# Question Difficulty Ranking for Multiple-Choice Reading Comprehension

arXiv: https://arxiv.org/abs/2404.10704 · HTML: https://mlsanity.com/paper/2404.10704/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 28 references that are in the map.
- First posted on arXiv 16 Apr 2024.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [RACE: Large-scale ReAding Comprehension Dataset From Examinations](https://mlsanity.com/paper/1704.04683/) (2017)
- [A Survey on Machine Reading Comprehension Systems](https://mlsanity.com/paper/2001.01582/) (2020)
- [Multiple-Choice Question Generation: Towards an Automated Assessment Framework](https://mlsanity.com/paper/2209.11830/) (2022)
- [Analysis of the Cambridge Multiple-Choice Questions Reading Dataset with a Focus on Candidate Response Distribution](https://mlsanity.com/paper/2306.13047/) (2023)
- [An Information-Theoretic Approach to Analyze NLP Classification Tasks](https://mlsanity.com/paper/2402.00978/) (2024)

## Built on by (most cited first, 7 of 7)

- [Efficient LLM Comparative Assessment: a Product of Experts Framework for Pairwise Comparisons](https://mlsanity.com/paper/2405.05894/) (2024)
- [Are You Doubtful? Oh, It Might Be Difficult Then! Exploring the Use of Model Uncertainty for Question Difficulty Estimation](https://mlsanity.com/paper/2412.11831/) (2024)
- [Can Model Uncertainty Function as a Proxy for Multiple-Choice Question Item Difficulty?](https://mlsanity.com/paper/2407.05327/) (2024)
- [Can LLMs Estimate Cognitive Complexity of Reading Comprehension Items?](https://mlsanity.com/paper/2510.25064/) (2025)
- [Finetuning LLMs for Comparative Assessment Tasks](https://mlsanity.com/paper/2409.15979/) (2024)
- [Question Difficulty Estimation for Large Language Models via Answer Plausibility Scoring](https://mlsanity.com/paper/2605.12398/) (2026)
- [A Multi-Agent Framework for Feature-Constrained Difficulty Control in Reading Comprehension Item Generation](https://mlsanity.com/paper/2605.19316/) (2026)

## Abstract (arXiv)

Multiple-choice (MC) tests are an efficient method to assess English learners. It is useful for test creators to rank candidate MC questions by difficulty during exam curation. Typically, the difficulty is determined by having human test takers trial the questions in a pretesting stage. However, this is expensive and not scalable. Therefore, we explore automated approaches to rank MC questions by difficulty. However, there is limited data for explicit training of a system for difficulty scores. Hence, we compare task transfer and zero-shot approaches: task transfer adapts level classification and reading comprehension systems for difficulty ranking while zero-shot prompting of instruction finetuned language models contrasts absolute assessment against comparative. It is found that level classification transfers better than reading comprehension. Additionally, zero-shot comparative assessment is more effective at difficulty ranking than the absolute assessment and even the task transfer approaches at question difficulty ranking with a Spearman's correlation of 40.4%. Combining the systems is observed to further boost the correlation.