IndicMMLU-Pro: Benchmarking Indic Large Language Models on Multi-Task Language Understanding

TL;DR

IndicMMLU-Pro is a comprehensive benchmark for evaluating large language models on multiple Indic languages across diverse tasks, aiming to advance culturally sensitive NLP research in the Indian subcontinent.

Contribution

The paper introduces a new benchmark specifically designed for Indic languages, covering multiple tasks and languages, with baseline results for state-of-the-art models.

Findings

Benchmark covers major Indic languages and tasks.

Baseline models show varied performance across languages.

Framework promotes development of culturally aware NLP models.

Abstract

Known by more than 1.5 billion people in the Indian subcontinent, Indic languages present unique challenges and opportunities for natural language processing (NLP) research due to their rich cultural heritage, linguistic diversity, and complex structures. IndicMMLU-Pro is a comprehensive benchmark designed to evaluate Large Language Models (LLMs) across Indic languages, building upon the MMLU Pro (Massive Multitask Language Understanding) framework. Covering major languages such as Hindi, Bengali, Gujarati, Marathi, Kannada, Punjabi, Tamil, Telugu, and Urdu, our benchmark addresses the unique challenges and opportunities presented by the linguistic diversity of the Indian subcontinent. This benchmark encompasses a wide range of tasks in language comprehension, reasoning, and generation, meticulously crafted to capture the intricacies of Indian languages. IndicMMLU-Pro provides a standardized evaluation framework to push the research boundaries in Indic language AI, facilitating the development of more accurate, efficient, and culturally sensitive models. This paper outlines the benchmarks' design principles, task taxonomy, and data collection methodology, and presents baseline results from state-of-the-art multilingual models.