mlsanityData to 7 Oct 2026

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

arXiv 2501.15747 ↗First posted 27 Jan 2025cs.CL cs.AIcited by 9

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Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 16 of its 34 references that are in the map. First posted on arXiv 27 Jan 2025.

Builds on

16 of 34 references.

References · 16

  1. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
  2. 2018A Call for Clarity in Reporting BLEU Scorescited by 1,790
  3. 2024MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmarkcited by 1,758
  4. 2020The State and Fate of Linguistic Diversity and Inclusion in the NLP Worldcited by 789
  5. 2020XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalizationcited by 738
  6. 2020XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generationcited by 221
  7. 2023MEGAVERSE: Benchmarking Large Language Models Across Languages, Modalities, Models and Taskscited by 57
  8. 2022IndicNLG Benchmark: Multilingual Datasets for Diverse NLG Tasks in Indic Languagescited by 42
  9. 2023Tamil-Llama: A New Tamil Language Model Based on Llama 2cited by 31
  10. 2024Evaluating the Elementary Multilingual Capabilities of Large Language Models with MultiQcited by 27
  11. 2022IndicXNLI: Evaluating Multilingual Inference for Indian Languagescited by 26
  12. 2024Parameter Efficient Fine Tuning: A Comprehensive Analysis Across Applicationscited by 25
4 more
  1. 2024The "Colonial Impulse" of Natural Language Processing: An Audit of Bengali Sentiment Analysis Tools and Their Identity-based Biasescited by 16
  2. 2025Potential and Perils of Large Language Models as Judges of Unstructured Textual Datacited by 11
  3. 2024Reconciling Methodological Paradigms: Employing Large Language Models as Novice Qualitative Research Assistants in Talent Management Researchcited by 5
  4. 2024Decoding the Diversity: A Review of the Indic AI Research Landscapecited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 4.

9 papers.

  1. 2025MELAC: Massive Evaluation of Large Language Models with Alignment of Culture in Persian Languagecited by 3
  2. 2026Common to Whom? Regional Cultural Commonsense and LLM Bias in Indiacited by 3
  3. 2025ParamBench: A Graduate-Level Benchmark for Evaluating LLM Understanding on Indic Subjectscited by 2
  4. 2025Benchmarking Hindi LLMs: A New Suite of Datasets and a Comparative Analysiscited by 2
  5. 2025Eka-Eval: An Evaluation Framework for Low-Resource Multilingual Large Language Modelscited by 0
  6. 2025HinTel-AlignBench: A Framework and Benchmark for Hindi-Telugu with English-Aligned Samplescited by 0
  7. 2026UrduMMLU: A Massive Multitask Benchmark for Urdu Language Understandingcited by 0
  8. 2026SurakshaEval: An Indic Safety Benchmark for Multilingual LLMscited by 0
  9. 2026When Models Defer to Wrong Answers: A Robustness Audit of Source-Attributed Cues in Multiple-Choice QAcited by 0