Few-shot Unified Question Answering: Tuning Models or Prompts?
Srijan Bansal Semih Yavuz Bo Pang Meghana Bhat Yingbo Zhou
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.