Although language models (LMs) have boosted the performance of Question Answering, they still need plenty of data. Data annotation, in contrast, is a time-consuming process. This especially applies to Question Answering, where possibly large documents have to be parsed and annotated with questions and their corresponding answers. Furthermore, Question Answering models often only work well for the domain they were trained on. Since annotation is costly, we argue that domain-agnostic knowledge from LMs, such as linguistic understanding, is sufficient to create a well-curated dataset. With this motivation, we show that using large language models can improve Question Answering performance on various datasets in the few-shot setting compared to state-of-the-art approaches. For this, we perform data generation leveraging the Prompting framework, suggesting that language models contain valuable task-agnostic knowledge that can be used beyond the common pre-training/fine-tuning scheme. As a result, we consistently outperform previous approaches on few-shot Question Answering.

翻译：尽管语言模型提升了问答任务的性能，但仍需大量数据支持。然而，数据标注是一个耗时过程，尤其适用于问答任务——可能需要对大型文档进行解析，并标注问题及其对应答案。此外，问答模型通常仅在训练领域表现良好。考虑到标注成本高昂，我们认为语言模型中与领域无关的知识（例如语言理解能力）足以构建精心策划的数据集。基于这一动机，我们证明在少样本场景下，使用大型语言模型能够有效提升多个数据集上的问答性能，性能优于现有最优方法。为此，我们利用提示框架进行数据生成，这表明语言模型包含超越传统预训练/微调范式的、有价值的任务无关知识。实验结果显示，我们在少样本问答任务上持续优于先前方法。