# Leaf: Multiple-Choice Question Generation

arXiv: https://arxiv.org/abs/2201.09012 · HTML: https://mlsanity.com/paper/2201.09012/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 48 references that are in the map.
- First posted on arXiv 22 Jan 2022.

## Builds on (most cited first, 30 of 31)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://mlsanity.com/paper/1803.05457/) (2018)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering](https://mlsanity.com/paper/1809.02789/) (2018)
- [RACE: Large-scale ReAding Comprehension Dataset From Examinations](https://mlsanity.com/paper/1704.04683/) (2017)
- [XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization](https://mlsanity.com/paper/2003.11080/) (2020)
- [Unified Language Model Pre-training for Natural Language Understanding and Generation](https://mlsanity.com/paper/1905.03197/) (2019)
- [TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages](https://mlsanity.com/paper/2003.05002/) (2020)
- [MLQA: Evaluating Cross-lingual Extractive Question Answering](https://mlsanity.com/paper/1910.07475/) (2019)
- [NewsQA: A Machine Comprehension Dataset](https://mlsanity.com/paper/1611.09830/) (2016)
- [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://mlsanity.com/paper/2001.04063/) (2020)
- [UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training](https://mlsanity.com/paper/2002.12804/) (2020)
- [Learning to Ask: Neural Question Generation for Reading Comprehension](https://mlsanity.com/paper/1705.00106/) (2017)
- [Neural Question Generation from Text: A Preliminary Study](https://mlsanity.com/paper/1704.01792/) (2017)
- [EXAMS: A Multi-Subject High School Examinations Dataset for Cross-Lingual and Multilingual Question Answering](https://mlsanity.com/paper/2011.03080/) (2020)
- [QuaRel: A Dataset and Models for Answering Questions about Qualitative Relationships](https://mlsanity.com/paper/1811.08048/) (2018)
- [ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation](https://mlsanity.com/paper/2001.11314/) (2020)
- [From 'F' to 'A' on the N.Y. Regents Science Exams: An Overview of the Aristo Project](https://mlsanity.com/paper/1909.01958/) (2019)
- [Generating Distractors for Reading Comprehension Questions from Real Examinations](https://mlsanity.com/paper/1809.02768/) (2018)
- [A BERT-based Distractor Generation Scheme with Multi-tasking and Negative Answer Training Strategies](https://mlsanity.com/paper/2010.05384/) (2020)
- [sense2vec - A Fast and Accurate Method for Word Sense Disambiguation In Neural Word Embeddings](https://mlsanity.com/paper/1511.06388/) (2015)
- [Generating Question-Answer Hierarchies](https://mlsanity.com/paper/1906.02622/) (2019)
- [BiPaR: A Bilingual Parallel Dataset for Multilingual and Cross-lingual Reading Comprehension on Novels](https://mlsanity.com/paper/1910.05040/) (2019)
- [Better Distractions: Transformer-based Distractor Generation and Multiple Choice Question Filtering](https://mlsanity.com/paper/2010.09598/) (2020)
- [Beyond English-Only Reading Comprehension: Experiments in Zero-Shot Multilingual Transfer for Bulgarian](https://mlsanity.com/paper/1908.01519/) (2019)
- [AnswerQuest: A System for Generating Question-Answer Items from Multi-Paragraph Documents](https://mlsanity.com/paper/2103.03820/) (2021)

## Built on by (most cited first, 9 of 9)

- [Distractor Generation in Multiple-Choice Tasks: A Survey of Methods, Datasets, and Evaluation](https://mlsanity.com/paper/2402.01512/) (2024)
- [Scalable Educational Question Generation with Pre-trained Language Models](https://mlsanity.com/paper/2305.07871/) (2023)
- [A Novel Multi-Stage Prompting Approach for Language Agnostic MCQ Generation using GPT](https://mlsanity.com/paper/2401.07098/) (2024)
- [Evaluating the Knowledge Dependency of Questions](https://mlsanity.com/paper/2211.11902/) (2022)
- [KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration](https://mlsanity.com/paper/2602.20135/) (2026)
- [Survey of Natural Language Processing for Education: Taxonomy, Systematic Review, and Future Trends](https://mlsanity.com/paper/2401.07518/) (2024)
- [Pre-Training With Scientific Text Improves Educational Question Generation](https://mlsanity.com/paper/2212.03869/) (2022)
- [UniPoll: A Unified Social Media Poll Generation Framework via Multi-Objective Optimization](https://mlsanity.com/paper/2306.06851/) (2023)
- [Generating Multiple-Choice Knowledge Questions with Interpretable Difficulty Estimation using Knowledge Graphs and Large Language Models](https://mlsanity.com/paper/2604.10748/) (2026)

## Abstract (arXiv)

Testing with quiz questions has proven to be an effective way to assess and improve the educational process. However, manually creating quizzes is tedious and time-consuming. To address this challenge, we present Leaf, a system for generating multiple-choice questions from factual text. In addition to being very well suited for the classroom, Leaf could also be used in an industrial setting, e.g., to facilitate onboarding and knowledge sharing, or as a component of chatbots, question answering systems, or Massive Open Online Courses (MOOCs). The code and the demo are available on https://github.com/KristiyanVachev/Leaf-Question-Generation.