Large language models (LLMs) have recently emerged as a powerful tool for a wide range of language generation tasks. Nevertheless, this progress has been slower in Arabic. In this work, we focus on the task of generating stories from LLMs. For our training, we use stories acquired through machine translation (MT) as well as GPT-4. For the MT data, we develop a careful pipeline that ensures we acquire high-quality stories. For our GPT-41 data, we introduce crafted prompts that allow us to generate data well-suited to the Arabic context in both Modern Standard Arabic (MSA) and two Arabic dialects (Egyptian and Moroccan). For example, we generate stories tailored to various Arab countries on a wide host of topics. Our manual evaluation shows that our model fine-tuned on these training datasets can generate coherent stories that adhere to our instructions. We also conduct an extensive automatic and human evaluation comparing our models against state-of-the-art proprietary and open-source models. Our datasets and models will be made publicly available at https: //github.com/UBC-NLP/arastories.

翻译：大型语言模型（LLMs）已成为广泛语言生成任务的有力工具，但这一进展在阿拉伯语领域相对缓慢。本研究聚焦于利用LLMs生成故事的任务。在训练阶段，我们采用了通过机器翻译（MT）获取的故事数据以及GPT-4生成的数据。针对机器翻译数据，我们构建了精细的处理流程以确保获得高质量故事；针对GPT-4数据，我们设计了特定提示模板，使其能生成适配阿拉伯语境的内容，涵盖现代标准阿拉伯语（MSA）及两种阿拉伯方言（埃及方言与摩洛哥方言）。例如，我们针对不同阿拉伯国家生成了涉及多元主题的定制化故事。人工评估表明，基于这些训练数据微调的模型能够生成连贯且符合指令要求的故事。我们进一步开展了系统的自动评估与人工评估，将本模型与当前最先进的专有模型及开源模型进行比较。相关数据集与模型已在https://github.com/UBC-NLP/arastories公开。