Mutarjim: Advancing Bidirectional Arabic-English Translation with a Small Language Model
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arXiv
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| Format: | Preprint |
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2025
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| author | Hennara, Khalil Hreden, Muhammad Hamed, Mohamed Motaism Aldallal, Zeina Chrouf, Sara AlModhayan, Safwan |
| author_facet | Hennara, Khalil Hreden, Muhammad Hamed, Mohamed Motaism Aldallal, Zeina Chrouf, Sara AlModhayan, Safwan |
| contents | We introduce Mutarjim, a compact yet powerful language model for bidirectional Arabic-English translation. While large-scale LLMs have shown impressive progress in natural language processing tasks, including machine translation, smaller models. Leveraging this insight, we developed Mutarjim based on Kuwain-1.5B , a language model tailored for both Arabic and English. Despite its modest size, Mutarjim outperforms much larger models on several established benchmarks, achieved through an optimized two-phase training approach and a carefully curated, high-quality training corpus.. Experimental results show that Mutarjim rivals models up to 20 times larger while significantly reducing computational costs and training requirements. We also introduce Tarjama-25, a new benchmark designed to overcome limitations in existing Arabic-English benchmarking datasets, such as domain narrowness, short sentence lengths, and English-source bias. Tarjama-25 comprises 5,000 expert-reviewed sentence pairs and spans a wide range of domains, offering a more comprehensive and balanced evaluation framework. Notably, Mutarjim achieves state-of-the-art performance on the English-to-Arabic task in Tarjama-25, surpassing even significantly larger and proprietary models like GPT-4o mini. We publicly release Tarjama-25 to support future research and advance the evaluation of Arabic-English translation systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_17894 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Mutarjim: Advancing Bidirectional Arabic-English Translation with a Small Language Model Hennara, Khalil Hreden, Muhammad Hamed, Mohamed Motaism Aldallal, Zeina Chrouf, Sara AlModhayan, Safwan Computation and Language Artificial Intelligence We introduce Mutarjim, a compact yet powerful language model for bidirectional Arabic-English translation. While large-scale LLMs have shown impressive progress in natural language processing tasks, including machine translation, smaller models. Leveraging this insight, we developed Mutarjim based on Kuwain-1.5B , a language model tailored for both Arabic and English. Despite its modest size, Mutarjim outperforms much larger models on several established benchmarks, achieved through an optimized two-phase training approach and a carefully curated, high-quality training corpus.. Experimental results show that Mutarjim rivals models up to 20 times larger while significantly reducing computational costs and training requirements. We also introduce Tarjama-25, a new benchmark designed to overcome limitations in existing Arabic-English benchmarking datasets, such as domain narrowness, short sentence lengths, and English-source bias. Tarjama-25 comprises 5,000 expert-reviewed sentence pairs and spans a wide range of domains, offering a more comprehensive and balanced evaluation framework. Notably, Mutarjim achieves state-of-the-art performance on the English-to-Arabic task in Tarjama-25, surpassing even significantly larger and proprietary models like GPT-4o mini. We publicly release Tarjama-25 to support future research and advance the evaluation of Arabic-English translation systems. |
| title | Mutarjim: Advancing Bidirectional Arabic-English Translation with a Small Language Model |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2505.17894 |