@inproceedings{khair-sawalha-2025-automated,
    title = "Automated Translation of Islamic Literature Using Large Language Models: Al-Shamela Library Application",
    author = "Khair, Mohammad Mohammad  and
      Sawalha, Majdi",
    editor = "Yagi, Sane  and
      Yagi, Sane  and
      Sawalha, Majdi  and
      Shawar, Bayan Abu  and
      AlShdaifat, Abdallah T.  and
      Abbas, Norhan  and
      Organizers",
    booktitle = "Proceedings of the New Horizons in Computational Linguistics for Religious Texts",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2025.clrel-1.5/",
    pages = "53--58",
    abstract = "Large Language Models (LLM) can be useful tools for translating Islamic literature written in Arabic into several languages, making this complex task technologically feasible, providing high-quality translations, at low cost and high-speed production enabled by parallel computing. We applied LLM-driven translation automation on a diverse corpus of Islamic scholarly works including: the Qur`an, Quranic exegesis (Tafseer), Hadith, and Jurisprudence from the Al-Shamela library. More than 250,000 pages have been translated into English, emphasizing the potential of LLMs to cross language barriers and increase global access to Islamic knowledge. OpenAI`s gpt-4o-mini model was used for the forward translation from Arabic to English with acceptable translation quality. Translation quality validation was achieved by reproducing Arabic text via back-translation from English using both the OpenAI LLM and an independent Anthropic LLM. Correlating the original source Arabic text and the back-translation Arabic text using a vector embedding cosine similarity metric demonstrated comparable translation quality between the two models."
}