Update README.md

README.md

@@ -46,7 +46,7 @@ Plume is the first LLM trained for Neural Machine Translation with only parallel
 
 In recent years, Large Language Models (LLMs) have demonstrated exceptional proficiency across a broad spectrum of Natural Language Processing (NLP) tasks, including Machine Translation. However, previous methodologies predominantly relied on iterative processes such as instruction fine-tuning or continual pre-training, leaving unexplored the challenges of training LLMs solely on parallel data. In this work, we introduce Plume (**P**arallel **L**ang**u**age **M**od**e**l), a collection of three 2B LLMs featuring varying vocabulary sizes (32k, 128k, and 256k) trained exclusively on  Catalan-centric parallel examples. These models perform comparable to previous encoder-decoder architectures on 16 supervised translation directions and 56 zero-shot ones.
 
-For more details regarding the model architecture, the dataset and model interpretability take a look at the paper which is available on [arXiv]().
+For more details regarding the model architecture, the dataset and model interpretability take a look at the paper which is available on [arXiv](https://arxiv.org/abs/2406.09140).
 
 ## Intended Uses and Limitations
 
@@ -114,7 +114,13 @@ Below are the evaluation results on Flores-200 and NTREX for supervised MT direc
 ## Citation
 
 ```bibtex
-
+@misc{gilabert2024investigating,
+      title={Investigating the translation capabilities of Large Language Models trained on parallel data only}, 
+      author={Javier García Gilabert and Carlos Escolano and Aleix Sant Savall and Francesca De Luca Fornaciari and Audrey Mash and Xixian Liao and Maite Melero},
+      year={2024},
+      eprint={2406.09140},
+      archivePrefix={arXiv}
+}
 ```
 
 ## Additional information