Summary of Empowering Persian Llms For Instruction Following: a Novel Dataset and Training Approach, by Hojjat Mokhtarabadi et al.
Empowering Persian LLMs for Instruction Following: A Novel Dataset and Training Approach
by Hojjat Mokhtarabadi, Ziba Zamani, Abbas Maazallahi, Mohammad Hossein Manshaei
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper focuses on enhancing the instruction-following abilities of large language models in low-resource languages, specifically Persian. To achieve this goal, the authors introduce FarsInstruct, a comprehensive dataset designed to improve the performance of language models in following human instructions. The dataset contains a wide range of task types and datasets, each with a mix of straightforward to complex manual written instructions and translations from the Public Pool of Prompts. Additionally, the paper proposes Co-CoLA, a framework that enhances the multi-task adaptability of LoRA-tuned models. Experimental results demonstrate the effectiveness of using FarsInstruct in conjunction with Co-CoLA training, leading to improved performance in the Persian context. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study aims to help large language models understand and follow instructions better, especially for languages like Persian that don’t have many resources. To do this, the researchers created a big dataset called FarsInstruct that has lots of different types of tasks and instructions. They also developed a new way to train these models using something called Co-CoLA. By testing their approach, they showed that it can make language models better at following instructions in Persian. |
Keywords
» Artificial intelligence » Lora » Multi task
