DisGeM: Distractor Generation for Multiple Choice Questions with Span Masking
<|reference_start|>DisGeM: Distractor Generation for Multiple Choice Questions with Span Masking: Recent advancements in Natural Language Processing (NLP) have impacted numerous sub-fields such as natural language generation, natural language inference, question answering, and more. However, in the field of question generation, the creation of distractors for multiple-choice questions (MCQ) remains a challenging task. In this work, we present a simple, generic framework for distractor generation using readily available Pre-trained Language Models (PLMs). Unlike previous methods, our framework relies solely on pre-trained language models and does not require additional training on specific datasets. Building upon previous research, we introduce a two-stage framework consisting of candidate generation and candidate selection. Our proposed distractor generation framework outperforms previous methods without the need for training or fine-tuning. Human evaluations confirm that our approach produces more effective and engaging distractors. The related codebase is publicly available at https://github.com/obss/disgem.<|reference_end|>
@article{cavusoglu2024disgem:,
    title={DisGeM: Distractor Generation for Multiple Choice Questions with Span
  Masking},
    author={Devrim Cavusoglu, Secil Sen, Ulas Sert},
    journal={arXiv preprint arXiv:2409.18263},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.18263},
    primaryClass={cs.CL cs.LG}
}