@inproceedings{pavlova-2025-multi,
    title = "Multi-stage Training of Bilingual Islamic {LLM} for Neural Passage Retrieval",
    author = "Pavlova, Vera",
    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/landing_page/2025.clrel-1.4/",
    pages = "42--52",
    abstract = "This study examines the use of Natural Language Processing (NLP) technology within the Islamic domain, focusing on developing an Islamic neural retrieval model. By leveraging the robust XLM-R base model, the research employs a language reduction technique to create a lightweight bilingual large language model (LLM). Our approach for domain adaptation addresses the unique challenges faced in the Islamic domain, where substantial in-domain corpora exist only in Arabic while limited in other languages, including English. The work utilizes a multi-stage training process for retrieval models, incorporating large retrieval datasets, such as MS MARCO, and smaller, in-domain datasets to improve retrieval performance. Additionally, we have curated an in-domain retrieval dataset in English by employing data augmentation techniques and involving a reliable Islamic source. This approach enhances the domain-specific dataset for retrieval, leading to further performance gains. The findings suggest that combining domain adaptation and a multi-stage training method for the bilingual Islamic neural retrieval model enables it to outperform monolingual models on downstream retrieval tasks."
}