Automated Translation of Islamic Literature Using Large Language Models: Al-Shamela Library Application

AbstractLarge 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.