大型语言模型能否预测司法判决结果？
Can Large Language Models Predict the Outcome of Judicial Decisions?
AI总结：
本研究构建阿拉伯语法律判决预测数据集，评估开源LLMs在零样本、单样本及LoRA微调下的性能，发现微调小模型在特定任务中媲美大模型且更高效，并公开资源以推动该领域发展。
摘要：
大型语言模型（LLMs）在跨领域的自然语言处理（NLP）任务中展现了卓越的能力。然而，它们在诸如阿拉伯语等低资源语言的专门任务（如法律判决预测（LJP））中的应用仍未得到充分探索。在本工作中，我们通过构建一个阿拉伯语LJP数据集来填补这一空白，该数据集从沙特商业法院判决中收集并预处理而成。我们在多种配置下对最先进的开源LLMs（包括LLaMA-3.2-3B和LLaMA-3.1-8B）进行了基准测试，配置包括零样本、单样本以及使用LoRA进行微调。此外，我们采用了一个综合评估框架，该框架整合了定量指标（如BLEU、ROUGE和BERT）和定性评估（包括连贯性、法律语言、清晰度等），并使用LLM进行评估。我们的结果表明，在特定任务情境下，微调后的较小模型能达到与较大模型相当的性能，同时显著提高资源效率。此外，我们研究了在多样化指令集上微调模型的影响，为开发更以人为中心且适应性更强的LLM提供了宝贵见解。我们已公开提供数据集、代码和模型，为阿拉伯语法律NLP的未来研究奠定坚实基础。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
