This paper introduces two methods for creating Arabic LLM prompts at scale: translating existing English prompt datasets and creating natural language prompts from Arabic NLP datasets. Using these methods, the authors generated over 67.4 million Arabic prompts covering tasks like summarization and question answering. Fine-tuning a 7B Qwen2 model on these prompts outperforms a 70B Llama3 model in handling Arabic prompts. Why it matters: The research provides a cost-effective approach to scaling Arabic LLM training data, potentially improving the performance of smaller, more accessible models for Arabic NLP.
This study reviews the use of large language models (LLMs) for Arabic language processing, focusing on pre-trained models and their applications. It highlights the challenges in Arabic NLP due to the language's complexity and the relative scarcity of resources. The review also discusses how techniques like fine-tuning and prompt engineering enhance model performance on Arabic benchmarks. Why it matters: This overview helps consolidate research directions and benchmarks in Arabic NLP, guiding future development of LLMs tailored for the Arabic language and its diverse dialects.