  While there has been significant progress towards developing NLU datasets and
benchmarks for Indic languages, syntactic evaluation has been relatively less
explored. Unlike English, Indic languages have rich morphosyntax, grammatical
genders, free linear word-order, and highly inflectional morphology. In this
paper, we introduce Vy\=akarana: a benchmark of gender-balanced Colorless Green
sentences in Indic languages for syntactic evaluation of multilingual language
models. The benchmark comprises four syntax-related tasks: PoS Tagging, Syntax
Tree-depth Prediction, Grammatical Case Marking, and Subject-Verb Agreement. We
use the datasets from the evaluation tasks to probe five multilingual language
models of varying architectures for syntax in Indic languages. Our results show
that the token-level and sentence-level representations from the Indic language
models (IndicBERT and MuRIL) do not capture the syntax in Indic languages as
efficiently as the other highly multilingual language models. Further, our
layer-wise probing experiments reveal that while mBERT, DistilmBERT, and XLM-R
localize the syntax in middle layers, the Indic language models do not show
such syntactic localization.
