  As large language models (LLMs) see increasing adoption across the globe, it
is imperative for LLMs to be representative of the linguistic diversity of the
world. India is a linguistically diverse country of 1.4 Billion people. To
facilitate research on multilingual LLM evaluation, we release IndicGenBench -
the largest benchmark for evaluating LLMs on user-facing generation tasks
across a diverse set 29 of Indic languages covering 13 scripts and 4 language
families. IndicGenBench is composed of diverse generation tasks like
cross-lingual summarization, machine translation, and cross-lingual question
answering. IndicGenBench extends existing benchmarks to many Indic languages
through human curation providing multi-way parallel evaluation data for many
under-represented Indic languages for the first time. We evaluate a wide range
of proprietary and open-source LLMs including GPT-3.5, GPT-4, PaLM-2, mT5,
Gemma, BLOOM and LLaMA on IndicGenBench in a variety of settings. The largest
PaLM-2 models performs the best on most tasks, however, there is a significant
performance gap in all languages compared to English showing that further
research is needed for the development of more inclusive multilingual language
models. IndicGenBench is released at
www.github.com/google-research-datasets/indic-gen-bench
