Summary of Gemmar: Enhancing Llms Through Arabic Instruction-tuning, by Hasna Chouikhi et al.
GemmAr: Enhancing LLMs Through Arabic Instruction-Tuning
by Hasna Chouikhi, Manel Aloui, Cyrine Ben Hammou, Ghaith Chaabane, Haithem Kchaou, Chehir Dhaouadi
First submitted to arxiv on: 2 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 Large language models have revolutionized the natural language processing field, particularly for English. These models can understand and generate human-like text with remarkable accuracy. However, their success largely depends on the availability of high-quality instruction datasets that provide detailed task descriptions and corresponding responses. While these models excel in English, they often struggle with languages like Arabic due to a lack of datasets for fine-tuning Arabic-specific tasks. To address this issue, we introduce InstAr-500k, a new Arabic instruction dataset generated and collected from various domains and instruction types. We assess this dataset by fine-tuning an open-source Gemma-7B model on several downstream tasks to improve its functionality. Our fine-tuned model achieves excellent performance on several Arabic NLP benchmarks, highlighting the effectiveness of our dataset in elevating language models for Arabic. This new resource bridges the performance gap between English and Arabic language models, amplifying Arabic NLP development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are special computers that can understand and create text like humans do. They’re really good at understanding and creating English text, but they have trouble with other languages like Arabic because there aren’t enough instructions to help them learn. To fix this problem, we created a new set of instructions just for Arabic called InstAr-500k. We used these instructions to teach an existing language model how to do better on different tasks related to Arabic. Our model did really well on tests and shows that our new instructions are helping language models get better at understanding and working with Arabic. |
Keywords
* Artificial intelligence * Fine tuning * Language model * Natural language processing * Nlp
