| Challenge: | MedQA-SWE is a clinical question & answering dataset in Swedish . it was created from exams aimed at evaluating doctors’ clinical understanding and decision making . |
| Approach: | They propose to create a multiple choice, clinical question & answering (Q&A) dataset in Swedish consisting of 3,180 questions. |
| Outcome: | The proposed dataset includes 3,180 questions and is the first open-source clinical Q&A dataset in Swedish. |
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João Matos, Shan Chen, Siena Kathleen V. Placino, Yingya Li, Juan Carlos Climent Pardo, Daphna Idan, Takeshi Tohyama, David Restrepo, Luis Filipe Nakayama, José María Millet Pascual-Leone, Guergana K Savova, Hugo Aerts, Leo Anthony Celi, An-Kwok Ian Wong, Danielle Bitterman, Jack Gallifant
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What Does Infect Mean to Cardio? Investigating the Role of Clinical Specialty Data in Medical LLMs (2026.eacl-long)
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| Challenge: | S-MedQA is an English question-answering dataset designed for benchmarking large language models in fine-grained clinical specialties. |
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PolQA: Polish Question Answering Dataset (2024.lrec-main)
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| Challenge: | Recent proposed systems for open-domain question answering (OpenQA) require large amounts of training data to achieve state-of-the-art performance. |
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| Challenge: | Existing abstractive question-answering datasets in Vietnamese are lacking . |
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XQA: A Cross-lingual Open-domain Question Answering Dataset (P19-1)
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| Challenge: | Open-domain question answering aims to answer questions through text retrieval and reading comprehension . but, the success of these models relies on a massive volume of training data, which is not available in other languages . a new dataset aims at investigating cross-lingual OpenQA . |
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AfriMed-QA: A Pan-African, Multi-Specialty, Medical Question-Answering Benchmark Dataset (2025.acl-long)
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Charles Nimo, Tobi Olatunji, Abraham Toluwase Owodunni, Tassallah Abdullahi, Emmanuel Ayodele, Mardhiyah Sanni, Ezinwanne C. Aka, Folafunmi Omofoye, Foutse Yuehgoh, Timothy Faniran, Bonaventure F. P. Dossou, Moshood O. Yekini, Jonas Kemp, Katherine A Heller, Jude Chidubem Omeke, Chidi Asuzu Md, Naome A Etori, Aïmérou Ndiaye, Ifeoma Okoh, Evans Doe Ocansey, Wendy Kinara, Michael L. Best, Irfan Essa, Stephen Edward Moore, Chris Fourie, Mercy Nyamewaa Asiedu
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MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering (2021.tacl-1)
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| Challenge: | Existing multilingual QA datasets lack linguistic diversity and comparable evaluation between languages. |
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emrQA: A Large Corpus for Question Answering on Electronic Medical Records (D18-1)
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| Challenge: | Existing annotations for other NLP tasks are used to generate domain-specific large-scale question answering (QA) datasets. |
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Multi-modal Concept Alignment Pre-training for Generative Medical Visual Question Answering (2024.findings-acl)
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| Challenge: | Medical Visual Question Answering (Med-VQA) aims to provide accurate answers to questions regarding medical images, a task particularly challenging for open-ended questions. |
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MedREQAL: Examining Medical Knowledge Recall of Large Language Models via Question Answering (2024.findings-acl)
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| Challenge: | Large language models can encode knowledge during pre-training on large text corpora, enabling downstream tasks like question answering (QA). |
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