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        <title>AI-Tutor: Interactive Learning of Ancient Knowledge from Low-Resource Languages</title>
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        <namePart type="given">Siddhartha</namePart>
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        <namePart type="given">Vethavikashini</namePart>
        <namePart type="family">Chithrra Raghuram</namePart>
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        <namePart type="given">Prahlad</namePart>
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            <namePart type="family">Nakazawa</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T AI-Tutor: Interactive Learning of Ancient Knowledge from Low-Resource Languages
%A Dalal, Siddhartha
%A Aditya, Rahul
%A Chithrra Raghuram, Vethavikashini
%A Koratamaddi, Prahlad
%Y Nakazawa, Toshiaki
%Y Goto, Isao
%S Proceedings of the Eleventh Workshop on Asian Translation (WAT 2024)
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F dalal-etal-2024-ai
%X 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.
%U https://aclanthology.org/2024.wat-1.5
%P 56-66

Markdown (Informal)

[AI-Tutor: Interactive Learning of Ancient Knowledge from Low-Resource Languages](https://aclanthology.org/2024.wat-1.5) (Dalal et al., WAT 2024)

• AI-Tutor: Interactive Learning of Ancient Knowledge from Low-Resource Languages (Dalal et al., WAT 2024)

ACL

• Siddhartha Dalal, Rahul Aditya, Vethavikashini Chithrra Raghuram, and Prahlad Koratamaddi. 2024. AI-Tutor: Interactive Learning of Ancient Knowledge from Low-Resource Languages. In Proceedings of the Eleventh Workshop on Asian Translation (WAT 2024), pages 56–66, Miami, Florida, USA. Association for Computational Linguistics.