# A BERT-based Distractor Generation Scheme with Multi-tasking and Negative Answer Training Strategies

arXiv: https://arxiv.org/abs/2010.05384 · HTML: https://mlsanity.com/paper/2010.05384/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2020.

## Built on by (most cited first, 22 of 22)

- [On the application of Large Language Models for language teaching and assessment technology](https://mlsanity.com/paper/2307.08393/) (2023)
- [Distractor Generation in Multiple-Choice Tasks: A Survey of Methods, Datasets, and Evaluation](https://mlsanity.com/paper/2402.01512/) (2024)
- [Distractor generation for multiple-choice questions with predictive prompting and large language models](https://mlsanity.com/paper/2307.16338/) (2023)
- [A Survey of Natural Language Generation](https://mlsanity.com/paper/2112.11739/) (2021)
- [BERT-based distractor generation for Swedish reading comprehension questions using a small-scale dataset](https://mlsanity.com/paper/2108.03973/) (2021)
- [Leaf: Multiple-Choice Question Generation](https://mlsanity.com/paper/2201.09012/) (2022)
- [Evaluating the Knowledge Dependency of Questions](https://mlsanity.com/paper/2211.11902/) (2022)
- [Learning to Reuse Distractors to support Multiple Choice Question Generation in Education](https://mlsanity.com/paper/2210.13964/) (2022)
- [LookAlike: Consistent Distractor Generation in Math MCQs](https://mlsanity.com/paper/2505.01903/) (2025)
- [DisGeM: Distractor Generation for Multiple Choice Questions with Span Masking](https://mlsanity.com/paper/2409.18263/) (2024)
- [Mitigating Easy Option Bias in Multiple-Choice Question Answering](https://mlsanity.com/paper/2508.13428/) (2025)
- [Generating multiple-choice questions for medical question answering with distractors and cue-masking](https://mlsanity.com/paper/2303.07069/) (2023)
- [Survey of Natural Language Processing for Education: Taxonomy, Systematic Review, and Future Trends](https://mlsanity.com/paper/2401.07518/) (2024)
- [DISTO: Evaluating Textual Distractors for Multi-Choice Questions using Negative Sampling based Approach](https://mlsanity.com/paper/2304.04881/) (2023)
- [Controlling Cloze-test Question Item Difficulty with PLM-based Surrogate Models for IRT Assessment](https://mlsanity.com/paper/2403.01456/) (2024)
- [Auditing MCQA Benchmarks through Probability Landscapes](https://mlsanity.com/paper/2608.30372/) (2026)
- [An AI-based Solution for Enhancing Delivery of Digital Learning for Future Teachers](https://mlsanity.com/paper/2112.01229/) (2021)
- [EMBRACE: Evaluation and Modifications for Boosting RACE](https://mlsanity.com/paper/2305.08433/) (2023)
- [DGRC: An Effective Fine-tuning Framework for Distractor Generation in Chinese Multi-choice Reading Comprehension](https://mlsanity.com/paper/2405.19139/) (2024)
- [DualReward: A Dynamic Reinforcement Learning Framework for Cloze Tests Distractor Generation](https://mlsanity.com/paper/2507.11875/) (2025)
- [FarsiMCQGen: a Persian Multiple-choice Question Generation Framework](https://mlsanity.com/paper/2510.15134/) (2025)
- [Corpus Prevalence of Multiple-Choice Question Options](https://mlsanity.com/paper/2602.17377/) (2026)

## Abstract (arXiv)

In this paper, we investigate the following two limitations for the existing distractor generation (DG) methods. First, the quality of the existing DG methods are still far from practical use. There is still room for DG quality improvement. Second, the existing DG designs are mainly for single distractor generation. However, for practical MCQ preparation, multiple distractors are desired. Aiming at these goals, in this paper, we present a new distractor generation scheme with multi-tasking and negative answer training strategies for effectively generating textitmultiple distractors. The experimental results show that (1) our model advances the state-of-the-art result from 28.65 to 39.81 (BLEU 1 score) and (2) the generated multiple distractors are diverse and show strong distracting power for multiple choice question.