C ross QG : Improving Difficulty-Controllable Question Generation through Consistency Enhancement (2025)

Abstract: Automatically generating questions with controlled difficulty has great application value, especially in the field of education. Although large language models are capable of generating questions of various difficulty levels, the generated questions often fail to align with the given target difficulty. To mitigate this issue, we propose CrossQG, a novel question generation method that requires no tuning of generator parameters, yet significantly improves difficulty consistency. Specifically, CrossQG consists of two steps: (1) contrast enhancement, which leverages questions from different difficulty levels to enhance the base models’ understanding of the target difficulty, and (2) cross filtering, which compares generated questions across different difficulty levels and filters out those that do not meet the target difficulty. We evaluate CrossQG on three high-quality question answering datasets. Experimental results demonstrate that across multiple models, CrossQG significantly outperforms several mainstream methods, achieving superior consistency with target difficulty and improving question quality. Notably, without generator training, CrossQG surpasses supervised fine-tuning in various instances.

## introduction the task of difficulty-controllable question generation (dcqg) aims to generate questions with controlled difficulty levels. it holds significant application value in education, such as improving learning efficiency @xcite and better assessing learners' abilities @xcite . the main difficulties of dcqg lie in: (1) appropriately categorizing and representing the difficulty levels of the questions, and (2) ensuring that the generated questions match the target difficulty. ## method given an input context text c and a specific difficulty level @xmath0, the objective of the task is to generate several question-answer (qa) pairs (q, a), where questions in q align with the difficulty level d. this process can be formalized as the following function: ## conclusion in this paper, we propose crossqg, a novel dcqg method that requires no generator tuning, aimed at optimizing the difficulty consistency of generated questions. crossqg leverages information from various difficulty levels, which is often overlooked in previous research, to assist in generating questions at the target difficulty. it first employs contrast enhancement to select questions from different difficulty levels as negative examples. then, it utilizes cross filtering to eliminate questions that exhibit high difficulty similarity with those targeted at other levels. we conduct experiments on three datasets. results across multiple llms demonstrate that crossqg achieves superior difficulty consistency and quality compared to three trainingfree baselines. in addition, it surpasses sft in many instances. future research will explore methods to enhance the robustness of crossqg against noisy data.