# From Answers to Questions: EQGBench for Evaluating LLMs' Educational Question Generation

arXiv: https://arxiv.org/abs/2508.10005 · HTML: https://mlsanity.com/paper/2508.10005/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 34 references that are in the map.
- First posted on arXiv 5 Aug 2025.

## Builds on (most cited first, 12 of 12)

- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models](https://mlsanity.com/paper/2305.08322/) (2023)
- [Unified Language Model Pre-training for Natural Language Understanding and Generation](https://mlsanity.com/paper/1905.03197/) (2019)
- [Evaluating the Performance of Large Language Models on GAOKAO Benchmark](https://mlsanity.com/paper/2305.12474/) (2023)
- [Neural Question Generation from Text: A Preliminary Study](https://mlsanity.com/paper/1704.01792/) (2017)
- [Harvesting Paragraph-Level Question-Answer Pairs from Wikipedia](https://mlsanity.com/paper/1805.05942/) (2018)
- [LLMs can Find Mathematical Reasoning Mistakes by Pedagogical Chain-of-Thought](https://mlsanity.com/paper/2405.06705/) (2024)
- [Mixture Content Selection for Diverse Sequence Generation](https://mlsanity.com/paper/1909.01953/) (2019)
- [EduBench: A Comprehensive Benchmarking Dataset for Evaluating Large Language Models in Diverse Educational Scenarios](https://mlsanity.com/paper/2505.16160/) (2025)
- [MATHWELL: Generating Educational Math Word Problems Using Teacher Annotations](https://mlsanity.com/paper/2402.15861/) (2024)
- [Automatic Answerability Evaluation for Question Generation](https://mlsanity.com/paper/2309.12546/) (2023)
- [Can Language Models Evaluate Human Written Text? Case Study on Korean Student Writing for Education](https://mlsanity.com/paper/2407.17022/) (2024)

## Built on by (most cited first, 1 of 1)

- [MAGMA-Edu: Multi-Agent Generative Multimodal Framework for Text-Diagram Educational Question Generation](https://mlsanity.com/paper/2511.18714/) (2025)

## Abstract (arXiv)

Large Language Models (LLMs) have demonstrated remarkable capabilities in mathematical problem-solving. However, the transition from providing answers to generating high-quality educational questions presents significant challenges that remain underexplored. To advance Educational Question Generation (EQG) and facilitate LLMs in generating pedagogically valuable and educationally effective questions, we introduce EQGBench, a comprehensive benchmark specifically designed for evaluating LLMs' performance in Chinese EQG. EQGBench establishes a five-dimensional evaluation framework supported by a dataset of 900 evaluation samples spanning three fundamental middle school disciplines: mathematics, physics, and chemistry. The dataset incorporates user queries with varying knowledge points, difficulty gradients, and question type specifications to simulate realistic educational scenarios. Through systematic evaluation of 46 mainstream large models, we reveal significant room for development in generating questions that reflect educational value and foster students' comprehensive abilities.