title: Sadeed: Advancing Arabic Diacritization Through Small Language Model
summary: Arabic text diacritization remains a persistent challenge in natural language
  processing due to the language's morphological richness. In this paper, we
  introduce Sadeed, a novel approach based on a fine-tuned decoder-only language
  model adapted from Kuwain 1.5B Hennara et al. [2025], a compact model
  originally trained on diverse Arabic corpora. Sadeed is fine-tuned on carefully
  curated, high-quality diacritized datasets, constructed through a rigorous
  data-cleaning and normalization pipeline. Despite utilizing modest
  computational resources, Sadeed achieves competitive results compared to
  proprietary large language models and outperforms traditional models trained on
  similar domains. Additionally, we highlight key limitations in current
  benchmarking practices for Arabic diacritization. To address these issues, we
  introduce SadeedDiac-25, a new benchmark designed to enable fairer and more
  comprehensive evaluation across diverse text genres and complexity levels.
  Together, Sadeed and SadeedDiac-25 provide a robust foundation for advancing
  Arabic NLP applications, including machine translation, text-to-speech, and
  language learning tools.
paper_authors._id: 68130be55342cbe1ddefb262
paper_authors.name: Zeina Aldallal
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paper_authors.fullname: Zeina Aldallal
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paper_authors.name: Sara Chrouf
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paper_authors.fullname: Sara Chrouf
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paper_authors.user: SaraChrouf
paper_authors._id: 68130be55342cbe1ddefb264
paper_authors.name: Khalil Hennara
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paper_authors.fullname: Khalil Hennara
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paper_authors.user: Hennara
paper_authors._id: 68130be55342cbe1ddefb265
paper_authors.name: Mohamed Motaism Hamed
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paper_authors.fullname: Mohamed Motasim Hamed
paper_authors.type: user
paper_authors.user: Moatasem444
paper_authors._id: 68130be55342cbe1ddefb266
paper_authors.name: Muhammad Hreden
paper_authors.status: extracted_confirmed
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paper_authors.fullname: Muhammad Hreden
paper_authors.type: user
paper_authors.user: hr99
paper_authors._id: 68130be55342cbe1ddefb267
paper_authors.name: Safwan AlModhayan
paper_title: Sadeed: Advancing Arabic Diacritization Through Small Language Model
paper_summary: Arabic text diacritization remains a persistent challenge in natural language
  processing due to the language's morphological richness. In this paper, we
  introduce Sadeed, a novel approach based on a fine-tuned decoder-only language
  model adapted from Kuwain 1.5B Hennara et al. [2025], a compact model
  originally trained on diverse Arabic corpora. Sadeed is fine-tuned on carefully
  curated, high-quality diacritized datasets, constructed through a rigorous
  data-cleaning and normalization pipeline. Despite utilizing modest
  computational resources, Sadeed achieves competitive results compared to
  proprietary large language models and outperforms traditional models trained on
  similar domains. Additionally, we highlight key limitations in current
  benchmarking practices for Arabic diacritization. To address these issues, we
  introduce SadeedDiac-25, a new benchmark designed to enable fairer and more
  comprehensive evaluation across diverse text genres and complexity levels.
  Together, Sadeed and SadeedDiac-25 provide a robust foundation for advancing
  Arabic NLP applications, including machine translation, text-to-speech, and
  language learning tools.
paper_ai_summary: Sadeed, a fine-tuned decoder-only language model, enhances Arabic text diacritization using limited resources and addresses benchmarking limitations with SadeedDiac-25.
paper_ai_keywords: decoder-only language model fine-tuned Kuwain 1.5B data-cleaning normalization benchmarking SadeedDiac-25