Update README.md

README.md

@@ -19,7 +19,7 @@ language:
 
 <img src="https://huggingface.co/datasets/AdaMLLab/AraMix/resolve/main/finetasks_arabic_results.png" width="900" alt="Finetasks benchmark scores, showing AraMix-Consensus as SOTA.">
 
-AraMix (https://arxiv.org/abs/2512.18834v1) is an Arabic pretraining corpus containing 178 billion tokens across 179 million documents (in the minhash subset). Rather than scraping the web again, AraMix combines seven publicly available Arabic datasets, applies Arabic-specific quality filtering, and performs cross-dataset deduplication.
+AraMix (https://arxiv.org/abs/2512.18834v2) is an Arabic pretraining corpus containing 178 billion tokens across 179 million documents (in the minhash subset). Rather than scraping the web again, AraMix combines seven publicly available Arabic datasets, applies Arabic-specific quality filtering, and performs cross-dataset deduplication.
 
 We train a 1.4B parameter language model through nanotron on 30 billion tokens to show that the `consensus` subset of AraMix outperforms the previous state-of-the-art, [arabicweb24](https://huggingface.co/datasets/lightonai/ArabicWeb24) (see [Appendix A9 in the Fineweb-2 paper](https://arxiv.org/pdf/2506.20920)) while having more total tokens. Furthermore, the `minhash_deduped` subset performs on-par with nearly 5 times the total number of tokens.
 
@@ -69,14 +69,14 @@ Tokens were counted using `meta-llama/Llama-3.2-3B`'s tokenizer
 ## Citation
 
 ```bib
-@misc{alrashed2025aramixrecyclingrefilteringdeduplicating,
-      title={AraMix: Recycling, Refiltering, and Deduplicating to Deliver the Largest Arabic Pretraining Corpus}, 
+@misc{alrashed2025mixminmatch,
+      title={Mix, MinHash, and Match: Cross-Source Agreement for Multilingual Pretraining Datasets}, 
       author={Sultan Alrashed and Francesco Orabona},
       year={2025},
-      eprint={2512.18834},
+      eprint={2512.18834v2},
       archivePrefix={arXiv},
       primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2512.18834}, 
+      url={https://arxiv.org/abs/2512.18834v2}, 
 }
 ```