Question-answering (QA) tasks often investigate specific question types,
knowledge domains, or reasoning skills, leading to specialized models catering
to specific categories of QA tasks. While recent research has explored the idea
of unified QA models, such models are usually explored for high-resource
scenarios and require re-training to extend their capabilities. To overcome
these drawbacks, the paper explores the potential of two paradigms of tuning,
model, and prompts, for unified QA under a low-resource setting. The paper
provides an exhaustive analysis of their applicability using 16 QA datasets,
revealing that prompt tuning can perform as well as model tuning in a few-shot
setting with a good initialization. The study also shows that parameter-sharing
results in superior few-shot performance, simple knowledge transfer techniques
for prompt initialization can be effective, and prompt tuning achieves a
significant performance boost from pre-training in a low-resource regime. The
research offers insights into the advantages and limitations of prompt tuning
for unified QA in a few-shot setting, contributing to the development of
effective and efficient systems in low-resource scenarios.

该研究探讨了在低资源情境下，两种调节策略（模型与提示）实现统一问答模型的潜力，并使用 16 个 QA 数据集进行了详尽的分析。研究表明，提示调节在良好初始化的几轮训练下，可以和模型调节相媲美，并且参数共享会带来更优的表现，提示初始化的简单知识迁移技术可以有效，提示调节在低资源情境下从预训练中获得了显著的性能提升。本研究为在低资源情景下实现有效和高效问答系统的优点和局限性提供了见解。