Update README.md

README.md

@@ -29,8 +29,14 @@ widget:
     output:
       text: Frades é un concello da provincia da Coruña, pertencente á comarca de Ordes. Está situado a 15 quilómetros de Santiago de Compostela.
 ---
+<div align="center">
+    <p align="center"><img width=20% src="https://gitlab.irlab.org/eliseo.bao/xovetic-llms-underrepresented-languages/-/raw/main/img/logo.png" /></p>
+</div>
 
-# Llama-3.1-8B-Instruct-Galician
+
+
+
+# Llama-3.1-8B-Instruct-Galician a.k.a. Cabuxa 2.0
 
 This model is a continued pretraining version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on the [CorpusNós](https://zenodo.org/records/11655219) dataset.
 
@@ -108,4 +114,13 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 ## Citation
 
-_Coming soon_
+```
+@inproceedings{bao-perez-parapar-xovetic-2024,
+  title={Adapting Large Language Models for Underrepresented Languages},
+  author={Eliseo Bao and Anxo Pérez and Javier Parapar	},
+  booktitle={VII Congreso XoveTIC: impulsando el talento cient{\'\i}fico},
+  year={2024},
+  organization={Universidade da Coru{\~n}a, Servizo de Publicaci{\'o}ns}
+  abstact = {The popularization of Large Language Models (LLMs), especially with the development of conversational systems, makes mandatory to think about facilitating the use of artificial intelligence (AI) to everyone. Most models neglect minority languages, prioritizing widely spoken ones. This exacerbates their underrepresentation in the digital world and negatively affects their speakers. We present two resources aimed at improving natural language processing (NLP) for Galician: (i) a Llama 3.1 instruct model adapted through continuous pre-training on the CorpusNos dataset; and (ii) a Galician version of the Alpaca dataset, used to assess the improvement over the base model. In this evaluation, our model outperformed both the base model and another Galician model in quantitative and qualitative terms}
+}
+```