用大模型预测阿拉伯语司法判决，小模型微调后效果不输大模型。
Can Large Language Models Predict the Outcome of Judicial Decisions?
- 构建阿拉伯语商事判决数据集，测试LLaMA系列模型在零样本、单样本和微调下的表现。
- 微调后的3B模型性能接近8B模型，且更省资源，适合低资源法律场景。
- 通过多样化指令微调提升模型可解释性，适合法律AI研究者与开发者参考。
大型语言模型在自然语言处理多个领域表现卓越，但在阿拉伯语等低资源语言的司法判决预测任务中应用仍有限。本文针对此问题，从沙特商事法院判决中构建并预处理了阿拉伯语司法判决数据集。我们对开源LLM（如LLaMA-3.2-3B和LLaMA-3.1-8B）在零样本、单样本及LoRA微调配置下进行基准测试。采用融合定量指标（如BLEU、ROUGE、BERT）与定性评估（连贯性、法律语言规范性、清晰度等）的综合框架，由大模型辅助评估。结果表明，在特定任务中，微调的小模型性能可媲美大模型，同时显著降低资源消耗。此外，探索了多样化指令微调对模型适应性的影响，为构建更贴近人类需求的可适配模型提供洞见。数据集、代码与模型已公开，为阿拉伯语法律NLP研究奠定基础。
原文摘要 · Abstract (English)
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 LoRA. Additionally, we employed a comprehensive evaluation framework that integrates both quantitative metrics (such as BLEU, ROUGE, and BERT) and qualitative assessments (including Coherence, Legal Language, Clarity, etc.) using an LLM. 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 impact of fine-tuning the model on a diverse set of instructions, offering valuable insights into the development of a more human-centric and adaptable LLM. We have made the dataset, code, and models publicly available to provide a solid foundation for future research in Arabic legal NLP.
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