Question Generation aims to automatically generate questions based on a given input provided as context. A controllable question generation scheme focuses on generating questions with specific attributes, allowing better control. In this study, we propose a few-shot prompting strategy for controlling the generation of question-answer pairs from children's narrative texts. We aim to control two attributes: the question's explicitness and underlying narrative elements. With empirical evaluation, we show the effectiveness of controlling the generation process by employing few-shot prompting side by side with a reference model. Our experiments highlight instances where the few-shot strategy surpasses the reference model, particularly in scenarios such as semantic closeness evaluation and the diversity and coherency of question-answer pairs. However, these improvements are not always statistically significant. The code is publicly available at github.com/bernardoleite/few-shot-prompting-qg-control.

翻译：问题生成旨在根据给定的输入上下文自动生成问题。可控问题生成方案侧重于生成具有特定属性的问题，从而实现更好的控制。在本研究中，我们提出了一种少样本提示策略，用于控制从儿童叙事文本中生成问题-答案对。我们旨在控制两个属性：问题的显性程度和潜在的叙事元素。通过实证评估，我们展示了在参考模型旁边使用少样本提示来控制生成过程的有效性。我们的实验突出了少样本策略在某些场景下超越参考模型的情况，特别是在语义接近度评估以及问题-答案对的多样性和连贯性方面。然而，这些改进并非总是具有统计显著性。代码已公开于 github.com/bernardoleite/few-shot-prompting-qg-control。