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- license: apache-2.0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ license: cc-by-4.0
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+ language:
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+ - en
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+ - es
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+ - fr
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+ - it
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+ tags:
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+ - casimedicos
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+ - explainability
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+ - medical exams
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+ - medical question answering
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+ - multilinguality
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+ - LLMs
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+ - LLM
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+ pretty_name: MedExpQA
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+ configs:
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+ - config_name: en
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+ data_files:
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+ - split: train
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+ path:
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+ - data/en/dev.en.train.casimedicos.rag.jsonl
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+ - split: validation
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+ path:
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+ - data/en/dev.en.casimedicos.rag.jsonl
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+ - split: test
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+ path:
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+ - data/en/test.en.casimedicos.rag.jsonl
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+ - config_name: es
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+ data_files:
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+ - split: train
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+ path:
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+ - data/es/dev.es.train.casimedicos.rag.jsonl
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+ - split: validation
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+ path:
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+ - data/es/dev.es.casimedicos.rag.jsonl
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+ - split: test
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+ path:
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+ - data/es/test.es.casimedicos.rag.jsonl
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+ - config_name: fr
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+ data_files:
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+ - split: train
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+ path:
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+ - data/fr/dev.fr.train.casimedicos.rag.jsonl
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+ - split: validation
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+ path:
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+ - data/fr/dev.fr.casimedicos.rag.jsonl
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+ - split: test
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+ path:
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+ - data/fr/test.fr.casimedicos.rag.jsonl
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+ - config_name: it
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+ data_files:
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+ - split: train
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+ path:
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+ - data/it/dev.it.train.casimedicos.rag.jsonl
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+ - split: validation
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+ path:
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+ - data/it/dev.it.casimedicos.rag.jsonl
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+ - split: test
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+ path:
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+ - data/it/test.it.casimedicos.rag.jsonl
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+ task_categories:
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+ - text-generation
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+ - question-answering
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+ size_categories:
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+ - 1K<n<10K
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  ---
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+
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+ <p align="center">
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+ <br>
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+ <img src="http://www.ixa.eus/sites/default/files/anitdote.png" style="height: 200px;">
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+ <br>
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+
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+ # MexExpQA: Benchmarking Medical QA with reference gold explanations and Retrieval Augmented Generation Methods (RAG)
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+
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+ We present a new multilingual parallel medical benchmark, MedExpQA, for the evaluation of LLMs on Medical Question Answering.
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+ This dataset can be used for various NLP tasks including: **Medical Question Answering**, **Explanatory Argument Extraction** or **Explanation Generation**.
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+
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+ Although the design of MedExpQA is independent of the specific dataset, for the first version of the MedExpQA benchmark we leverage the commented MIR exams,
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+ from the [Antidote CasiMedicos dataset which includes gold reference explanations](https://huggingface.co/datasets/HiTZ/casimedicos-exp), which is currently
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+ available for 4 languages: **English, French, Italian and Spanish**.
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+
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+ <table style="width:33%">
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+ <tr>
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+ <th>Antidote CasiMedicos splits</th>
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+ <tr>
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+ <td>train</td>
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+ <td>434</td>
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+ </tr>
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+ <tr>
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+ <td>validation</td>
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+ <td>63</td>
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+ </tr>
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+ <tr>
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+ <td>test</td>
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+ <td>125</td>
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+ </tr>
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+ </table>
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+
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+ - 📖 Paper:[MedExpQA: Multilingual Benchmarking of Large Language Models for Medical Question Answering](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4780937)
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+ - 💻 Github Repo (Data and Code): [https://github.com/hitz-zentroa/MedExpQA](https://github.com/hitz-zentroa/MedExpQA)
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+ - 🌐 Project Website: [https://univ-cotedazur.eu/antidote](https://univ-cotedazur.eu/antidote)
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+ - Funding: CHIST-ERA XAI 2019 call. Antidote (PCI2020-120717-2) funded by MCIN/AEI /10.13039/501100011033 and by European Union NextGenerationEU/PRTR
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+
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+
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+ ## Example
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+
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+ <p align="center">
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+ <img src="https://github.com/ixa-ehu/antidote-casimedicos/blob/main/casimedicos-exp.png?raw=true" style="height: 650px;">
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+ </p>
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+
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+ In this repository you can find the following data:
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+
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+ - **casimedicos-raw**: The textual content including Clinical Case (C), Question (Q), Possible Answers (P), and Explanation (E) as shown in the example above.
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+ - **casimedicos-exp**: The manual annotations linking the explanations of the correct and incorrect possible answers.
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+ - **MedExpQA**: benchmark for Medical QA based on gold reference explanations from casimedicos-exp and knowledge automatically extracted using RAG methods.
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+
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+ ## Data Explanation
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+
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+ The following attributes composed **casimedicos-raw**:
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+
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+ - **id**: unique doc identifier.
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+ - **year**: year in which the exam was published by the Spanish Ministry of Health.
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+ - **question_id_specific**: id given to the original exam published by the Spanish Ministry of Health.
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+ - **full_question**: Clinical Case (C) and Question (Q) as illustrated in the example document above.
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+ - **full answer**: Full commented explanation (E) as illustrated in the example document above.
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+ - **type**: medical speciality.
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+ - **options**: Possible Answers (P) as illustrated in the example document above.
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+ - **correct option**: solution to the exam question.
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+
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+ Additionally, the following jsonl attribute was added to create **casimedicos-exp**:
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+
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+ - **explanations**: for each possible answer above, manual annotation states whether:
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+ 1. the explanation for each possible answer exists in the full comment (E) and
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+ 2. if present, then we provide character and token offsets plus the text corresponding to the explanation for each possible answer.
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+
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+ For **MedExpQA** benchmarking we have added the following elements in the data:
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+
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+ - **rag**
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+ 1. **clinical_case_options**: etc.
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+
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+
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+
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+
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+ ## Citation
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+
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+ If you use Antidote CasiMedicos dataset then please **cite the following paper**:
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+
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+ ```bibtex
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+ @inproceedings{Agerri2023HiTZAntidoteAE,
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+ title={HiTZ@Antidote: Argumentation-driven Explainable Artificial Intelligence for Digital Medicine},
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+ author={Rodrigo Agerri and I{\~n}igo Alonso and Aitziber Atutxa and Ander Berrondo and Ainara Estarrona and Iker Garc{\'i}a-Ferrero and Iakes Goenaga and Koldo Gojenola and Maite Oronoz and Igor Perez-Tejedor and German Rigau and Anar Yeginbergenova},
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+ booktitle={SEPLN 2023: 39th International Conference of the Spanish Society for Natural Language Processing.},
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+ year={2023}
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+ }
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+
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+ @misc{goenaga2023explanatory,
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+ title={Explanatory Argument Extraction of Correct Answers in Resident Medical Exams},
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+ author={Iakes Goenaga and Aitziber Atutxa and Koldo Gojenola and Maite Oronoz and Rodrigo Agerri},
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+ year={2023},
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+ eprint={2312.00567},
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+ archivePrefix={arXiv}
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+ }
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+ ```
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+
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+ **Contact**: [Iñigo Alonso](https://hitz.ehu.eus/en/node/282) and [Rodrigo Agerri](https://ragerri.github.io/)
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+ HiTZ Center - Ixa, University of the Basque Country UPV/EHU