Update README.md

README.md

@@ -35,7 +35,8 @@ To adapt foundational models from English to Korean, we use subword-based embedd
 This approach progressively trains from input embeddings to full parameters, efficiently extending the model's vocabulary to include Korean. 
 Our method enhances the model's cross-linguistic applicability by carefully integrating new linguistic tokens, focusing on causal language modeling pre-training. 
 We leverage the inherent capabilities of foundational models trained on English to efficiently transfer knowledge and reasoning to Korean, optimizing the adaptation process.
-For detail, please refer our technical report - [Efficient and Effective Vocabulary Expansion Towards Multilingual Large Language Models](https://arxiv.org).
+
+For detail, please refer our technical report(TBU) - [Efficient and Effective Vocabulary Expansion Towards Multilingual Large Language Models](https://arxiv.org).
 
 Here’s an simplified code for our key approach:
 
@@ -91,8 +92,8 @@ This rigorous approach ensured a comprehensive and contextually rich Korean voca
 ## Citation
 
 ```
-@misc{Kim2024Efficient,
-      title={Efficient and Effective Vocabulary Expansion \\Towards Multilingual Large Language Models}, 
+@misc{kim2024efficient,
+      title={Efficient and Effective Vocabulary Expansion Towards Multilingual Large Language Models}, 
       author={Seungduk Kim, Seungtaek Choi, Myeongho Jeong},
       year={2024},
       eprint={2402.XXXXX},