  Building Natural Language Understanding (NLU) capabilities for Indic
languages, which have a collective speaker base of more than one billion
speakers is absolutely crucial. In this work, we aim to improve the NLU
capabilities of Indic languages by making contributions along 3 important axes
(i) monolingual corpora (ii) NLU testsets (iii) multilingual LLMs focusing on
Indic languages. Specifically, we curate the largest monolingual corpora,
IndicCorp, with 20.9B tokens covering 24 languages from 4 language families - a
2.3x increase over prior work, while supporting 12 additional languages. Next,
we create a human-supervised benchmark, IndicXTREME, consisting of nine diverse
NLU tasks covering 20 languages. Across languages and tasks, IndicXTREME
contains a total of 105 evaluation sets, of which 52 are new contributions to
the literature. To the best of our knowledge, this is the first effort towards
creating a standard benchmark for Indic languages that aims to test the
multilingual zero-shot capabilities of pretrained language models. Finally, we
train IndicBERT v2, a state-of-the-art model supporting all the languages.
Averaged across languages and tasks, the model achieves an absolute improvement
of 2 points over a strong baseline. The data and models are available at
https://github.com/AI4Bharat/IndicBERT.
