Elsie Li Chen Ong
2024
Few-shot Question Generation for Reading Comprehension
Yin Poon
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John Sie Yuen Lee
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Yu Yan Lam
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Wing Lam Suen
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Elsie Li Chen Ong
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Samuel Kai Wah Chu
Proceedings of the 10th SIGHAN Workshop on Chinese Language Processing (SIGHAN-10)
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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