Airavata: Introducing Hindi Instruction-tuned LLM

Jay P. Gala; Thanmay Jayakumar; Jaavid Aktar Husain; Kumar M. Aswanth; Mohammed Safi Ur Rahman Khan; Diptesh Kanojia; Ratish Puduppully; Mitesh M. Khapra; Raj Dabre; V. Rudra Murthy; Anoop Kunchukuttan

doi:10.48550/ARXIV.2401.15006

Airavata: Introducing Hindi Instruction-tuned LLM

Jay P. Gala, Thanmay Jayakumar, Jaavid Aktar Husain, Kumar M. Aswanth, Mohammed Safi Ur Rahman Khan, Diptesh Kanojia, Ratish Puduppully, Mitesh M. Khapra, Raj Dabre, V. Rudra Murthy, Anoop Kunchukuttan

Publikation: Andet › Andet bidrag › Forskning

Abstract

We announce the initial release of "Airavata," an instruction-tuned LLM for Hindi. Airavata was created by fine-tuning OpenHathi with diverse, instruction-tuning Hindi datasets to make it better suited for assistive tasks. Along with the model, we also share the IndicInstruct dataset, which is a collection of diverse instruction-tuning datasets to enable further research for Indic LLMs. Additionally, we present evaluation benchmarks and a framework for assessing LLM performance across tasks in Hindi. Currently, Airavata supports Hindi, but we plan to expand this to all 22 scheduled Indic languages. You can access all artifacts at this https URL.

Originalsprog	Engelsk
Publikationsdato	2024
Vol/bind	abs/2401.15006
Antal sider	20
DOI	https://doi.org/10.48550/ARXIV.2401.15006
Status	Udgivet - 2024
Udgivet eksternt	Ja

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10.48550/ARXIV.2401.15006Licens: CC BY

https://dblp.org/rec/journals/corr/abs-2401-15006

Citationsformater

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AU - Husain, Jaavid Aktar

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AU - Khan, Mohammed Safi Ur Rahman

AU - Kanojia, Diptesh

AU - Puduppully, Ratish

AU - Khapra, Mitesh M.

AU - Dabre, Raj

AU - Murthy, V. Rudra

AU - Kunchukuttan, Anoop

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AB - We announce the initial release of "Airavata," an instruction-tuned LLM for Hindi. Airavata was created by fine-tuning OpenHathi with diverse, instruction-tuning Hindi datasets to make it better suited for assistive tasks. Along with the model, we also share the IndicInstruct dataset, which is a collection of diverse instruction-tuning datasets to enable further research for Indic LLMs. Additionally, we present evaluation benchmarks and a framework for assessing LLM performance across tasks in Hindi. Currently, Airavata supports Hindi, but we plan to expand this to all 22 scheduled Indic languages. You can access all artifacts at this https URL.

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Airavata: Introducing Hindi Instruction-tuned LLM

Abstract

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