title: Mutarjim: Advancing Bidirectional Arabic-English Translation with a
    Small Language Model
summary: 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.
paper_authors._id: 683577db7733c0f27e945847
paper_authors.name: Khalil Hennara
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paper_authors.fullname: Khalil Hennara
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paper_authors.name: Muhammad Hreden
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paper_authors.fullname: Muhammad Hreden
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paper_authors.name: Mohamed Motaism Hamed
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paper_authors.fullname: Mohamed Motasim Hamed
paper_authors.type: user
paper_authors.user: Moatasem444
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paper_authors.name: Zeina Aldallal
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paper_authors.fullname: Zeina Aldallal
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paper_authors.user: ZeinaD
paper_authors._id: 683577db7733c0f27e94584b
paper_authors.name: Sara Chrouf
paper_authors.status: extracted_confirmed
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paper_authors.avatarUrl: /avatars/12f284e007e8b5cddeadf4491a0a1fe8.svg
paper_authors.fullname: Sara Chrouf
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paper_authors.user: SaraChrouf
paper_authors._id: 683577db7733c0f27e94584c
paper_authors.name: Safwan AlModhayan
paper_title: Mutarjim: Advancing Bidirectional Arabic-English Translation with a
    Small Language Model
paper_summary: 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.
paper_ai_summary: Mutarjim is a compact Arabic-English translation model that outperforms larger models on established benchmarks and achieves state-of-the-art performance on a new comprehensive Tarjama-25 benchmark.
paper_ai_keywords: language model bidirectional Arabic-English translation LLMs Kuwain-1.5B two-phase training high-quality training corpus Tarjama-25 domain narrowness English-source bias GPT-4