Few-shot question answering (QA) aims at precisely discovering answers to a set of questions from context passages while only a few training samples are available. Although existing studies have made some progress and can usually achieve proper results, they suffer from understanding deep semantics for reasoning out the questions. In this paper, we develop Gotta, a Generative prOmpT-based daTa Augmentation framework to mitigate the challenge above. Inspired by the human reasoning process, we propose to integrate the cloze task to enhance few-shot QA learning. Following the recent success of prompt-tuning, we present the cloze task in the same format as the main QA task, allowing the model to learn both tasks seamlessly together to fully take advantage of the power of prompt-tuning. Extensive experiments on widely used benchmarks demonstrate that Gotta consistently outperforms competitive baselines, validating the effectiveness of our proposed prompt-tuning-based cloze task, which not only fine-tunes language models but also learns to guide reasoning in QA tasks. Further analysis shows that the prompt-based loss incorporates the auxiliary task better than the multi-task loss, highlighting the strength of prompt-tuning on the few-shot QA task.

翻译：少样本问答任务旨在从上下文段落中精确发现一系列问题的答案，且仅需少量训练样本即可完成。尽管现有研究已取得一定进展，通常能获得合理结果，但在理解深层语义以推理问题答案方面仍存在不足。本文提出Gotta——一种基于生成式提示的数据增强框架，以缓解上述挑战。受人类推理过程启发，我们创新性地引入完形填空任务来增强少样本问答学习。基于提示调优的最新成功，我们将完形填空任务设计为与主问答任务相同的格式，使得模型能够无缝地联合学习这两个任务，从而充分利用提示调优的优势。在广泛使用的基准测试上进行的大量实验表明，Gotta持续优于具有竞争力的基线模型，验证了我们提出的基于提示调优的完形填空任务的有效性——它不仅能够微调语言模型，还能学习引导问答任务中的推理过程。进一步分析表明，基于提示的损失函数比多任务损失函数能更好地整合辅助任务，凸显了提示调优在少样本问答任务中的优势。