Summary of Arabic Automatic Story Generation with Large Language Models, by Ahmed Oumar El-shangiti and Fakhraddin Alwajih and Muhammad Abdul-mageed
Arabic Automatic Story Generation with Large Language Models
by Ahmed Oumar El-Shangiti, Fakhraddin Alwajih, Muhammad Abdul-Mageed
First submitted to arxiv on: 10 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a significant step forward in generating stories from large language models (LLMs) for Arabic, a long-underexplored language generation task. To develop high-quality training data, the authors employ machine translation (MT) and GPT-4, crafting prompts tailored to the Modern Standard Arabic (MSA), Egyptian, and Moroccan dialects. The fine-tuned model is evaluated through manual and automatic assessments, demonstrating its ability to generate coherent stories that adhere to instructions. The authors also conduct a comprehensive comparison with state-of-the-art proprietary and open-source models. By releasing their datasets and models publicly, the paper aims to accelerate progress in this area. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it possible for computers to create stories in Arabic, which is important because Arabic-speaking people deserve better language technology. The researchers used machine translation and a special kind of AI model called GPT-4 to create training data that’s specifically designed for Arabic. They also made sure the data was high-quality by carefully selecting what to translate and how to present it. The results show that their model can generate stories that make sense and follow instructions. This is an important step forward in developing language technology that works well for Arabic-speaking people. |
Keywords
» Artificial intelligence » Gpt » Translation
