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

TL;DR

This paper introduces a new method for generating multiple-choice reading comprehension questions with controllable difficulty, using large language models trained with direct preference optimization to enhance difficulty accuracy.

Contribution

It presents a novel approach that enables direct generation of multiple-choice questions with precise difficulty control, addressing limitations of previous methods.

Findings

Improved difficulty controllability in question generation.

Effective use of large language models with direct preference optimization.

Enhanced accuracy in generating appropriately challenging questions.

Abstract

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.