: 在生物医学多任务学习中获取指导