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

翻译：随着大语言模型（LLMs）在全球范围内的日益普及，LLMs必须体现世界语言的多样性。印度是一个拥有14亿人口的语言多样性国家。为促进多语言LLM评估研究，我们发布了IndicGenBench——这是目前最大的基准测试，用于评估LLMs在涵盖13种文字和4个语系的29种印度语言上的面向用户的生成任务。IndicGenBench包括多样化的生成任务，如跨语言摘要、机器翻译和跨语言问答。通过人工策划，IndicGenBench将现有基准测试扩展至多种印度语言，首次为许多代表性不足的印度语言提供了多路并行评估数据。我们在多种设置下评估了包括GPT-3.5、GPT-4、PaLM-2、mT5、Gemma、BLOOM和LLaMA在内的广泛专有和开源LLMs在IndicGenBench上的表现。最大的PaLM-2模型在大多数任务上表现最佳，但所有语言相比英语仍存在显著的性能差距，这表明需要进一步研究以开发更具包容性的多语言语言模型。IndicGenBench发布于www.github.com/google-research-datasets/indic-gen-bench。