Update README.md

README.md

@@ -1,3 +1,167 @@
 ---
-license: apache-2.0
+license: cc-by-4.0
+language:
+- en
+- es
+- fr
+- it
+tags:
+- casimedicos
+- explainability
+- medical exams
+- medical question answering
+- multilinguality
+- LLMs
+- LLM
+pretty_name: MedExpQA
+configs:
+- config_name: en
+  data_files:
+  - split: train
+    path:
+    - data/en/dev.en.train.casimedicos.rag.jsonl
+  - split: validation
+    path:
+    - data/en/dev.en.casimedicos.rag.jsonl
+  - split: test
+    path:
+    - data/en/test.en.casimedicos.rag.jsonl
+- config_name: es
+  data_files:
+  - split: train
+    path:
+    - data/es/dev.es.train.casimedicos.rag.jsonl
+  - split: validation
+    path:
+    - data/es/dev.es.casimedicos.rag.jsonl
+  - split: test
+    path:
+    - data/es/test.es.casimedicos.rag.jsonl
+- config_name: fr
+  data_files:
+  - split: train
+    path:
+    - data/fr/dev.fr.train.casimedicos.rag.jsonl
+  - split: validation
+    path:
+    - data/fr/dev.fr.casimedicos.rag.jsonl
+  - split: test
+    path:
+    - data/fr/test.fr.casimedicos.rag.jsonl
+- config_name: it
+  data_files:
+  - split: train
+    path:
+    - data/it/dev.it.train.casimedicos.rag.jsonl
+  - split: validation
+    path:
+    - data/it/dev.it.casimedicos.rag.jsonl
+  - split: test
+    path:
+    - data/it/test.it.casimedicos.rag.jsonl
+task_categories:
+- text-generation
+- question-answering
+size_categories:
+- 1K<n<10K
 ---
+
+<p align="center">
+    <br>
+    <img src="http://www.ixa.eus/sites/default/files/anitdote.png" style="height: 200px;">
+    <br>
+
+# MexExpQA: Benchmarking Medical QA with reference gold explanations and Retrieval Augmented Generation Methods (RAG)
+
+We present a new multilingual parallel medical benchmark, MedExpQA, for the evaluation of LLMs on Medical Question Answering.
+This dataset can be used for various NLP tasks including: **Medical Question Answering**, **Explanatory Argument Extraction** or **Explanation Generation**.
+
+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, 
+from the [Antidote CasiMedicos dataset which includes gold reference explanations](https://huggingface.co/datasets/HiTZ/casimedicos-exp), which is currently 
+available for 4 languages: **English, French, Italian and Spanish**.
+
+<table style="width:33%">
+    <tr>
+         <th>Antidote CasiMedicos splits</th>
+     <tr>
+         <td>train</td>
+         <td>434</td>
+     </tr>
+     <tr>
+         <td>validation</td>
+         <td>63</td>
+     </tr>
+     <tr>
+         <td>test</td>
+         <td>125</td>
+     </tr>
+ </table>
+
+- 📖 Paper:[MedExpQA: Multilingual Benchmarking of Large Language Models for Medical Question Answering](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4780937)
+- 💻 Github Repo (Data and Code): [https://github.com/hitz-zentroa/MedExpQA](https://github.com/hitz-zentroa/MedExpQA)
+- 🌐 Project Website: [https://univ-cotedazur.eu/antidote](https://univ-cotedazur.eu/antidote)
+- Funding: CHIST-ERA XAI 2019 call. Antidote (PCI2020-120717-2) funded by MCIN/AEI /10.13039/501100011033 and by European Union NextGenerationEU/PRTR
+
+
+## Example
+
+<p align="center">
+<img src="https://github.com/ixa-ehu/antidote-casimedicos/blob/main/casimedicos-exp.png?raw=true" style="height: 650px;">
+</p>
+
+In this repository you can find the following data:
+
+- **casimedicos-raw**: The textual content including Clinical Case (C), Question (Q), Possible Answers (P), and Explanation (E) as shown in the example above.
+- **casimedicos-exp**: The manual annotations linking the explanations of the correct and incorrect possible answers.
+- **MedExpQA**: benchmark for Medical QA based on gold reference explanations from casimedicos-exp and knowledge automatically extracted using RAG methods.
+
+## Data Explanation
+
+The following attributes composed **casimedicos-raw**:
+
+- **id**: unique doc identifier.
+- **year**: year in which the exam was published by the Spanish Ministry of Health.
+- **question_id_specific**: id given to the original exam published by the Spanish Ministry of Health.
+- **full_question**: Clinical Case (C) and Question (Q) as illustrated in the example document above.
+- **full answer**: Full commented explanation (E) as illustrated in the example document above.
+- **type**: medical speciality.
+- **options**: Possible Answers (P) as illustrated in the example document above.
+- **correct option**: solution to the exam question.
+
+Additionally, the following jsonl attribute was added to create **casimedicos-exp**:
+
+- **explanations**: for each possible answer above, manual annotation states whether:
+  1. the explanation for each possible answer exists in the full comment (E) and
+  2. if present, then we provide character and token offsets plus the text corresponding to the explanation for each possible answer.
+
+For **MedExpQA** benchmarking we have added the following elements in the data:
+
+- **rag**
+  1. **clinical_case_options**: etc.
+
+ 
+
+
+## Citation
+
+If you use Antidote CasiMedicos dataset then please **cite the following paper**:
+
+```bibtex
+@inproceedings{Agerri2023HiTZAntidoteAE,
+  title={HiTZ@Antidote: Argumentation-driven Explainable Artificial Intelligence for Digital Medicine},
+  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},
+  booktitle={SEPLN 2023: 39th International Conference of the Spanish Society for Natural Language Processing.},
+  year={2023}
+}
+
+@misc{goenaga2023explanatory,
+      title={Explanatory Argument Extraction of Correct Answers in Resident Medical Exams}, 
+      author={Iakes Goenaga and Aitziber Atutxa and Koldo Gojenola and Maite Oronoz and Rodrigo Agerri},
+      year={2023},
+      eprint={2312.00567},
+      archivePrefix={arXiv}
+}
+```
+
+**Contact**: [Iñigo Alonso](https://hitz.ehu.eus/en/node/282) and [Rodrigo Agerri](https://ragerri.github.io/)
+HiTZ Center - Ixa, University of the Basque Country UPV/EHU