mlsanityData to 7 Oct 2026

Generating multiple-choice questions for medical question answering with distractors and cue-masking

arXiv 2303.07069 ↗First posted 13 Mar 2023cs.CLcited by 3

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Abstract (arXiv)

Medical multiple-choice question answering (MCQA) is particularly difficult. Questions may describe patient symptoms and ask for the correct diagnosis, which requires domain knowledge and complex reasoning. Standard language modeling pretraining alone is not sufficient to achieve the best results. citetjin2020disease showed that focusing masked language modeling on disease name prediction when using medical encyclopedic paragraphs as input leads to considerable MCQA accuracy improvement. In this work, we show that (1) fine-tuning on generated MCQA dataset outperforms the masked language modeling based objective and (2) correctly masking the cues to the answers is critical for good performance. We release new pretraining datasets and achieve state-of-the-art results on 4 MCQA datasets, notably +5.7% with base-size model on MedQA-USMLE.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 22 of its 36 references that are in the map. First posted on arXiv 13 Mar 2023.

Builds on

22 of 36 references.

References · 22

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10 more
  1. 2018Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data Taskscited by 214
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 2.

3 papers.

  1. 2024Distractor Generation in Multiple-Choice Tasks: A Survey of Methods, Datasets, and Evaluationcited by 25
  2. 2026EHRBench: An Automated and Reliable EHR-based Benchmark for Clinical Decision Making with LLMscited by 2
  3. 2026Dense Clinical Contrasts Enhance Medical Knowledge Updating in Large Language Modelscited by 0