On Few-Shot Prompting for Controllable Question-Answer Generation in
  Narrative Comprehension
Bernardo Leite Henrique Lopes Cardoso
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.