README.md

metadata

dataset_info:
  features:
    - name: question_id
      dtype: int64
    - name: image
      dtype: image
    - name: text
      dtype: string
    - name: category
      dtype: string
    - name: label
      dtype: string
    - name: image_source
      dtype: string
  splits:
    - name: assamese
      num_bytes: 455367007.7
      num_examples: 8910
    - name: bengali
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      num_examples: 8910
    - name: english
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      num_examples: 8910
    - name: gujarati
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      num_examples: 8910
    - name: hindi
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      num_examples: 8910
    - name: kannada
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    - name: malayalam
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      num_examples: 8910
    - name: marathi
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      num_examples: 8910
    - name: odia
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    - name: sanskrit
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    - name: tamil
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      num_examples: 8910
    - name: telugu
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      num_examples: 8910
  download_size: 956887209
  dataset_size: 5462674475.399999
configs:
  - config_name: default
    data_files:
      - split: assamese
        path: data/assamese-*
      - split: bengali
        path: data/bengali-*
      - split: english
        path: data/english-*
      - split: gujarati
        path: data/gujarati-*
      - split: hindi
        path: data/hindi-*
      - split: kannada
        path: data/kannada-*
      - split: malayalam
        path: data/malayalam-*
      - split: marathi
        path: data/marathi-*
      - split: odia
        path: data/odia-*
      - split: sanskrit
        path: data/sanskrit-*
      - split: tamil
        path: data/tamil-*
      - split: telugu
        path: data/telugu-*
license: other
license_name: krutrim-community-license-agreement-version-1.0
license_link: LICENSE.md
extra_gated_heading: Acknowledge license to accept the repository
extra_gated_button_content: Acknowledge license
language:
  - as
  - hi
  - gu
  - ml
  - te
  - ta
  - kn
  - or
  - bn
  - en
  - mr
  - sa

IndicPope: Indian Multilingual Translation Dataset For Evaluating Large Vision Language Models

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

1. Introduction

IndicPope is a new dataset designed for evaluating Large Vision-Language Models (LVLMs) on Visual Question Answering (VQA) tasks. It focuses on simple Yes-or-No questions probing objects in images (e.g., Is there a car in the image?).

This dataset is built upon POPE: Polling-based Object Probing Evaluation for Object Hallucination (GitHub), which employs negative sampling techniques to test hallucination in vision-language models under Random, Popular, and Adversarial settings.

---

2. Dataset Details

IndicPope consists of 8.91k samples spanning 12 Indic languages along with English. Each sample includes:

• Text: The question about the image.
• Category: The type of sampling used (Random/Popular/Adversarial).
• Label: The answer (Yes/No).

Supported Languages

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

---

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/IndicPope")
print(dataset)

---

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},
}

@misc{liu2023llava,
      title={Visual Instruction Tuning}, 
      author={Liu, Haotian and Li, Chunyuan and Wu, Qingyang and Lee, Yong Jae},
      publisher={NeurIPS},
      year={2023},
}

@article{li2023evaluating,
  title={Evaluating object hallucination in large vision-language models},
  author={Li, Yifan and Du, Yifan and Zhou, Kun and Wang, Jinpeng and Zhao, Wayne Xin and Wen, Ji-Rong},
  journal={arXiv preprint arXiv:2305.10355},
  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

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