Can Large Language Models Predict the Outcome of Judicial Decisions?

• URL: http://arxiv.org/abs/2501.09768v2
• Date: Wed, 05 Feb 2025 12:17:36 GMT
• Title: Can Large Language Models Predict the Outcome of Judicial Decisions?
• Authors: Mohamed Bayan Kmainasi, Ali Ezzat Shahroor, Amani Al-Ghraibah,
• Abstract summary: Large Language Models (LLMs) have shown exceptional capabilities in Natural Language Processing (NLP) We benchmark state-of-the-art open-source LLMs, including LLaMA-3.2-3B and LLaMA-3.1-8B, under varying configurations. Our results demonstrate that fine-tuned smaller models achieve comparable performance to larger models in task-specific contexts.
• Score: 0.0
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• Abstract: Large Language Models (LLMs) have shown exceptional capabilities in Natural Language Processing (NLP) across diverse domains. However, their application in specialized tasks such as Legal Judgment Prediction (LJP) for low-resource languages like Arabic remains underexplored. In this work, we address this gap by developing an Arabic LJP dataset, collected and preprocessed from Saudi commercial court judgments. We benchmark state-of-the-art open-source LLMs, including LLaMA-3.2-3B and LLaMA-3.1-8B, under varying configurations such as zero-shot, one-shot, and fine-tuning using QLoRA. Additionally, we used a comprehensive evaluation framework combining quantitative metrics (BLEU and ROUGE) and qualitative assessments (Coherence, legal language, clarity). Our results demonstrate that fine-tuned smaller models achieve comparable performance to larger models in task-specific contexts while offering significant resource efficiency. Furthermore, we investigate the effects of prompt engineering and fine-tuning on model outputs, providing insights into performance variability and instruction sensitivity. By making the dataset, implementation code, and models publicly available, we establish a robust foundation for future research in Arabic legal NLP.

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