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

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, medium

Summary

IndicMMLU-Pro is a comprehensive benchmark that extends the MMLU-Pro framework to nine major Indic languages: Hindi, Bengali, Gujarati, Marathi, Kannada, Punjabi, Tamil, Telugu, and Urdu. This benchmark offers a standardized evaluation framework for AI model development in Indic contexts, covering a wide range of tasks in comprehension, reasoning, and generation. It addresses the unique NLP challenges of Indic languages, spoken by over 1.5 billion people, which stem from their cultural richness, linguistic diversity, and structural complexity. The benchmark's design, taxonomy, and data curation are detailed, and baseline results are established using advanced multilingual models. As an open resource, IndicMMLU-Pro aims to accelerate progress in Indic language technologies and support inclusive research in multilingual NLP.

Key takeaway

For NLP Engineers developing or evaluating Large Language Models for Indic languages, IndicMMLU-Pro provides a critical, standardized framework. You should utilize this open resource to rigorously benchmark your models across nine major Indic languages, including Hindi, Bengali, and Tamil, on tasks like comprehension, reasoning, and generation. This ensures your models address the unique linguistic complexities and cultural richness of these languages, accelerating progress and fostering inclusive AI development for over 1.5 billion speakers.

Key insights

IndicMMLU-Pro is an open, comprehensive benchmark for evaluating LLMs across nine Indic languages.

Principles

• Indic languages present unique NLP challenges.
• Standardized benchmarks are crucial for AI model advancement.

Method

The benchmark's design, taxonomy, and data curation are detailed, establishing baseline results using advanced multilingual models.

In practice

• Evaluate LLMs on nine Indic languages.
• Compare model performance across diverse Indic tasks.

Topics

• IndicMMLU-Pro
• Large Language Models
• Multilingual NLP
• Language Benchmarking
• Indic Languages
• Natural Language Processing

Best for: Research Scientist, AI Engineer, AI Scientist, NLP Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.