Update README.md

README.md

@@ -193,19 +193,17 @@ We translate the BioASQ-6B English Question Answering dataset to generate parall
 We translate the `body`, `snippets`, `ideal_answer` and `exact_answer` fields. We have validated the quality of the `ideal_answer` field, however, the `exact_answer` field can contain translation artifacts, as NLLB200 often produces low-quality translations of single-word sentences. 
 </p>
 
-  - 📖 Paper: [Medical mT5: An Open-Source Multilingual Text-to-Text LLM for The Medical Domain](https://arxiv.org/abs/2404.07613)
+  - 📖 Paper: [Medical mT5: An Open-Source Multilingual Text-to-Text LLM for The Medical Domain. In LREC-COLING 2024](https://arxiv.org/abs/2404.07613)
   - 🌐 Project Website: [https://univ-cotedazur.eu/antidote](https://univ-cotedazur.eu/antidote)
   - Original Dataset: [http://bioasq.org/participate/challenges_year_6](http://bioasq.org/participate/challenges_year_6)
   - Funding: CHIST-ERA XAI 2019 call. Antidote (PCI2020-120717-2) funded by MCIN/AEI /10.13039/501100011033 and by European Union NextGenerationEU/PRTR
 
 ## Citation
 ```bibtext
-@misc{garcíaferrero2024medical,
+@proceedings{garcíaferrero2024medical,
       title={Medical mT5: An Open-Source Multilingual Text-to-Text LLM for The Medical Domain}, 
       author={Iker García-Ferrero and Rodrigo Agerri and Aitziber Atutxa Salazar and Elena Cabrio and Iker de la Iglesia and Alberto Lavelli and Bernardo Magnini and Benjamin Molinet and Johana Ramirez-Romero and German Rigau and Jose Maria Villa-Gonzalez and Serena Villata and Andrea Zaninello},
       year={2024},
-      eprint={2404.07613},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
+      booktitle={Proceedings of LREC-COLING}
 }
 ```