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

In-BoXBART: Get Instructions into Biomedical Multi-Task Learning

What kinds of instructional prompts are easier to follow for Language Models
(LMs)? We study this question by conducting extensive empirical analysis that
shed light on important features of successful instructional prompts.
Specifically, we study several classes of reframing techniques for manual
reformulation of prompts into more effective ones. Some examples include
decomposing a complex task instruction into multiple simpler tasks or itemizing
instructions into sequential steps. Our experiments compare the zero-shot and
few-shot performance of LMs prompted with reframed instructions on 12 NLP tasks
across 6 categories. Compared with original instructions, our reframed
instructions lead to significant improvements across LMs with different sizes.
For example, the same reframed prompts boost few-shot performance of
GPT3-series and GPT2-series by 12.5% and 6.7% respectively averaged over all
tasks. Furthermore, reframed instructions reduce the number of examples
required to prompt LMs in the few-shot setting. We hope these
empirically-driven techniques will pave the way towards more effective future
prompting algorithms.

通过对 12 个自然语言处理任务的实验比较，研究着手从多个方面进行指导语的重构，以便更好地指导语言模型的零样本和少样本学习，并且比较了各个模型的不同结果，结果表明：重新构建的指导语有助于提高语言模型的学习效果。