# ConQuer: A Framework for Concept-Based Quiz Generation

arXiv: https://arxiv.org/abs/2503.14662 · HTML: https://mlsanity.com/paper/2503.14662/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 25 references that are in the map.
- First posted on arXiv 18 Mar 2025.

## Builds on (most cited first, 8 of 8)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [CAMPHOR: Collaborative Agents for Multi-input Planning and High-Order Reasoning On Device](https://mlsanity.com/paper/2410.09407/) (2024)
- [Comparison of Large Language Models for Generating Contextually Relevant Questions](https://mlsanity.com/paper/2407.20578/) (2024)

## Built on by (most cited first, 2 of 2)

- [Chengyu-Bench: Benchmarking Large Language Models for Chinese Idiom Understanding and Use](https://mlsanity.com/paper/2506.18105/) (2025)
- [Optimizing Coverage and Difficulty in Reinforcement Learning for Quiz Composition](https://mlsanity.com/paper/2603.27695/) (2026)

## Abstract (arXiv)

Quizzes play a crucial role in education by reinforcing students' understanding of key concepts and encouraging self-directed exploration. However, compiling high-quality quizzes can be challenging and require deep expertise and insight into specific subject matter. Although LLMs have greatly enhanced the efficiency of quiz generation, concerns remain regarding the quality of these AI-generated quizzes and their educational impact on students. To address these issues, we introduce ConQuer, a concept-based quiz generation framework that leverages external knowledge sources. We employ comprehensive evaluation dimensions to assess the quality of the generated quizzes, using LLMs as judges. Our experiment results demonstrate a 4.8% improvement in evaluation scores and a 77.52% win rate in pairwise comparisons against baseline quiz sets. Ablation studies further underscore the effectiveness of each component in our framework. Code available at https://github.com/sofyc/ConQuer.