README.md

---
license: cc-by-nc-sa-4.0
task_categories:
  - text-classification
  - question-answering
language:
  - ar
tags:
  - MMLU
  - exams
  - BoolQ
pretty_name: 'AraDiCE -- Arabic Dialect and Cultural Evaluation'
size_categories:
  - 10K<n<100K
dataset_info:
- config_name: ArabicMMLU-egy
  splits:
    - name: test
      num_examples: 14455
- config_name: ArabicMMLU-lev
  splits:
    - name: test
      num_examples: 14455
configs:
- config_name: ArabicMMLU-egy
  data_files:
    - split: test
      path: ArabicMMLU_egy/test.json
- config_name: ArabicMMLU-lev
  data_files:
    - split: test
      path: ArabicMMLU_lev/test.json
---

# AraDiCE: Benchmarks for Dialectal and Cultural Capabilities in LLMs

## Overview

The **AraDiCE** dataset is designed to evaluate dialectal and cultural capabilities in large language models (LLMs). The dataset consists of post-edited versions of various benchmark datasets, curated for validation in cultural and dialectal contexts relevant to Arabic.

As part of the supplemental materials, we have selected a few datasets (see below) for the reader to review. We will make the full AraDiCE benchmarking suite publicly available to the community.

## File/Directory

TO DO:

- **licenses_by-nc-sa_4.0_legalcode.txt** License information.
- **README.md** This file.


## Dataset Usage

The AraDiCE dataset is intended to be used for benchmarking and evaluating large language models, specifically focusing on:

- Assessing the performance of LLMs on Arabic-specific dialect and cultural specifics.
- Dialectal variations in the Arabic language.
- Cultural context awareness in reasoning.


## License

The dataset is distributed under the **Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)**. The full license text can be found in the accompanying `licenses_by-nc-sa_4.0_legalcode.txt` file.


## Citation

```
@article{mousi2024aradicebenchmarksdialectalcultural,
      title={{AraDiCE}: Benchmarks for Dialectal and Cultural Capabilities in LLMs},
      author={Basel Mousi and Nadir Durrani and Fatema Ahmad and Md. Arid Hasan and Maram Hasanain and Tameem Kabbani and Fahim Dalvi and Shammur Absar Chowdhury and Firoj Alam},
      year={2024},
      publisher={arXiv:2409.11404},
      url={https://arxiv.org/abs/2409.11404},
}
```