Mind the Gap: A Review of Arabic Post-Training Datasets and Their Limitations

2025-07-19

AI Summary

This work addresses critical shortcomings in existing Arabic large language model (LLM) post-training datasets—namely, insufficient task diversity, incomplete documentation, low annotation quality, and poor community adoption. To systematically evaluate these datasets, we propose the first four-dimensional Arabic-specific assessment framework, covering capability coverage, controllability, alignment, and robustness. Our methodology involves a comprehensive survey of publicly available Arabic datasets on Hugging Face, augmented by literature review and empirical analysis across key dimensions: popularity, maintenance status, annotation quality, and license transparency. This reveals structural gaps—including task distribution imbalance, missing metadata, and limited real-world applicability. Crucially, we provide the first quantitative characterization of weaknesses in the Arabic post-training data ecosystem. Furthermore, we outline actionable, implementable strategies for data curation and governance optimization. The study thus delivers both a methodological foundation and practical guidelines to enhance Arabic LLM performance and foster sustainable community development.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Evaluation and Analysis

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Post-training has emerged as a crucial technique for aligning pre-trained Large Language Models (LLMs) with human instructions, significantly enhancing their performance across a wide range of tasks. Central to this process is the quality and diversity of post-training datasets. This paper presents a review of publicly available Arabic post-training datasets on the Hugging Face Hub, organized along four key dimensions: (1) LLM Capabilities (e.g., Question Answering, Translation, Reasoning, Summarization, Dialogue, Code Generation, and Function Calling); (2) Steerability (e.g., persona and system prompts); (3) Alignment (e.g., cultural, safety, ethics, and fairness), and (4) Robustness. Each dataset is rigorously evaluated based on popularity, practical adoption, recency and maintenance, documentation and annotation quality, licensing transparency, and scientific contribution. Our review revealed critical gaps in the development of Arabic post-training datasets, including limited task diversity, inconsistent or missing documentation and annotation, and low adoption across the community. Finally, the paper discusses the implications of these gaps on the progress of Arabic LLMs and applications while providing concrete recommendations for future efforts in post-training dataset development.

Problem

• Reviewing Arabic post-training datasets for LLM limitations
• Identifying gaps in task diversity and documentation quality
• Assessing impact on Arabic LLM progress and applications

Innovation

• Reviewing Arabic post-training datasets on Hugging Face
• Evaluating datasets across four key dimensions
• Identifying gaps in Arabic dataset development