Gazelle: An Instruction Dataset for Arabic Writing Assistance

TL;DR

Gazelle introduces a new Arabic writing assistance dataset and evaluation framework, addressing data scarcity challenges and assessing current large language models' performance in Arabic text generation.

Contribution

The paper presents Gazelle, a comprehensive Arabic writing dataset, and an evaluation framework, advancing AI writing tools for underrepresented languages.

Findings

GPT-4 shows strong performance but still faces challenges with Arabic.

Dataset enrichment improves model effectiveness.

Evaluation highlights specific strengths and limitations of top LLMs.

Abstract

Writing has long been considered a hallmark of human intelligence and remains a pinnacle task for artificial intelligence (AI) due to the intricate cognitive processes involved. Recently, rapid advancements in generative AI, particularly through the development of Large Language Models (LLMs), have significantly transformed the landscape of writing assistance. However, underrepresented languages like Arabic encounter significant challenges in the development of advanced AI writing tools, largely due to the limited availability of data. This scarcity constrains the training of effective models, impeding the creation of sophisticated writing assistance technologies. To address these issues, we present Gazelle, a comprehensive dataset for Arabic writing assistance. In addition, we offer an evaluation framework designed to enhance Arabic writing assistance tools. Our human evaluation of leading LLMs, including GPT-4, GPT-4o, Cohere Command R+, and Gemini 1.5 Pro, highlights their respective strengths and limitations in addressing the challenges of Arabic writing. Our findings underscore the need for continuous model training and dataset enrichment to manage the complexities of Arabic language processing, paving the way for more effective AI-powered Arabic writing tools.