PNLPhub/FarsInstruct

Dataset Summary

Instruction-tuned large language models, such as T0, have demonstrated remarkable capabilities in following instructions across various domains. However, their proficiency remains notably deficient in many low-resource languages. To address this challenge, we introduce FarsInstruct: a comprehensive instruction dataset designed to enhance the instruction-following ability of large language models specifically for the Persian language, a significant yet underrepresented language globally. FarsInstruct encompasses a wide range of task types and datasets, each containing a mix of straightforward to complex manual written instructions, as well as translations from Public Pool of Prompts, ensuring a rich linguistic and cultural representation. As of the current writing, FarsInstruct comprises more than 200 templates across 21 distinct datasets, and we intend to update it consistently, thus augmenting its applicability.

Supported tasks

Figure 1: Detailed depiction of 11 task types utilized in our dataset. Each box within the figure lists the specific datasets associated with the respective task type. Datasets designated for training are highlighted in blue, and those reserved for testing are marked in orange. Additionally, manual datasets, which have been specifically curated and prompted by our team, are enclosed with solid borders. In contrast, datasets that have been translated from English to Persian are enclosed with dashed borders

Citation information

@misc{mokhtarabadi2024farsinstructempoweringlargelanguage,
      title={FarsInstruct: Empowering Large Language Models for Persian Instruction Understanding}, 
      author={Hojjat Mokhtarabadi and Ziba Zamani and Abbas Maazallahi and Hossein Manshaei},
      year={2024},
      eprint={2407.11186},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2407.11186}, 
}