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.
\citet{jin2020disease} 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.

本文通过在医学百科全书段落中以疾病名称预测为重点的 Masked Language Modeling 方法，提高了医学多项选择题回答准确率；本文还展示了基于生成的 MCQA 数据集的 fine-tuning 方法胜过基于 MLM 的方法，并且正确遮盖答案线索对于良好表现非常关键。我们发布了新的预训练数据集，并在 4 个 MCQA 数据集上实现了最新最好的结果，在 MedQA-USMLE 上基础型号效果提高了 5.7%。