Gazelle: An Instruction Dataset for Arabic Writing Assistance
This addresses the problem of limited AI writing tools for underrepresented languages like Arabic, but it is incremental as it focuses on data creation and evaluation without introducing new methods.
The authors tackled the lack of data for Arabic writing assistance by creating Gazelle, a comprehensive dataset and evaluation framework, and found that leading LLMs like GPT-4 have strengths and limitations in handling Arabic writing, highlighting the need for further training and dataset enrichment.
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.