# Improving Reading Comprehension Question Generation with Data Augmentation and Overgenerate-and-rank

arXiv: https://arxiv.org/abs/2306.08847 · HTML: https://mlsanity.com/paper/2306.08847/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 40 references that are in the map.
- First posted on arXiv 15 Jun 2023.

## Builds on (most cited first, 22 of 22)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [Fine-Tuning Language Models from Human Preferences](https://mlsanity.com/paper/1909.08593/) (2019)
- [Measuring and Narrowing the Compositionality Gap in Language Models](https://mlsanity.com/paper/2210.03350/) (2022)
- [REPLUG: Retrieval-Augmented Black-Box Language Models](https://mlsanity.com/paper/2301.12652/) (2023)
- [The NarrativeQA Reading Comprehension Challenge](https://mlsanity.com/paper/1712.07040/) (2017)
- [A Contrastive Framework for Neural Text Generation](https://mlsanity.com/paper/2202.06417/) (2022)
- [ConvBERT: Improving BERT with Span-based Dynamic Convolution](https://mlsanity.com/paper/2008.02496/) (2020)
- [Fantastic Questions and Where to Find Them: FairytaleQA -- An Authentic Dataset for Narrative Comprehension](https://mlsanity.com/paper/2203.13947/) (2022)
- [StoryBuddy: A Human-AI Collaborative Chatbot for Parent-Child Interactive Storytelling with Flexible Parental Involvement](https://mlsanity.com/paper/2202.06205/) (2022)
- [TellMeWhy: A Dataset for Answering Why-Questions in Narratives](https://mlsanity.com/paper/2106.06132/) (2021)
- [Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints](https://mlsanity.com/paper/2109.04546/) (2021)
- [Educational Question Generation of Children Storybooks via Question Type Distribution Learning and Event-Centric Summarization](https://mlsanity.com/paper/2203.14187/) (2022)
- [It is AI's Turn to Ask Humans a Question: Question-Answer Pair Generation for Children's Story Books](https://mlsanity.com/paper/2109.03423/) (2021)
- [Selecting Better Samples from Pre-trained LLMs: A Case Study on Question Generation](https://mlsanity.com/paper/2209.11000/) (2022)
- [A Feasibility Study of Answer-Agnostic Question Generation for Education](https://mlsanity.com/paper/2203.08685/) (2022)
- [NECE: Narrative Event Chain Extraction Toolkit](https://mlsanity.com/paper/2208.08063/) (2022)

## Built on by (most cited first, 12 of 12)

- [Let the LLMs Talk: Simulating Human-to-Human Conversational QA via Zero-Shot LLM-to-LLM Interactions](https://mlsanity.com/paper/2312.02913/) (2023)
- [SMART: Simulated Students Aligned with Item Response Theory for Question Difficulty Prediction](https://mlsanity.com/paper/2507.05129/) (2025)
- [Improving Socratic Question Generation using Data Augmentation and Preference Optimization](https://mlsanity.com/paper/2403.00199/) (2024)
- [Improving Automated Distractor Generation for Math Multiple-choice Questions with Overgenerate-and-rank](https://mlsanity.com/paper/2405.05144/) (2024)
- [Math Multiple Choice Question Generation via Human-Large Language Model Collaboration](https://mlsanity.com/paper/2405.00864/) (2024)
- [The StudyChat Dataset: Analyzing Student Dialogues With ChatGPT in an Artificial Intelligence Course](https://mlsanity.com/paper/2503.07928/) (2025)
- [DiVERT: Distractor Generation with Variational Errors Represented as Text for Math Multiple-choice Questions](https://mlsanity.com/paper/2406.19356/) (2024)
- [Using Large Language Models for Student-Code Guided Test Case Generation in Computer Science Education](https://mlsanity.com/paper/2402.07081/) (2024)
- [What Would You Ask When You First Saw a2+b2=c2? Evaluating LLM on Curiosity-Driven Questioning](https://mlsanity.com/paper/2409.17172/) (2024)
- [Linguistically-Controlled Paraphrase Generation](https://mlsanity.com/paper/2410.24199/) (2024)
- [Automatic Generation of Inference Making Questions for Reading Comprehension Assessments](https://mlsanity.com/paper/2506.08260/) (2025)
- [From Model to Classroom: Evaluating Generated MCQs for Portuguese with Narrative and Difficulty Concerns](https://mlsanity.com/paper/2506.15598/) (2025)

## Abstract (arXiv)

Reading comprehension is a crucial skill in many aspects of education, including language learning, cognitive development, and fostering early literacy skills in children. Automated answer-aware reading comprehension question generation has significant potential to scale up learner support in educational activities. One key technical challenge in this setting is that there can be multiple questions, sometimes very different from each other, with the same answer; a trained question generation method may not necessarily know which question human educators would prefer. To address this challenge, we propose 1) a data augmentation method that enriches the training dataset with diverse questions given the same context and answer and 2) an overgenerate-and-rank method to select the best question from a pool of candidates. We evaluate our method on the FairytaleQA dataset, showing a 5% absolute improvement in ROUGE-L over the best existing method. We also demonstrate the effectiveness of our method in generating harder, "implicit" questions, where the answers are not contained in the context as text spans.