@inproceedings{poon-etal-2024-shot,
title = "Few-shot Question Generation for Reading Comprehension",
author = "Poon, Yin and
Lee, John Sie Yuen and
Lam, Yu Yan and
Suen, Wing Lam and
Ong, Elsie Li Chen and
Chu, Samuel Kai Wah",
editor = "Wong, Kam-Fai and
Zhang, Min and
Xu, Ruifeng and
Li, Jing and
Wei, Zhongyu and
Gui, Lin and
Liang, Bin and
Zhao, Runcong",
booktitle = "Proceedings of the 10th SIGHAN Workshop on Chinese Language Processing (SIGHAN-10)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.sighan-1.3/",
pages = "21--27",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Few-shot Question Generation for Reading Comprehension
%A Poon, Yin
%A Lee, John Sie Yuen
%A Lam, Yu Yan
%A Suen, Wing Lam
%A Ong, Elsie Li Chen
%A Chu, Samuel Kai Wah
%Y Wong, Kam-Fai
%Y Zhang, Min
%Y Xu, Ruifeng
%Y Li, Jing
%Y Wei, Zhongyu
%Y Gui, Lin
%Y Liang, Bin
%Y Zhao, Runcong
%S Proceedings of the 10th SIGHAN Workshop on Chinese Language Processing (SIGHAN-10)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F poon-etal-2024-shot
%X 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.
%U https://aclanthology.org/2024.sighan-1.3/
%P 21-27
Markdown (Informal)
[Few-shot Question Generation for Reading Comprehension](https://aclanthology.org/2024.sighan-1.3/) (Poon et al., SIGHAN 2024)
ACL
- Yin Poon, John Sie Yuen Lee, Yu Yan Lam, Wing Lam Suen, Elsie Li Chen Ong, and Samuel Kai Wah Chu. 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.