Update README.md

README.md

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   - split: test
     path: xl_wic/test-*
 ---
+
+## News
+
+* **[2025.01.20]** 🏆 Our paper was nominated as the best paper at <a href="https://loreslm.github.io/">LowResLM @ COLING 2025!</a>
+
 <p align="center">
   <img src="https://huggingface.co/datasets/PNLPhub/FarsInstruct/resolve/main/photo_2024-09-13_11-13-49.jpg" width="200" height="200">
 </p>
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 - **Homepage:** https://github.com/Hojjat-Mokhtarabadi/FarsInstruct
 - **Repository:** https://github.com/Hojjat-Mokhtarabadi/promptsource
-- **Paper:** [FarsInstruct: Empowering Large Language Models for Persian Instruction Understanding](https://arxiv.org/abs/2407.11186)
+- **Paper:** [Empowering Persian LLMs for Instruction Following: A Novel Dataset and Training Approach](https://arxiv.org/abs/2407.11186)
 - **Point of Contact:** [Hojjat Mokhtarabadi](mailto:[email protected])
 
 ### 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.
+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
 <img src="https://huggingface.co/datasets/PNLPhub/FarsInstruct/resolve/main/high-quality.jpeg" width="900" height="500"> Figure 1: Detailed depiction of 11 task types utilized in our dataset. Each box within the figure lists the specific
@@ -691,10 +696,10 @@ to Persian are enclosed with dashed borders
 ## Citation information
 <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
 ```bibtex
-@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},
+@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},