Update arXiv link & citation

README.md

@@ -25,7 +25,7 @@ library_name: transformers
 
 We introduce EXAONE Deep, which exhibits superior capabilities in various reasoning tasks including math and coding benchmarks, ranging from 2.4B to 32B parameters developed and released by LG AI Research. Evaluation results show that 1) EXAONE Deep **2.4B** outperforms other models of comparable size, 2) EXAONE Deep **7.8B** outperforms not only open-weight models of comparable scale but also a proprietary reasoning model OpenAI o1-mini, and 3) EXAONE Deep **32B** demonstrates competitive performance against leading open-weight models.
 
-For more details, please refer to our [documentation](https://lgresearch.ai/data/upload/EXAONE_Deep__Model_Card.pdf), [blog](https://www.lgresearch.ai/news/view?seq=543) and [GitHub](https://github.com/LG-AI-EXAONE/EXAONE-Deep).
+For more details, please refer to our [documentation](https://arxiv.org/abs/2503.12524), [blog](https://www.lgresearch.ai/news/view?seq=543) and [GitHub](https://github.com/LG-AI-EXAONE/EXAONE-Deep).
 
 <p align="center">
 <img src="assets/exaone_deep_overall_performance.png", width="100%", style="margin: 40 auto;">
@@ -103,7 +103,7 @@ else:
 
 ## Evaluation
 
-The following table shows the evaluation results of reasoning tasks such as math and coding. The full evaluation results can be found in the [documentation](https://lgresearch.ai/data/upload/EXAONE_Deep__Model_Card.pdf).
+The following table shows the evaluation results of reasoning tasks such as math and coding. The full evaluation results can be found in the [documentation](https://arxiv.org/abs/2503.12524).
 
 <table>
     <tr>
@@ -268,7 +268,14 @@ The model is licensed under [EXAONE AI Model License Agreement 1.1 - NC](./LICEN
 
 ## Citation
  
-TBU
+```
+@article{exaone-deep,
+  title={EXAONE Deep: Reasoning Enhanced Language Models},
+  author={{LG AI Research}},
+  journal={arXiv preprint arXiv:2503.12524},
+  year={2025}
+}
+```
 
 ## Contact
 LG AI Research Technical Support: [email protected]