mlsanityData to 14 Oct 2026

Enhancing Distractor Generation for Multiple-Choice Questions with Retrieval Augmented Pretraining and Knowledge Graph Integration

arXiv 2406.13578 ↗First posted 19 Jun 2024cs.CLcited by 11

On the map: Retrieval-augmented generation → RAG robustness and adaptive retrieval

Abstract (arXiv)

In this paper, we tackle the task of distractor generation (DG) for multiple-choice questions. Our study introduces two key designs. First, we propose textitretrieval augmented pretraining, which involves refining the language model pretraining to align it more closely with the downstream task of DG. Second, we explore the integration of knowledge graphs to enhance the performance of DG. Through experiments with benchmarking datasets, we show that our models significantly outperform the state-of-the-art results. Our best-performing model advances the F1@3 score from 14.80 to 16.47 in MCQ dataset and from 15.92 to 16.50 in Sciq dataset.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation. Builds on 14 of its 23 references that are in the map. First posted on arXiv 19 Jun 2024.

Builds on

14 of 23 references.

References · 14

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
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  7. 2016ConceptNet 5.5: An Open Multilingual Graph of General Knowledgecited by 1,067
  8. 2017Crowdsourcing Multiple Choice Science Questionscited by 765
  9. 2021QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answeringcited by 388
  10. 2020Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddingscited by 171
  11. 2022GreaseLM: Graph REASoning Enhanced Language Models for Question Answeringcited by 171
  12. 2022Deep Bidirectional Language-Knowledge Graph Pretrainingcited by 156
2 more
  1. 2020Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answeringcited by 122
  2. 2020Knowledge-Driven Distractor Generation for Cloze-style Multiple Choice Questionscited by 24

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 9; 2026: 1.

11 papers.

  1. 2025Answer Matching Outperforms Multiple Choice for Language Model Evaluationcited by 41
  2. 2025Automated Generation of Challenging Multiple-Choice Questions for Vision Language Model Evaluationcited by 30
  3. 2025Generating Plausible Distractors for Multiple-Choice Questions via Student Choice Predictioncited by 8
  4. 2025LookAlike: Consistent Distractor Generation in Math MCQscited by 7
  5. 2025Wrong Answers Can Also Be Useful: PlausibleQA -- A Large-Scale QA Dataset with Answer Plausibility Scorescited by 5
  6. 2024LLM Distillation for Efficient Few-Shot Multiple Choice Question Answeringcited by 4
  7. 2025AutoDrive-QA: A Multiple-Choice Benchmark for Vision-Language Evaluation in Urban Autonomous Drivingcited by 2
  8. 2026Cognitively Diverse Multiple-Choice Question Generation: A Hybrid Multi-Agent Framework with Large Language Modelscited by 2
  9. 2025D-GEN: Automatic Distractor Generation and Evaluation for Reliable Assessment of Generative Modelcited by 1
  10. 2025Beyond Multiple Choice: Verifiable OpenQA for Robust Vision-Language RFTcited by 1
  11. 2025DualReward: A Dynamic Reinforcement Learning Framework for Cloze Tests Distractor Generationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Beyond Fine-Tuning: In-Context Learning and Chain-of-Thought for Reasoned Distractor Generation
  2. 2024DGRC: An Effective Fine-tuning Framework for Distractor Generation in Chinese Multi-choice Reading Comprehension
  3. 2026KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration
  4. 2025ARK: Answer-Centric Retriever Tuning via KG-augmented Curriculum Learning
  5. 2025Beyond the Textual: Generating Coherent Visual Options for MCQs
  6. 2025Beyond RAG vs. Long-Context: Learning Distraction-Aware Retrieval for Efficient Knowledge Grounding
  7. 2025Tailoring Diagnostic Modeling to Individual Learners: Personalized Distractor Generation via MCTS-Guided Reasoning Reconstruction
  8. 2026StratRAG: A Multi-Hop Retrieval Evaluation Dataset for Retrieval-Augmented Generation Systems