# Arabic Automatic Story Generation with Large Language Models

arXiv: https://arxiv.org/abs/2407.07551 · HTML: https://mlsanity.com/paper/2407.07551/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 35 references that are in the map.
- First posted on arXiv 10 Jul 2024.

## Builds on (most cited first, 21 of 21)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [Hierarchical Neural Story Generation](https://mlsanity.com/paper/1805.04833/) (2018)
- [Language Modeling with Gated Convolutional Networks](https://mlsanity.com/paper/1612.08083/) (2016)
- [Language-agnostic BERT Sentence Embedding](https://mlsanity.com/paper/2007.01852/) (2020)
- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023)
- [TinyStories: How Small Can Language Models Be and Still Speak Coherent English?](https://mlsanity.com/paper/2305.07759/) (2023)
- [A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories](https://mlsanity.com/paper/1604.01696/) (2016)
- [GeDi: Generative Discriminator Guided Sequence Generation](https://mlsanity.com/paper/2009.06367/) (2020)
- [Re3: Generating Longer Stories With Recursive Reprompting and Revision](https://mlsanity.com/paper/2210.06774/) (2022)
- [Visual Storytelling](https://mlsanity.com/paper/1604.03968/) (2016)
- [Do Multilingual Language Models Think Better in English?](https://mlsanity.com/paper/2308.01223/) (2023)
- [AceGPT, Localizing Large Language Models in Arabic](https://mlsanity.com/paper/2309.12053/) (2023)
- [STORIUM: A Dataset and Evaluation Platform for Machine-in-the-Loop Story Generation](https://mlsanity.com/paper/2010.01717/) (2020)
- [Peacock: A Family of Arabic Multimodal Large Language Models and Benchmarks](https://mlsanity.com/paper/2403.01031/) (2024)
- [Weaver: Foundation Models for Creative Writing](https://mlsanity.com/paper/2401.17268/) (2024)
- [Story Generation from Sequence of Independent Short Descriptions](https://mlsanity.com/paper/1707.05501/) (2017)
- [ALDi: Quantifying the Arabic Level of Dialectness of Text](https://mlsanity.com/paper/2310.13747/) (2023)
- [SARD: A Human-AI Collaborative Story Generation](https://mlsanity.com/paper/2403.01575/) (2024)

## Built on by (most cited first, 1 of 1)

- [A Survey of Large Language Models for Arabic Language and its Dialects](https://mlsanity.com/paper/2410.20238/) (2024)

## Abstract (arXiv)

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.