Update README.md

README.md

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+# Model Card for Raidium ECN-QA
+
+
+The dataset is introduced in the paper "Efficient Medical Question Answering with Knowledge-Augmented Question Generation".
+
+Paper: [https://arxiv.org/abs/2405.14654](https://arxiv.org/abs/2405.14654)
+
+## Dataset Details
+
+In the expanding field of language model applications, medical knowledge representation remains a significant challenge due to the specialized nature of the domain. 
+Large language models, such as GPT-4, obtain reasonable scores on medical question-answering tasks, but smaller models are far behind. 
+In this work, we introduce a method to improve the proficiency of a small language model in the medical domain by employing a two-fold approach. 
+We first fine-tune the model on a corpus of medical textbooks. Then, we use GPT-4 to generate questions similar to the downstream task, prompted with textbook knowledge, and use them to fine-tune the model. 
+We show the benefits of our training strategy on a medical answering question dataset.
+The study's findings highlight the potential of small language models in the medical domain when appropriately fine-tuned. 
+
+### Dataset Description
+
+The dataset contains medical questions of different types. It was built from passed ECN exams (french medical examination) and questions created by [FreeCN](https://www.freecn.io/).
+The questions can be:
+- IQ (individual question) containing a question and several propositions that can be right or wrong
+- Custom which are IQ created by FreeCN
+- PQ (progressive questions) containing a case with an introduction and several following questions with multiple propositions
+
+
+- **Developed by:** Raidium
+- **License:** Apache 2.0
+
+### Dataset Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [https://github.com/raidium-med/MQG]
+- **Paper:**  [https://arxiv.org/abs/2405.14654](https://arxiv.org/abs/2405.14654)
+
+
+## Citation
+
+
+**BibTeX:**
+```
+@article{khlaut2024efficient,
+  title={Efficient Medical Question Answering with Knowledge-Augmented Question Generation},
+  author={Khlaut, Julien and Dancette, Corentin and Ferreres, Elodie and Bennani, Alaedine and H{\'e}rent, Paul and Manceron, Pierre},
+  journal={Clinical NLP Workshop, NAACL 2024},
+  year={2024}
+}
+```
+
+## Dataset Card Contact
+
+julien.khlaut at raidium.fr