How to Engage your Readers? Generating Guiding Questions to Promote Active Reading
Peng Cui, Vilém Zouhar, Xiaoyu Zhang, Mrinmaya Sachan
摘要
Using questions in written text is an effective strategy to enhance readability. However, what makes an active reading question good, what the linguistic role of these questions is, and what is their impact on human reading remains understudied. We introduce GUIDINGQ, a dataset of 10K in-text questions from textbooks and scientific articles. By analyzing the dataset, we present a comprehensive understanding of the use, distribution, and linguistic characteristics of these questions. Then, we explore various approaches to generate such questions using language models. Our results highlight the importance of capturing inter-question relationships and the challenge of question position identification in generating these questions. Finally, we conduct a human study to understand the implication of such questions on reading comprehension. We find that the generated questions are of high quality and are almost as effective as human-written questions in terms of improving readers' memorization and comprehension. github.com/eth-lre/engage-your-readers Questions in titles: How do Philosophers arrive at truth? Is there no quantum form of Einstein Gravity? Why do house-hunting ants recruit in both directions?
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
它引用的顶会 Paper9
- Chain-of-Thought Prompting Elicits Reasoning in Large Language ModelsJason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma 等NeurIPS 2022 · 被引用 22,562 次
- BERTScore: Evaluating Text Generation with BERTTianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger 等ICLR 2020 · 被引用 8,443 次
- G-Eval: NLG Evaluation using Gpt-4 with Better Human AlignmentYang Liu, Dan Iter, Yichong Xu, Shuohang Wang 等EMNLP 2023 · 被引用 549 次
- CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data AnnotationMinzhi Li, Taiwei Shi, Caleb Ziems, Min-Yen Kan 等EMNLP 2023 · 被引用 33 次
- Adaptive and Personalized Exercise Generation for Online Language LearningPeng Cui, Mrinmaya SachanACL 2023 · 被引用 15 次
相关 Paper
- Inquisitive Question Generation for High Level Text ComprehensionWei-Jen Ko, Te-Yuan Chen, Yiyan Huang, Greg Durrett 等EMNLP 2020 · 被引用 32 次
- Asking Questions the Human Way: Scalable Question-Answer Generation from Text CorpusBang Liu, Haojie Wei, Di Niu, Haolan Chen 等WWW 2020 · 被引用 100 次
- Which questions should I answer? Salience Prediction of Inquisitive QuestionsYating Wu, Ritika Mangla, Alex Dimakis, Greg Durrett 等EMNLP 2024 · 被引用 1 次
- LatentQA: Teaching LLMs to Decode Activations Into Natural LanguageAlexander Pan, Lijie Chen, Jacob SteinhardtICLR 2026 · 被引用 31 次
- SciDQA: A Deep Reading Comprehension Dataset over Scientific PapersShruti Singh, Nandan Sarkar, Arman CohanEMNLP 2024 · 被引用 2 次
