Saudi Arabia has launched its National Strategy for Data and AI (NSDAI), outlining the Kingdom's ambition to become a global leader in the field. The strategy focuses on creating a thriving data and AI ecosystem, developing local talent, and attracting foreign investment. It aims to drive economic diversification and improve the quality of life for citizens. Why it matters: The NSDAI signals a strong commitment from Saudi Arabia to become a major player in the global AI landscape, potentially influencing regional development and investment.
This paper presents a UI-level evaluation of ALLaM-34B, an Arabic-centric LLM developed by SDAIA and deployed in the HUMAIN Chat service. The evaluation used a prompt pack spanning various Arabic dialects, code-switching, reasoning, and safety, with outputs scored by frontier LLM judges. Results indicate strong performance in generation, code-switching, MSA handling, reasoning, and improved dialect fidelity, positioning ALLaM-34B as a robust Arabic LLM suitable for real-world use.
The QU-NLP team presented their approach to the QIAS 2025 shared task on Islamic Inheritance Reasoning, fine-tuning the Fanar-1-9B model using LoRA and integrating it into a RAG pipeline. Their system achieved an accuracy of 0.858 on the final test, outperforming models like GPT 4.5, LLaMA, and Mistral in zero-shot settings. The system particularly excelled in advanced reasoning, achieving 97.6% accuracy. Why it matters: This demonstrates the effectiveness of domain-specific fine-tuning and retrieval augmentation for Arabic LLMs in complex reasoning tasks, even surpassing frontier models.
The UAE is making major investments in AI to diversify its economy and exert global influence. These investments include partnerships with global tech companies, development of AI infrastructure, and the establishment of AI-focused institutions. The UAE aims to become a leader in AI research and development, attracting talent and fostering innovation. Why it matters: This strategic focus on AI could reshape the UAE's economic and geopolitical role in the region and beyond.
This paper introduces Saudi-Dialect-ALLaM, a LoRA fine-tuned version of the Saudi Arabian foundation model ALLaM-7B-Instruct-preview, designed to improve the generation of Saudi dialects (Najdi and Hijazi). The model is trained on a private dataset of 5,466 synthetic instruction-response pairs, with two variants explored: Dialect-Token and No-Token training. Results indicate that the Dialect-Token model achieves superior dialect control and fidelity compared to generic instruction models, although the dataset and model weights are not released.