On Few-Shot Prompting for Controllable Question-Answer Generation in Narrative Comprehension
<|reference_start|>On Few-Shot Prompting for Controllable Question-Answer Generation in Narrative Comprehension: 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.<|reference_end|>
@article{leite2024on,
    title={On Few-Shot Prompting for Controllable Question-Answer Generation in
  Narrative Comprehension},
    author={Bernardo Leite and Henrique Lopes Cardoso},
    journal={arXiv preprint arXiv:2404.02800},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.02800},
    primaryClass={cs.CL cs.AI}
}