AraMUS: Pushing the Limits of Data and Model Scale for Arabic Natural Language Processing (2023.findings-acl)
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Asaad Alghamdi, Xinyu Duan, Wei Jiang, Zhenhai Wang, Yimeng Wu, Qingrong Xia, Zhefeng Wang, Yi Zheng, Mehdi Rezagholizadeh, Baoxing Huai, Peilun Cheng, Abbas Ghaddar
| Challenge: | Developing monolingual large Pre-trained Language Models (PLMs) is shown to be very successful in handling different tasks in Natural Language Processing (NLP). |
| Approach: | They present AraMUS, the largest Arabic PLM with 11B parameters trained on 529GB of high-quality Arabic textual data. |
| Outcome: | The proposed model achieves state-of-the-art performance on a diverse set of Arabic classification and generative tasks. |
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