title: ATHAR: A High-Quality and Diverse Dataset for Classical Arabic to
    English Translation
summary: Classical Arabic represents a significant era, encompassing the golden age of
  Arab culture, philosophy, and scientific literature. With a broad consensus on
  the importance of translating these literatures to enrich knowledge
  dissemination across communities, the advent of large language models (LLMs)
  and translation systems offers promising tools to facilitate this goal.
  However, we have identified a scarcity of translation datasets in Classical
  Arabic, which are often limited in scope and topics, hindering the development
  of high-quality translation systems. In response, we present the ATHAR dataset,
  comprising 66,000 high-quality Classical Arabic to English translation samples
  that cover a wide array of subjects including science, culture, and philosophy.
  Furthermore, we assess the performance of current state-of-the-art LLMs under
  various settings, concluding that there is a need for such datasets in current
  systems. Our findings highlight how models can benefit from fine-tuning or
  incorporating this dataset into their pretraining pipelines. The dataset is
  publicly available on the HuggingFace Data Hub at
  https://huggingface.co/datasets/mohamed-khalil/ATHAR.
paper_authors._id: 66a867c1d96a5adbccad2fcb
paper_authors.name: Mohammed Khalil
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-30T09:07:12.073Z
paper_authors._id: 64af7c627ab7586520ed8688
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64af7c627ab7586520ed8688/1F3INiiqWhFZTK7Dh3DRa.jpeg
paper_authors.fullname: Mohammed Khalil
paper_authors.type: user
paper_authors.user: mohamed-khalil
paper_authors._id: 66a867c1d96a5adbccad2fcc
paper_authors.name: Mohammed Sabry
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-07-30T04:10:41.716Z
paper_authors._id: 64074cc94dc5f2846c95d6f3
paper_authors.avatarUrl: /avatars/c76075e6ae0846771f5624f5348aa0b7.svg
paper_authors.fullname: M
paper_authors.type: user
paper_authors.user: MohammedSabry
paper_title: ATHAR: A High-Quality and Diverse Dataset for Classical Arabic to
    English Translation
paper_summary: Classical Arabic represents a significant era, encompassing the golden age of
  Arab culture, philosophy, and scientific literature. With a broad consensus on
  the importance of translating these literatures to enrich knowledge
  dissemination across communities, the advent of large language models (LLMs)
  and translation systems offers promising tools to facilitate this goal.
  However, we have identified a scarcity of translation datasets in Classical
  Arabic, which are often limited in scope and topics, hindering the development
  of high-quality translation systems. In response, we present the ATHAR dataset,
  comprising 66,000 high-quality Classical Arabic to English translation samples
  that cover a wide array of subjects including science, culture, and philosophy.
  Furthermore, we assess the performance of current state-of-the-art LLMs under
  various settings, concluding that there is a need for such datasets in current
  systems. Our findings highlight how models can benefit from fine-tuning or
  incorporating this dataset into their pretraining pipelines. The dataset is
  publicly available on the HuggingFace Data Hub at
  https://huggingface.co/datasets/mohamed-khalil/ATHAR.
paper_ai_keywords: large language models (LLMs) translation systems translation datasets ATHAR dataset pretraining pipelines fine-tuning