Papers with MedQA-USMLE
Hierarchical Representation-based Dynamic Reasoning Network for Biomedical Question Answering (2022.coling-1)
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Jianguo Mao, Jiyuan Zhang, Zengfeng Zeng, Weihua Peng, Wenbin Jiang, Xiangdong Wang, Hong Liu, Yajuan Lyu
| Challenge: | Existing models of biomedical question answering are limited in their ability to predict answers . a new model improves the performance of existing models, but the code will be released after the paper is published. |
| Approach: | They propose a hierarchical representation-based dynamic reasoning network to solve biomedical problems. |
| Outcome: | The proposed model significantly improves on three mainstream biomedical datasets . the code will be released after the paper is published . |
To Generate or to Retrieve? On the Effectiveness of Artificial Contexts for Medical Open-Domain Question Answering (2024.acl-long)
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| Challenge: | Medical open-domain question answering requires substantial access to specialized knowledge. |
| Approach: | They propose a framework that generates multiple-choice questions from a set of open-book parameters and a small-scale reader that can outcompete closed-book questions by 706x using fewer parameters. |
| Outcome: | The proposed framework outperforms closed-book models on MedQA-USMLE, MedMCQA, and MMLU while using up to 706x fewer parameters. |
Generating Multiple-choice Questions for Medical Question Answering with Distractors and Cue-masking (2024.lrec-main)
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| Challenge: | Medical multiple-choice question answering (MCQA) requires high accuracy to be useful in practice. |
| Approach: | They propose to focus masked language modeling on disease name prediction when using medical encyclopedic paragraphs as input. |
| Outcome: | The proposed model outperforms the masked language model on disease name prediction and masks the cues to the answers. |