Distractor Generation

Distractor Generation (DG) is a subtask in the field of Natural Language Processing that aims to generate incorrect options related to the given context, question, and correct answer to construct multiple-choice questions. This task enhances the difficulty and discriminability of questions by simulating human cognitive biases, and it is widely used in educational assessment and online testing systems, helping to more accurately measure the test-takers' mastery of knowledge.