Resolving duplicates in Large Multiple-Choice Questions Repositories

Multiple-choice questions (MCQs) are commonly used in educational assessments and professional certification examinations. However, managing vast collections of MCQs presents numerous challenges, including maintaining their quality and relevance. A notable issue in such repositories is the occurrence of conceptually identical questions presented in varied forms. These duplicates, while different in wording, fail to enhance the value of the repository. In this extended abstract, we present our approach for identifying and handling potential duplicate questions in large MCQ databases. Our proposed method involves three primary stages: initial pre-processing of MCQs, calculation of similarity based on Natural Language Processing (NLP) techniques, and a graph-based method for exploring these similarities.