@inproceedings{dalal-etal-2024-ai,
    title = "{AI}-Tutor: Interactive Learning of Ancient Knowledge from Low-Resource Languages",
    author = "Dalal, Siddhartha  and
      Aditya, Rahul  and
      Chithrra Raghuram, Vethavikashini  and
      Koratamaddi, Prahlad",
    editor = "Nakazawa, Toshiaki  and
      Goto, Isao",
    booktitle = "Proceedings of the Eleventh Workshop on Asian Translation (WAT 2024)",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/2024.wat-1.5/",
    doi = "10.18653/v1/2024.wat-1.5",
    pages = "56--66",
    abstract = "Many low-resource languages, such as Prakrit, present significant linguistic complexities and have limited modern-day resources. These languages often have multiple derivatives; for example, Prakrit, a language in use by masses around 2500 years ago for 500 years, includes Pali and Gandhari, which encompass a vast body of Buddhist literature, as well as Ardhamagadhi, rich in Jain literature. Despite these challenges, these languages are invaluable for their historical, religious, and cultural insights needed by non-language experts and others.To explore and understand the deep knowledge within these ancient texts for non-language experts, we propose a novel approach: translating multiple dialects of the parent language into a contemporary language and then enabling them to interact with the system in their native language, including English, Hindi, French and German, through a question-and-answer interface built on Large Language Models. We demonstrate the effectiveness of this novel AI-Tutor system by focusing on Ardhamagadhi and Pali."
}