ParsiAI/FarsInstruct

News

• [2025.01.20] 🏆 Our paper was nominated as the best paper at LowResLM @ COLING 2025!
• [2024.12.07] ✨ Our paper has been accepted for oral presentation at LowResLM @ COLING 2025!

Dataset Summary

Instruction-tuned large language models have demonstrated remarkable capabilities in following human instructions across various domains. However, their proficiency remains notably deficient in many low-resource languages. To address this challenge, we begin by introducing 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 the Public Pool of Prompts, ensuring a rich linguistic and cultural representation. Furthermore, we introduce Co-CoLA, a framework designed to enhance the multi-task adaptability of LoRA-tuned models. Through extensive experimental analyses, our study showcases the effectiveness of the FarsInstruct dataset coupled with training by the Co-CoLA framework, in improving the performance of large language models within the Persian context. As of the current writing, FarsInstruct comprises 197 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{mokhtarabadi2025empoweringpersianllmsinstruction,
      title={Empowering Persian LLMs for Instruction Following: A Novel Dataset and Training Approach}, 
      author={Hojjat Mokhtarabadi and Ziba Zamani and Abbas Maazallahi and Mohammad Hossein Manshaei},
      year={2025},
      eprint={2407.11186},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2407.11186}, 
}