AraDiCE-TruthfulQA

# 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. In this repository, we present the TruthfulQA split of the data

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## Evaluation
We have used [lm-harness](https://github.com/EleutherAI/lm-evaluation-harness) eval framework to for the benchmarking. We will soon release them. Stay tuned!!

## 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 <a href="https://arxiv.org/pdf/2409.11404" target="_blank" style="margin-right: 15px; margin-left: 10px">here.</a>

```
@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},
}
```

# AraDiCE：评估大语言模型方言与文化能力的基准数据集

## 概述
**AraDiCE**数据集旨在评估大语言模型（LLMs）的方言理解与文化适配能力。本数据集由经过后编辑的各类基准数据集版本组成，专为阿拉伯语相关的文化与方言场景验证工作整理构建。本仓库中我们公开了该数据集的TruthfulQA拆分版本。

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## 评测
我们采用[lm-harness](https://github.com/EleutherAI/lm-evaluation-harness)评测框架开展基准测试。其余拆分版本将在近期公开，敬请关注！

## 许可协议
本数据集采用**知识共享署名-非商业性使用-相同方式共享4.0国际许可协议（CC BY-NC-SA 4.0）**进行分发。完整许可协议文本可在附带的`licenses_by-nc-sa_4.0_legalcode.txt`文件中查阅。

## 引用
相关论文请点击<a href="https://arxiv.org/pdf/2409.11404" target="_blank" style="margin-right: 15px; margin-left: 10px">此处</a>查阅。

@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},
}