Arabic Automatic Story Generation with Large Language Models

TL;DR

This paper explores Arabic story generation using large language models, leveraging machine translation and GPT-4 to create high-quality training data, and demonstrates the models' ability to generate coherent stories in Modern Standard Arabic and dialects.

Contribution

It introduces a novel pipeline for high-quality Arabic story dataset creation and fine-tunes LLMs for Arabic story generation, including dialects, with comprehensive evaluations.

Findings

Models generate coherent, instruction-adherent stories.

Fine-tuned models outperform some state-of-the-art counterparts.

Datasets and models will be publicly available.

Abstract

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.