Datasets:
Formats:
csv
Languages:
Arabic
Size:
< 1K
ArXiv:
Tags:
MMLU
reading-comprehension
commonsense-reasoning
capabilities
cultural-understanding
world-knowledge
License:
File size: 2,322 Bytes
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---
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},
}
```
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