mlsanityData to 7 Oct 2026

Difficulty-Controllable Multiple-Choice Question Generation Using Large Language Models and Direct Preference Optimization

arXiv 2510.19265 ↗First posted 22 Oct 2025cs.CLcited by 1

On the map: LLM alignment / RLHF / preference optimisation → Process reward models for reasoning

Abstract (arXiv)

Difficulty-controllable question generation for reading comprehension has gained significant attention in the field of education as a fundamental tool for adaptive learning support. Although several neural question generation methods have recently succeeded in controlling difficulty, conventional approaches still face two major limitations. First, they cannot directly generate multiple-choice questions, which are the most widely used question type in educational contexts. Second, they are not explicitly trained to optimize the accuracy of difficulty control, leaving room for further improvement in difficulty controllability. To address these limitations, this study proposes a novel difficulty-controllable multiple-choice question generation method for reading comprehension which leverages a large language model trained using a direct preference optimization technique to improve the accuracy of difficulty control.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Process reward models for reasoning, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 31 of its 85 references that are in the map. First posted on arXiv 22 Oct 2025.

Builds on

31 of 85 references.

References · 31

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19 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026The Architect, the Adversary, and the Judge: Closed-Loop Generation of Standards-Aligned Assessment Items at Scalecited by 0

Similar, not cited

Close in wording, no citation either way

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  3. 2025Reasoning and Sampling-Augmented MCQ Difficulty Prediction via LLMs
  4. 2024The Future of Learning in the Age of Generative AI: Automated Question Generation and Assessment with Large Language Models
  5. 2022Automatic Generation of Socratic Subquestions for Teaching Math Word Problems
  6. 2024DGRC: An Effective Fine-tuning Framework for Distractor Generation in Chinese Multi-choice Reading Comprehension
  7. 2021Simple or Complex? Complexity-Controllable Question Generation with Soft Templates and Deep Mixture of Experts Model
  8. 2025Generate-Then-Validate: A Novel Question Generation Approach Using Small Language Models