---
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
    num_bytes: 455101633.7
    num_examples: 8910
  - name: english
    num_bytes: 454020487.7
    num_examples: 8910
  - name: gujarati
    num_bytes: 455105448.7
    num_examples: 8910
  - name: hindi
    num_bytes: 455210630.7
    num_examples: 8910
  - name: kannada
    num_bytes: 455153061.7
    num_examples: 8910
  - name: malayalam
    num_bytes: 455401526.7
    num_examples: 8910
  - name: marathi
    num_bytes: 455379587.7
    num_examples: 8910
  - name: odia
    num_bytes: 455463255.7
    num_examples: 8910
  - name: sanskrit
    num_bytes: 455470746.7
    num_examples: 8910
  - name: tamil
    num_bytes: 455693348.7
    num_examples: 8910
  - name: telugu
    num_bytes: 455307739.7
    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**](https://arxiv.org/abs/2502.15392) | [**Github**](https://github.com/ola-krutrim/Chitrarth) | [**HuggingFace**](https://huggingface.co/krutrim-ai-labs/Chitrarth)
- Evaluation Scripts of BharatBench is available here : [**Github**](https://github.com/ola-krutrim/BharatBench)

## 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](https://github.com/AoiDragon/POPE)), 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:  

```python
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.](LICENSE.md)

## 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](https://github.com/AoiDragon/POPE), and [LLaVA-1.5](https://github.com/haotian-liu/LLaVA). Thanks for their awesome work!