Few-shot Question Generation for Reading Comprehension
Yin Poon, John Sie Yuen Lee, Yuylam@hkmu.edu.hk Yuylam@hkmu.edu.hk, Wlsuen@hkmu.edu.hk Wlsuen@hkmu.edu.hk, Eong@hkmu.edu.hk Eong@hkmu.edu.hk, Skwchu@hkmu.edu.hk Skwchu@hkmu.edu.hk
Abstract
According to the internationally recognized PIRLS (Progress in International Reading Literacy Study) assessment standards, reading comprehension questions should require not only information retrieval, but also higher-order processes such as inferencing, interpreting and evaluation. However, these kinds of questions are often not available in large quantities for training question generation models. This paper investigates whether pre-trained Large Language Models (LLMs) can produce higher-order questions. Human assessment on a Chinese dataset shows that few-shot LLM prompting generates more usable and higher-order questions than two competitive neural baselines.- Anthology ID:
- 2024.sighan-1.3
- Volume:
- Proceedings of the 10th SIGHAN Workshop on Chinese Language Processing (SIGHAN-10)
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Kam-Fai Wong, Min Zhang, Ruifeng Xu, Jing Li, Zhongyu Wei, Lin Gui, Bin Liang, Runcong Zhao
- Venues:
- SIGHAN | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 21–27
- Language:
- URL:
- https://aclanthology.org/2024.sighan-1.3
- DOI:
- Cite (ACL):
- Yin Poon, John Sie Yuen Lee, Yuylam@hkmu.edu.hk Yuylam@hkmu.edu.hk, Wlsuen@hkmu.edu.hk Wlsuen@hkmu.edu.hk, Eong@hkmu.edu.hk Eong@hkmu.edu.hk, and Skwchu@hkmu.edu.hk Skwchu@hkmu.edu.hk. 2024. Few-shot Question Generation for Reading Comprehension. In Proceedings of the 10th SIGHAN Workshop on Chinese Language Processing (SIGHAN-10), pages 21–27, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- Few-shot Question Generation for Reading Comprehension (Poon et al., SIGHAN-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2024.sighan-1.3.pdf