The Efficacy of Large Language Models (LLM) in Predicting the Outcomes of Commercial Legal Cases based on The Saudi Law : A Preliminary Study

Abstract

Recently, there has been significant and rapid evolution both in Artificial Intelligence (AI) and data availability. Particularly in Machine Learning (ML) and Deep Learning (DL) where patterns are automatically detected in large volumes of data. The legal field is one of many sectors that can benefit from such evolution. The services such an evolution can provide include document analysis, automated contract drafting, advising support, summarization, case outcome prediction. This paper examines the efficiency of the AI-based models, in particular, Large Language Models (LLMs) within the context of commercial Saudi law to predict case’s outcome. The study analyzes the potential of LLMs to assist legal professionals in interpreting case context and predicting case outcomes through navigating the complexities of Saudi commercial regulations. It also explores how three different LLMs can analyze factual scenarios, apply relevant legal principles, and generate reasoned legal opinions based on analyzing 500 legal cases in the Saudi commercial court of law. The performance assessment of the included models is based on human evaluation, BLEU score, Rouge score, and Bert. Although this study is preliminary, it highlights both the opportunities and challenges presented by incorporating LLMs into Arabic legal practice in Saudi Arabia, considering the unique characteristics of the Arabic language and the Saudi legal system, and the evolving capabilities of these advanced language models. Finally, the paper discuss the implications of using LLM in the Arabic legal field to enhance the efficiency and accuracy.