The UAE is reportedly leveraging Artificial Intelligence to address and bridge existing language gaps within its legal system. This initiative aims to enhance the clarity, accuracy, and efficiency of legal processes, particularly in a multi-lingual environment. Utilizing AI tools is expected to streamline the interpretation and understanding of legal documents and proceedings across diverse linguistic contexts. Why it matters: This development underscores the UAE's strategic commitment to integrating advanced technologies for improving governmental services and operational effectiveness.
This paper introduces a predictive analysis of Arabic court decisions, utilizing 10,813 real commercial court cases. The study evaluates LLaMA-7b, JAIS-13b, and GPT3.5-turbo models under zero-shot, one-shot, and fine-tuned training paradigms, also experimenting with summarization and translation. GPT-3.5 models significantly outperformed others, exceeding JAIS model performance by 50%, while also demonstrating the unreliability of most automated metrics. Why it matters: This research bridges computational linguistics and Arabic legal analytics, offering insights for enhancing judicial processes and legal strategies in the Arabic-speaking world.
UAE businesses are reportedly increasingly adopting digital legal tools to address various challenges stemming from recent crises. This strategic shift aims to enhance operational efficiency, reduce costs, and ensure compliance within a rapidly evolving business environment. The digital solutions are being leveraged across different sectors within the UAE's economy. Why it matters: This trend signifies a broader digital transformation within the UAE's legal and corporate sectors, potentially driving innovation and operational resilience.
Researchers introduce ALARB, a new benchmark for evaluating reasoning in Arabic LLMs using 13K Saudi commercial court cases. The benchmark includes tasks like verdict prediction, reasoning chain completion, and identification of relevant regulations. Instruction-tuning a 12B parameter model on ALARB achieves performance comparable to GPT-4o in verdict prediction and generation.
The paper introduces a web-based expert system called RCSES for civil service regulations in Saudi Arabia. The system covers 17 regulations and utilizes XML for knowledge representation and ASP.net for rule-based inference. RCSES was validated by domain experts and technical users, and compared favorably to other web-based expert systems.