ATHAR: A High-Quality and Diverse Dataset for Classical Arabic to
  English Translation
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.
66a867c1d96a5adbccad2fcb
Mohammed Khalil
admin_assigned
2024-07-30T09:07:12.073Z
64af7c627ab7586520ed8688
https://cdn-avatars.huggingface.co/v1/production/uploads/64af7c627ab7586520ed8688/1F3INiiqWhFZTK7Dh3DRa.jpeg
Mohammed Khalil
user
mohamed-khalil
66a867c1d96a5adbccad2fcc
Mohammed Sabry
extracted_pending
2024-07-30T04:10:41.716Z
64074cc94dc5f2846c95d6f3
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M
user
MohammedSabry
ATHAR: A High-Quality and Diverse Dataset for Classical Arabic to
  English Translation
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.
The creation and evaluation of the ATHAR dataset, containing 66,000 high-quality Classical Arabic to English translation samples, demonstrates the need for extensive datasets to improve current large language models.
large language models LLMs translation datasets Classical Arabic high-quality translation systems fine-tuning pretraining pipelines