IndicMMVet: Indian Multilingual Translated Dataset For Evaluating Large Vision Language Models for Integrated Capabilities

• You can find the performance of Chitrarth on IndicMMVet here : Paper | Github | HuggingFace
• Evaluation Scripts of BharatBench is available here : Github

1. Introduction

IndicMMVet is a new dataset designed for evaluating Large Vision-Language Models (LVLMs) on Visual Question Answering (VQA) tasks and focuses on the integration of multiple core VL capabilities. These include recognition, OCR, knowledge, language generation, spatial awareness, and math.

This dataset is built upon MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities (GitHub).

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2. Dataset Details

IndicMMVet consists of 218 samples spanning 12 Indic languages along with English. Each sample includes:

• Question: The question about the image.
• Capability: The type of sampling used (recognition, OCR, knowledge, language generation, spatial awareness, and math).
• Answer: The answer.

Supported Languages

• Assamese
• Bengali
• English
• Gujarati
• Hindi
• Kannada
• Malayalam
• Marathi
• Odia
• Sanskrit
• Tamil
• Telugu

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3. How to Use and Run

You can load the dataset using the datasets library:

from datasets import load_dataset

dataset = load_dataset("krutrim-ai-labs/IndicMMVet")
print(dataset)

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4. License

This code repository and the model weights are licensed under the Krutrim Community License.

5. Citation

@article{khan2025chitrarth,
  title={Chitrarth: Bridging Vision and Language for a Billion People},
  author={Shaharukh Khan, Ayush Tarun, Abhinav Ravi, Ali Faraz, Akshat Patidar, Praveen Kumar Pokala, Anagha Bhangare, Raja Kolla, Chandra Khatri, Shubham Agarwal},
  journal={arXiv preprint arXiv:2502.15392},
  year={2025}
}

@misc{liu2023improvedllava,
      title={Improved Baselines with Visual Instruction Tuning}, 
      author={Liu, Haotian and Li, Chunyuan and Li, Yuheng and Lee, Yong Jae},
      publisher={arXiv:2310.03744},
      year={2023},
}

@article{yu2023mm,
  title={Mm-vet: Evaluating large multimodal models for integrated capabilities},
  author={Yu, Weihao and Yang, Zhengyuan and Li, Linjie and Wang, Jianfeng and Lin, Kevin and Liu, Zicheng and Wang, Xinchao and Wang, Lijuan},
  journal={arXiv preprint arXiv:2308.02490},
  year={2023}
}

@article{gala2023indictrans2,
  title={Indictrans2: Towards high-quality and accessible machine translation models for all 22 scheduled indian languages},
  author={Gala, Jay and Chitale, Pranjal A and AK, Raghavan and Gumma, Varun and Doddapaneni, Sumanth and Kumar, Aswanth and Nawale, Janki and Sujatha, Anupama and Puduppully, Ratish and Raghavan, Vivek and others},
  journal={arXiv preprint arXiv:2305.16307},
  year={2023}
}

6. Contact

Contributions are welcome! If you have any improvements or suggestions, feel free to submit a pull request on GitHub.

7. Acknowledgement

IndicMMVet is built with reference to the code of the following projects: MM-Vet, and LLaVA-1.5. Thanks for their awesome work!