Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.02800 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908397999226880 |
|---|---|
| author | Leite, Bernardo Cardoso, Henrique Lopes |
| author_facet | Leite, Bernardo Cardoso, Henrique Lopes |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_02800 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | On Few-Shot Prompting for Controllable Question-Answer Generation in Narrative Comprehension Leite, Bernardo Cardoso, Henrique Lopes Computation and Language Artificial Intelligence 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. |
| title | On Few-Shot Prompting for Controllable Question-Answer Generation in Narrative Comprehension |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2404.02800 |