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.

AraDICE Collection

AraDICE collection can accessed through collection page

Individual dataset can also be accessed by the following link:

• ArabicMMLU-lev
• ArabicMMLU-egy
• AraDiCE-Culture
• BoolQ
• OpenBookQA (OBQA)
• PIQA
• TruthfulQA
• AraDiCE-WinoGrande

Dataset Statistics

The datasets used in this study include: i) four existing Arabic datasets for understanding and generation: Arabic Dialects Dataset (ADD), ADI, QADI, along with a dialectal response generation dataset, and MADAR; ii) seven datasets translated and post-edited into MSA and dialects (Levantine and Egyptian), which include ArabicMMLU, BoolQ, PIQA, OBQA, Winogrande, Belebele, and TruthfulQA; and iii) AraDiCE-Culture, an in-house developed regional Arabic cultural understanding dataset. Please find below the types of dataset and their statistics benchmarked in AraDiCE.

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.

Evaluation

We have used lm-harness eval framework to for the benchmarking. We will soon release them. Stay tuned!!

Machine Translation Models

We will soon be releasing all our machine translation models. Stay tuned! For early access, feel free to contact us.

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

Please find the paper here.If you use all or any specific dataset in this collection, please make sure if also cite original dataset paper. You will find the citations in our paper.

@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},
}