metadata

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},
}