MILU
Full name: Multi-task Indic Language Understanding Benchmark

## Overview

MILU (Multi-task Indic Language Understanding Benchmark) is a comprehensive evaluation dataset designed to assess the performance of Large Language Models (LLMs) across 11 Indic languages. It spans 8 domains and 42 subjects, reflecting both general and culturally specific knowledge from India.

## Key Features

- **Languages**: Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Odia, Punjabi, Tamil, Telugu, and English
- **Domains**: 8 diverse domains including Arts & Humanities, Social Sciences, STEM, and more
- **Subjects**: 42 subjects covering a wide range of topics
- **Questions**: ~85,000 multiple-choice questions
- **Cultural Relevance**: Incorporates India-specific knowledge from regional and state-level examinations

## Dataset Statistics

| Language | Total Questions | Translated Questions | Avg Words Per Question |
|----------|-----------------|----------------------|------------------------|
| Bengali  | 7138            | 1601                 | 15.72                  |
| Gujarati | 5327            | 2755                 | 16.69                  |
| Hindi    | 15450           | 115                  | 20.63                  |
| Kannada  | 6734            | 1522                 | 12.83                  |
| Malayalam| 4670            | 1534                 | 12.82                  |
| Marathi  | 7424            | 1235                 | 18.8                   |
| Odia     | 5025            | 1452                 | 15.63                  |
| Punjabi  | 4363            | 2341                 | 19.9                   |
| Tamil    | 7059            | 1524                 | 13.32                  |
| Telugu   | 7847            | 1298                 | 16.13                  |
| English  | 14036           | -                    | 22.01                  |
| **Total**| **85073**       | **15377**            | **16.77** (avg)        |



## Dataset Structure

### Test Set
The test set consists of the MILU (Multi-task Indic Language Understanding) benchmark, which contains approximately 85,000 multiple-choice questions across 11 Indic languages.

### Validation Set
The dataset includes a separate validation set of 9,157 samples that can be used for few-shot examples during evaluation. This validation set was created by sampling from each of the 42 subject tags, which were then condensed into 8 broader domains. This approach ensures a balanced representation across subjects and domains, allowing for consistent few-shot prompting across different models and experiments.

### Subjects spanning MILU


| Domain | Subjects |
|--------|----------|
| Arts & Humanities | Architecture and Design, Arts and Culture, Education, History, Language Studies, Literature and Linguistics, Media and Communication, Music and Performing Arts, Religion and Spirituality |
| Business Studies | Business and Management, Economics, Finance and Investment |
| Engineering & Tech | Energy and Power, Engineering, Information Technology, Materials Science, Technology and Innovation, Transportation and Logistics |
| Environmental Sciences | Agriculture, Earth Sciences, Environmental Science, Geography |
| Health & Medicine | Food Science, Health and Medicine |
| Law & Governance | Defense and Security, Ethics and Human Rights, Law and Ethics, Politics and Governance |
| Math and Sciences | Astronomy and Astrophysics, Biology, Chemistry, Computer Science, Logical Reasoning, Mathematics, Physics |
| Social Sciences | Anthropology, International Relations, Psychology, Public Administration, Social Welfare and Development, Sociology, Sports and Recreation |



## Usage

Since this is a gated dataset, after your request for accessing the dataset is accepted, you can set your HuggingFace token:

```bash
export HF_TOKEN=YOUR_TOKEN_HERE
```

To load the MILU dataset for a Language:

```python
from datasets import load_dataset

language = 'Hindi'

# Use 'test' split for evaluation & 'validation' split for few-shot
split = 'test'

language_data = load_dataset("ai4bharat/MILU", data_dir=language, split=split, token=True)

print(language_data[0])
```

## Evaluation

We evaluated 45 different LLMs on MILU, including:

- Closed proprietary models (e.g., GPT-4o, Gemini-1.5)
- Open-source multilingual models
- Language-specific fine-tuned models

Key findings:

- GPT-4o achieved the highest average accuracy at 72%
- Open multilingual models outperformed language-specific fine-tuned models
- Models performed better in high-resource languages compared to low-resource ones
- Performance was lower in culturally relevant areas (e.g., Arts & Humanities) compared to general fields like STEM

For detailed results and analysis, please refer to our paper.

## Citation

If you use MILU in your research, please cite our paper:

```
@misc{verma2024milumultitaskindiclanguage,
      title={MILU: A Multi-task Indic Language Understanding Benchmark}, 
      author={Sshubam Verma and Mohammed Safi Ur Rahman Khan and Vishwajeet Kumar and Rudra Murthy and Jaydeep Sen},
      year={2024},
      eprint={2411.02538},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2411.02538}, 
}```

## License

This dataset is released under the [MIT License](https://opensource.org/licenses/MIT).

## Contact

For any questions or feedback, please contact:
- Sshubam Verma (sshubamverma@ai4bharat.org)
- Mohammed Safi Ur Rahman Khan (safikhan@ai4bharat.org)
- Rudra Murthy (rmurthyv@in.ibm.com)
- Vishwajeet Kumar (vishk024@in.ibm.com)

## Links

- [GitHub Repository](https://github.com/AI4Bharat/MILU)
- [Paper](https://arxiv.org/abs/2411.02538)
- [Hugging Face Dataset](https://huggingface.co/datasets/ai4bharat/MILU)

Introducing paper: MILU: A Multi-task Indic Language Understanding Benchmark